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+---------------------- + + + +Deutsch Jozsa +---------------------- diff --git a/_sources/api.rst.txt b/_sources/api.rst.txt new file mode 100644 index 00000000..9a31913d --- /dev/null +++ b/_sources/api.rst.txt @@ -0,0 +1,15 @@ +API +=== + +.. autosummary:: + :toctree: generated + :recursive: + + qlasskit.qlassfun.qlassf + qlasskit.qlassfun.qlassfa + qlasskit.qlassfun.QlassF + qlasskit.algorithms.qalgorithm + qlasskit.algorithms.grover.Grover + qlasskit.qcircuit.qcircuit.QCircuit + qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper + qlasskit.qcircuit.gates \ No newline at end of file diff --git a/_sources/example_big_circuit.ipynb.txt b/_sources/example_big_circuit.ipynb.txt new file mode 100644 index 00000000..ee235e22 --- /dev/null +++ b/_sources/example_big_circuit.ipynb.txt @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Working with big circuits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qlasskit is capable of producing large circuit without any issue. The only thing that you have to do, is to use the `fastOptimizer`, since running CSE is too slow on large expressions lists.\n", + "\n", + "In the next example we are going to create a quantum circuit with 64 `Qint8` in input, and one `Qint8` in output, resulting on a circuit of ~21984 qubits and ~1044 gates in around 5 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import Qint8, Qlist, boolopt, qlassfa\n", + "\n", + "@qlassfa(bool_optimizer=boolopt.fastOptimizer)\n", + "def test(a_list: Qlist[Qint8, 64]) -> Qint8:\n", + " h_val = Qint8(0)\n", + " for c in a_list:\n", + " h_val = h_val + c\n", + " return h_val" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "QCircuit(21984 gates, 1044 qubits)\n" + ] + } + ], + "source": [ + "print(test.circuit())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_deutsch_jozsa.ipynb.txt b/_sources/example_deutsch_jozsa.ipynb.txt new file mode 100644 index 00000000..cbb83460 --- /dev/null +++ b/_sources/example_deutsch_jozsa.ipynb.txt @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deutsch Jozsa algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint4\n", + "\n", + "\n", + "@qlassf\n", + "def f(a: Qint4) -> bool:\n", + " return a > 7" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+nw+A6enpoOstLS14vV6SkpLIysqat3758mX8fj/9/f18+9vfxuVycf/994c0i8fjYWhoKKR9Q/Hhh6Hd5P3wgw8iOudKsG3ALpeLsbExurq6qKysnLM2MjLCwYMHASguLsbhcMzb/4477ph9hs7JyaGtrY1NmzaFPEskzVzwhrbftJctW6JzKyLUj5Ftb6M9+uijPP3002RkZPDrX/+avLw8AI4fP87evXt55513uHTpEvv37w/6LtypU6cYHx/n7NmzPPXUU7z33nt0dHRw3XXXRfq3smw9g/Ds/13+fv/pTvNupdn2LoTb7SY1NZWBgQEKCwspKioiNzeXiooKsrOzqa6uBha+/t2+fTs333wzu3fv5je/+Q2Tk5M8+eSTkfwthCz/U7AxYXn7pCRA/ubwzBNOtg04PT2d9vZ2du7cidPppK+vj5SUFI4cOUJrayunT58GrvwCDmDDhg3k5OTw9ttvh3vsFRETA/dXQMz8K6Pg2zvgvorAfqax7SXEYqampkhOTsbhcDA5OUl8fPyi27/33nts27aNhx56iJ/85CcRmvLqdb8L/60DZhZ5gyI2Bh78DJSu/iujoGz7Im4xPT09WJZFXl7evHgffPBBcnJyKC0tZcOGDbz11lv88Ic/JDY2lq9//etRmjg0pdeB61p4/RQcPwsfzMxdvy0Xbtse2MZUazLgEydOAMEvH2655RZ+9rOf8eMf/5iLFy+SkZFBVVUV3/nOdxa8p7yaua6FL1TAPTfC4Cj8y2uBL6VMdgYeN50C/it1dXXU1dVFeqSwc66HnL+F9esCvw5y59BIBl62X73FAhazrMln4I++TkLMtyafgcU+FLAYTQGL0RSwGE0Bi9EUsBhNAYvRFLAYTQGL0RSwGE0Bi9EUsBhNAYvRFLAYTQGL0RSwGE0Bi9EUsBhNAYvRFLAYTQGL0RSwGE0Bi9EUsBhNAYvRFLAYTQGL0RSwGE0Bi9EUsBhNAYvRFLAYTQGL0RSwGE0Bi9EUsBhNAYvR1uT/UrRWWBa8PwUDo4EfE9OB/+QQYPoS/LEfMlIgNdHc/zduTf5fyXbn+wB+fwZ++xZ4p668fVoi3JoLN2+DhGvCP99KUsA2MnMZXvl3eLUXLl1e/v7r18Gd18N/KILYdSs/XzgoYJsYGIX/8VsYOX/1x9p8LTxQCRmpV3+scFPANnBiAP7r6zDjX7ljxsbAw7dBUcbKHTMcdBfCcP8+CM+1r2y8EDjec+3QM7iyx11pCthgI+OBZ15/mD6H+i14/nXwrMBlSbjoEsJQl/3wo/8TuPZdjm/cDclxgVtq//S/l7bPdalQ/3ewbhU+3a3CkWQpXu1dfrwQiHdDfODnpXr3/cD5ViPbB+z1enG73eTk5OB0OsnIyKC+vh6fz8e+fftwOBw0NTVFe8xl+XAG2k5G9pxtJwPnXW1s/U5cd3c3NTU1eDweEhISKCgoYHh4mMbGRs6cOcPoaOAprLS0NLqDLtMf+z9+Ry1SLnwI3e9CRXZkz3sltn0G9nq93HvvvXg8Hg4cOMDIyAhdXV14PB4aGhpobW3l+PHjOBwOiouLoz3usvz2reict+N0dM67GNsG/OijjzI4OEhdXR2HDx8mKSlpds3tdlNSUsLMzAxbt24lOTk5ipMuzweXAtek0fDuKHywyi4jbBlwb28vLS0tpKWlcejQoaDblJWVAVBSUrLgcWpqanA4HDzxxBPhGDMkQ2MQrdtGlgVDIbxwDCdbBtzc3Izf76e2tpbExMSg28TFBV6GLxTwz3/+c7q7u8M1YsgGx6J7/lDufISTLV/EtbW1AVBVVbXgNoODgbeYggU8MTHB1772NQ4fPsyDDz541fOUl5fj8Xiu+jgABXf9AwU7vhZ07aN7vItJdn788xOfW3i7he4T/2PDj6j918NLG3YZXC4XnZ2dy97PlgH39/cDkJmZGXR9ZmaGjo4OIHjAjz32GHl5edTW1q5IwB6Ph6Ghoas+DkDmhekF1z66x7sUMTFL3/aTfBemV+z3shJsGbDP5wNgejr4H3ZLSwter5ekpCSysrLmrHV2dvLss8/yhz/8YcXmcblcK3ashLiFv2B3YuG2ZyU7A/H6/TBxceHtFjpWQtw1bNmy5conWqZQP0a2DNjlcjE2NkZXVxeVlZVz1kZGRjh48CAAxcXFOD7xrQiXL1/mkUceoa6ujsLCwhWbJ5RPjQt54wz8zzeCry3lreEnPhd45p24CE/8r+Wf/z8//g1ufv4by98xTGz5Im7Hjh0ANDQ0cPr0xzcvjx8/TlVVFV6vF5j/BkZTUxN/+ctfVtVdh7+WkbK2z//XbBmw2+0mNTWVgYEBCgsLKSoqIjc3l4qKCrKzs6murgbmXv96vV4ef/xxvvvd7zIzM8P4+Djj4+MAXLx4kfHxcfz+Ff6axRC4rg18rW40rF8Hf3ttdM69EFsGnJ6eTnt7Ozt37sTpdNLX10dKSgpHjhyhtbV19ln5kwEPDg4yOTnJI488wsaNG2d/QOCZfOPGjbz77rtR+f180roYyP9UdM59/ebV9xVptrwGBsjPz+fYsWPzHp+amqKvr4+YmBhuuOGG2cdzcnJ49dVX521fVVXFww8/zJe+9KUVfTF2NW7LgxNR+ELz2/Iif84rsW3AC+np6cGyLPLy8oiP//g+UmJiInfeeWfQfbZu3brgWjTkumBTEpybjNw5NyUFzrvarLJPCOF34sQJYPG3kFe7GAf8x09H9pyf/XTgvKvNmnsGXm7Aq/UbVm5Ih/Is6Dwb/nPdlAWF6eE/Tyj0DGywz5VBSsLy9pmYhvELS3vTAwLH/1zZ8meLFH1PnOHOTcLT/7r0IJcjOQ4evQvSkq68bbQoYBvwTsJ/aVvaPyO1VGmJ8PfVqzteUMC2cfESvPRH6FiB79b4TC7ceyM411/9scJNAdvMaQ/86k/Q513+vplpsLME8lbh7bKFKGCbGhgNfA/bKQ+M+RbebmMCbHfBZ/JW39c5LIUCXgOmLgaCnrwY+BcsY9dBkjMQbKIz2tNdHQUsRltz94HFXhSwGE0Bi9EUsBhNAYvRFLAYTQGL0RSwGE0Bi9EUsBhNAYvRFLAYTQGL0RSwGE0Bi9EUsBhNAYvRFLAYTQGL0RSwGE0Bi9EUsBhNAYvRFLAYTQGL0RSwGE0Bi9EUsBhNAYvRFLAYTQGL0f4/pHgpJEFQIasAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import DeutschJozsa\n", + "\n", + "q_algo = DeutschJozsa(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = q_algo.export(\"qiskit\")\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import Aer, QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "\n", + "qc.measure_all()\n", + "simulator = Aer.get_backend(\"aer_simulator\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover.ipynb.txt b/_sources/example_grover.ipynb.txt new file mode 100644 index 00000000..aa416ec2 --- /dev/null +++ b/_sources/example_grover.ipynb.txt @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover search\n", + "\n", + "Qlasskit offer a class to easily perform a `Grover` search over a qlasskit oracle. \n", + "First, we define a function named `and_all` that returns `True` iff all the element of an input list `a_list` are `True`. We want to use a Grover search to find the input value that led to a `True` result of the function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qlist, Qint2\n", + "\n", + "\n", + "@qlassf\n", + "def and_all(a_list: Qlist[bool, 4]) -> bool:\n", + " r = True\n", + " for i in a_list:\n", + " r = r and i\n", + " return r" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The qlasskit compiler will produce an optimized quantum circuit performing the given function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "and_all.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now can use our quantum function as an oracle for a Grover search. For instance, we want to find the input value that yeld to a `True` value of the function:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Grover\n", + "\n", + "q_algo = Grover(and_all, True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qlasskit prepares the quantum circuit for the Grover search:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = q_algo.export(\"qiskit\")\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`.\n", + "\n", + "The `Grover` class, along with all circuit wrappers in qlasskit, provides utilities to encode inputs and decode outputs from a quantum circuit using the high level type definitions. In the output histogram, it's now evident that the input leading to a `True` result in the `and_all` function is a list where all elements are set to `True`, aligning with our expectations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import Aer, QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "\n", + "qc.measure_all()\n", + "simulator = Aer.get_backend(\"aer_simulator\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover_factors.ipynb.txt b/_sources/example_grover_factors.ipynb.txt new file mode 100644 index 00000000..5809def1 --- /dev/null +++ b/_sources/example_grover_factors.ipynb.txt @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover: factorize number" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Tuple\n", + "from qlasskit import qlassf, Qint2\n", + "from qlasskit.algorithms import Grover\n", + "\n", + "\n", + "@qlassf\n", + "def factorize(a: Tuple[Qint2, Qint2]) -> bool:\n", + " return a[0] * a[1] == 9\n", + "\n", + "\n", + "q_algo = Grover(factorize)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Tg+DgYJP1hYSEIDo6GhcvXoSvr2+J+pueng4PDw+kpaWhWrVqpRwtERERVTTDlln2/M/GWKMXRStp9qhw5+RFRkbi77//Rp8+fWAwGLB582bMnz8fK1euRGJiosmyCQkJSE5ORlBQkEnAAwBXV1cEBQXh/PnzJufwRUdHq4/dLywsDAAQExNTBiMjIiIiKj8V7uvaI0eOAADs7Ozw+OOP4+zZs+pjOp0OY8eOxaJFiwDcC3kAijyHzt/fHxEREUhISICPjw8yMzNx5coVPPbYYwW+As6/HuN6C5OdnY3s7Gz1/9PT0wEAubm5yM3NVftpZ2cHg8GAvLw8k/7b2dlBr9cj/wFUOzs76HS6ItuN6zUyHt3M/7X1g9odHByQl5cHg8GgtimKAnt7+yLbi+o7x8QxcUwcE8fEMWl/TA6wRHmMqSQqXMi7du0aAGDJkiVo3bo1Dh48iEceeQTHjh3D8OHDsXjxYjRp0gRvvPEG0tLSAAAeHh6Frst4CNO4XGmXL8y8efMwc+bMAu2RkZF46KGHAAC+vr5o1aoVTpw4gUuXLqnLNGvWDAEBATh48CCuX7+utgcGBsLPzw979+7F7du31fYOHTrAy8sLkZGRJhMaEhICFxcXbNu2zaQPPXv2RFZWFqKiotQ2e3t7hIeH48aNGzhw4IDa7u7ujq5duyIpKQnx8fFqe+3atdGxY0ckJCTgzJkzajvHxDFxTBwTx8QxVZUxAeGwRFmPyXhArDgV7py84cOH47PPPoOLiwsSExPh7e2tPnby5Em0bNkSjRo1QmJiIjZs2IABAwZg6tSpmD17doF1TZ06FXPnzsXmzZvRt29fJCcno379+ggKCsIvv/xSYPmdO3ciNDQUo0ePxvLlywvtX2FH8nx8fHDjxg01JPKTEsfEMXFMHBPHxDFV3jG9+ZFlR/I+HV22Y0pNTYWnp2ex5+RVuCN5xqNsbdq0MQl4APDYY4+hcePGSExMxK1bt9RlizryZvwq1bhcaZcvjJOTE5ycnAq0Ozg4wMHBdKOws7Mr9Gvhoi4mKar9/vWa067T6aDTFTwFs6j2ovrOMXFMpW3nmDgmgGMqqo+lbeeYbDem0rDVmArUK9FS5ahZs2YAgOrVqxf6uLE9Kyur2HPo7j9nz9XVFfXq1cOFCxdMknRRyxMRERFVVhUu5IWEhAAATp06VeCx3NxcJCYmwtXVFbVr14a/vz+8vb0RGxuLzMxMk2UzMzMRGxuLRo0aqTdOBoDOnTurj93PeH+8+2+tQkRERFTZVLiQ16RJE4SGhiIxMRGrV682eWz+/Pm4desW+vbtC3t7eyiKgqFDhyIjIwOzZs0yWXbWrFnIyMjAsGHDTNqHDx8OAJg2bRpycnLU9u3btyM6OhqhoaHw8/Mro9ERERERlY8Kd+EFAJw7dw4dO3bEtWvXEB4ejoCAABw7dgx79uyBn58f4uLiULduXQD3jtgFBQXh+PHjCA0NRevWrXH06FFERkaibdu2iImJgYuLi8n6hw0bpv6sWXh4OK5cuYJvv/0Wbm5uOHDgAJo2bVrivvJmyERERNrCmyGXoSZNmuDw4cMYNGgQjhw5gg8//BAJCQkYOXIkDh48qAY84N55djExMRgzZgxOnTqFxYsX4/Tp0xg/fjx2795dIOABwKeffqpePbt8+XJs27YNffv2xcGDB0sV8IiIiIgqqgp5JK8y4ZE8IiIibeGRPCIiIiKqsBjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIgxjyiIiIiDSIIY+IiIhIg8wOeXv37sWlS5ceuExSUhL27t1rbgkiIiIiMpPZIS8kJARffvnlA5dZt24dQkJCzC1BRERERGYyO+SJSLHL5OXlQVEUc0sQERERkZnK9Jy8hIQEeHh4lGUJIiIiIiqEfWkWHjJkiMn///DDD/jzzz8LLGcwGNTz8Xr06GFRB4mIiIio9EoV8vKfg6coCuLj4xEfH1/osoqioG3btli6dKkl/SMiIiIiM5Qq5F24cAHAvfPxGjdujDFjxuD//u//CixnZ2eHGjVqwNXV1Tq9JCIiIqJSKVXI8/PzU/97zZo1aNWqlUkbEREREVUMpQp5+Q0cONCa/SAiIiIiKzI75BkdPHgQhw4dwq1bt2AwGAo8rigKpk2bZmkZIiIiIioFs0Neamoq+vTpg9jY2AfeM48hj4iIiKj8mR3yxo0bh19++QVdunTBwIED0aBBA9jbW3xgkIiIiIiswOxU9t///hft2rXD7t27+asWRERERBWM2b94kZWVheDgYAY8IiIiogrI7JAXGBhY6K9dEBEREZHtmR3ypk+fjp9++glxcXHW7A8RERERWYHZ5+SlpKQgPDwcnTt3xoABA9C6dWtUq1at0GVfe+01sztIRERERKWnyIPuf/IAOp0OiqKY3D7l/vPzRASKohR6/zytSE9Ph4eHB9LS0ooMuURERFR5DFtm2fM/G2ONXhStpNnD7CN5a9asMfepRERERFTG+LNmRERERBpk9oUXRERERFRxmX0k79KlSyVe1tfX19wyRERERGQGs0New4YNS3QjZEVRoNfrzS1DRERERGYwO+S99tprhYa8tLQ0HD9+HBcuXEDnzp3RsGFDS/oHAFiwYAEmTZoEADhw4ACefPJJk8fT09MxY8YMbNq0CSkpKahXrx5eeOEFTJ8+HW5ubgXWl5eXh48++girVq1CYmIi3Nzc8Mwzz2DOnDlo3Lixxf0lIiIisjWzb6HyICKCxYsXY+HChYiLi7MoOJ08eRJt2rSBvb09MjMzC4S8zMxMdOrUCfHx8QgNDUWrVq1w7NgxREZGom3btti7dy+cnZ1N1jls2DCsXr0azZs3R3h4OJKTk/Gf//wHbm5uiIuLg7+/f4n7x1uoEBERaYtWbqFSJhdeKIqCt99+G82bN8c777xj9npyc3MxcOBABAYGom/fvoUus3DhQsTHx2PixImIiIjA/PnzERERgYkTJ+LQoUNYunSpyfJRUVFYvXo1goODcfToUSxYsABfffUVfvjhB6SmpmLUqFFm95eIiIiooijTq2vbtGmDPXv2mP38OXPm4Pfff8cXX3wBOzu7Ao+LCFavXg03NzdMmzbN5LFp06bBzc0Nq1evNmn/7LPPAACzZs2Co6Oj2t6jRw906dIFkZGRpbqohIiIiKgiKtOQd+7cObMvujh69CjmzJmD6dOn49FHHy10mYSEBCQnJyMoKAiurq4mj7m6uiIoKAjnz59HUlKS2h4dHa0+dr+wsDAAQExMjFl9JiIiIqoozL7woih5eXn466+/8OWXX+LHH3/E008/Xep1ZGdn47XXXkNgYCAmTJhQ5HIJCQkAUOQ5dP7+/oiIiEBCQgJ8fHyQmZmJK1eu4LHHHiv0yKBxPcb1FtW37Oxs9f/T09MB3PtqOTc3F8C9n3yzs7ODwWBAXl6euqyxXa/Xm/wcnJ2dHXQ6XZHtxvUa2dvfm7b7A3RR7Q4ODsjLyzP5eTlFUWBvb19ke1F955g4Jo6JY+KYOCbtj8kBliiPMZWE2SHP+Nu1RRER1KhRA4sXLy71ut977z0kJCTgyJEjhYYxo7S0NACAh4dHoY8bT0Y0Llfa5Qszb948zJw5s0B7ZGQkHnroIQD37gvYqlUrnDhxwuSr32bNmiEgIAAHDx7E9evX1fbAwED4+flh7969uH37ttreoUMHeHl5ITIy0mRCQ0JC4OLigm3btpn0oWfPnsjKykJUVJTaZm9vj/DwcNy4cQMHDhxQ293d3dG1a1ckJSUhPj5eba9duzY6duyIhIQEnDlzRm3nmDgmjolj4pg4pqoyJiAclijrMR05cqRE/TD76touXboUGvJ0Oh1q1KiBtm3bYvDgwfDy8irVeg8cOIBOnTphxowZJufZDRo0CGvXrjW5unbDhg0YMGAApk6ditmzZxdY19SpUzF37lxs3rwZffv2RXJyMurXr4+goCD88ssvBZbfuXMnQkNDMXr0aCxfvrzQ/hV2JM/Hxwc3btxQQyI/KXFMHBPHxDFxTBxT5R3Tmx9ZdiTv09FlO6bU1FR4enoWe3Wt2UfyoqOjzX1qkfR6PQYOHIjHH39cvS/egxiPyBV15M34VapxudIuXxgnJyc4OTkVaHdwcICDg+lGYWdnV+iRSOPGXtL2+9drTrtOp4NOV/AUzKLai+o7x8QxlbadY+KYAI6pqD6Wtp1jst2YSsNWYyqwXImWKicZGRnq+XD5r3zNr0OHDgCALVu2qBdkFHUO3f3n7Lm6uqJevXq4cOECDAZDgT9ccef4EREREVUWVgl5sbGxiI+PR3p6OqpVq4bAwMBCr14tjpOTE15//fVCH9u7dy8SEhLQq1cv1K5dGw0bNoS/vz+8vb0RGxuLzMxMkytsMzMzERsbi0aNGsHHx0dt79y5M7755hvExsYiODjYpEZERAQAFGgnIiIiqmwsCnn79+/H4MGDkZiYCODexRbG8/T8/f2xZs0a9chbSbi4uBS4r53RoEGDkJCQgMmTJ5v84sXQoUPx/vvvY9asWZg/f77aPmvWLGRkZGDKlCkm6xk+fDi++eYbTJs2DTt37lSPGG7fvh3R0dEIDQ2Fn59fiftMREREVBGZHfJ+//13hIaG4s6dO+jWrRtCQkJQr149pKSkICoqCpGRkQgLC0NcXFyR97mzhgkTJuDHH3/EggULcOzYMbRu3RpHjx5Vf9ZszJgxJsuHhIRg6NChWL16NVq3bo3w8HBcuXIF3377LWrWrIkVK1aUWV+JiIiIyovZIe/9999HTk4Otm3bhu7du5s8NnHiROzYsQO9evXC+++/j2+++cbijhbF1dUVMTExmDFjBjZt2oSoqCjUq1cP48ePx/Tp0+Hi4lLgOZ9++ilatGiBVatWYfny5XBzc0Pfvn0xZ84cNGnSpMz6SkRERFRezL6FSp06dfD0009jw4YNRS7Tv39/7N69G1evXjW7gxVdSX8kmIiIiCqHYcsse/5nY6zRi6KVNHuY/bNmaWlpaNSo0QOXadSo0QNvLExEREREZcPskOft7Y24uLgHLvPrr7/C29vb3BJEREREZCazQ16vXr0QHR2NadOm4e7duyaP3b17F9OnT0dUVBR69+5tcSeJiIiIqHTMPifv5s2baN++PS5cuABPT0+0a9cOderUwdWrV3Ho0CFcv34djRs3xsGDB1GzZk1r97vC4Dl5RERE2qKVc/LMvrrW09MTcXFxmDBhAr755huTHyN2dnbG4MGDsWDBAk0HPCIiIqKKyqKbIdeqVQtffPEFPv30U5w+fVr9xYuAgACr/PYbEREREZmn1CFvzpw5yMzMxMyZM9Ug5+DggBYtWqjL5OTkYOrUqXB3d8ekSZOs11siIiIiKpFSXXixa9cuvPfee/D09HzgkTpHR0d4enpi6tSpiIqKsriTRERERFQ6pQp569atQ40aNTBq1Khilx05ciRq1qyJNWvWmN05IiIiIjJPqULe/v378cwzz8DJyanYZZ2cnPDMM88gNjbW7M4RERERkXlKFfKSk5PRuHHjEi/fqFEjXLlypdSdIiIiIiLLlCrk6XQ65Obmlnj53Nxc6HRm32+ZiIiIiMxUqgTm7e2NkydPlnj5kydPon79+qXuFBERERFZplQh76mnnsKePXvw559/Frvsn3/+iT179iA4ONjcvhERERGRmUoV8kaOHInc3Fz885//xI0bN4pc7ubNm3jhhReg1+vxxhtvWNxJIiIiIiqdUt0MuXXr1hgzZgyWLVuGRx99FCNGjEBISAgaNGgAAPjrr7+we/durFq1CtevX8e4cePQunXrMuk4ERERERWt1L94sXjxYjg7O+ODDz7AnDlzMGfOHJPHRQR2dnaYPHkyZs+ebbWOEhEREVHJlTrkKYqCuXPn4vXXX8eaNWuwf/9+pKSkAADq1q2LoKAgDBo0CE2aNLF6Z4mIiIioZEod8oyaNGnCI3VEREREFRRvYkdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQRUu5P31119YtmwZQkND4evrC0dHR9StWxfPP/88fv3110Kfk56ejnHjxsHPzw9OTk5o2LAh3nnnHWRkZBS6fF5eHlasWIEWLVrAxcUFtWvXRr9+/XD+/PmyHBoRERFRualwIW/FihUYO3Yszp8/j9DQUIwfPx6dOnXCjz/+iI4dO+Lbb781WT4zMxOdO3fG0qVLERAQgLFjx6JZs2ZYtGgRunbtirt37xao8a9//QujR4+GiGD06NHo3r07Nm/ejLZt2yIhIaG8hkpERERUZuxt3YH7tWvXDtHR0ejcubNJ+759+/D000/jjTfeQJ8+feDk5AQAWLhwIeLj4zFx4kTMnz9fXX7SpElYsGABli5dismTJ6vtUVFRWL16NYKDg7Fz5044OjoCAPr374+ePXti1KhRiIiIKIeREhEREZUdRUTE1p0oqbCwMERGRuLQoUNo06YNRAQNGjRAeno6UlJS4Orqqi6bmZmJunXrwsvLC+fOnVPb+/fvj40bNyImJgbBwcEm6w8JCUF0dDQuXrwIX1/fEvUpPT0dHh4eSEtLQ7Vq1awzUCIiIrKZYcsse/5nY6zRi6KVNHtUuK9rH8TBwQEAYG9/7wBkQkICkpOTERQUZBLwAMDV1RVBQUE4f/48kpKS1Pbo6Gj1sfuFhYUBAGJiYspqCERERETlosJ9XVuUS5cuYdeuXahXrx5atGgBAOr5c/7+/oU+x9/fHxEREUhISICPjw8yMzNx5coVPPbYY7Czsyt0+fzrLUx2djays7PV/09PTwcA5ObmIjc3FwCg0+lgZ2cHg8GAvLw8dVlju16vR/4DqHZ2dtDpdEW2G9drZAy5er2+RO0ODg7Iy8uDwWBQ2xRFgb29fZHtRfWdY+KYOCaOiWPimLQ/JgdYojzGVBKVIuTl5ubi1VdfRXZ2NhYsWKAGtLS0NACAh4dHoc8zHsI0Llfa5Qszb948zJw5s0B7ZGQkHnroIQCAr68vWrVqhRMnTuDSpUvqMs2aNUNAQAAOHjyI69evq+2BgYHw8/PD3r17cfv2bbW9Q4cO8PLyQmRkpMmEhoSEwMXFBdu2bTPpQ8+ePZGVlYWoqCi1zd7eHuHh4bhx4wYOHDigtru7u6Nr165ISkpCfHy82l67dm107NgRCQkJOHPmjNrOMXFMHBPHxDFxTFVlTEA4LFHWYzpy5EiJ+lHhz8nLy8vDq6++ig0bNmDYsGFYtWqV+tiGDRswYMAATJ06FbNnzy7w3KlTp2Lu3LnYvHkz+vbti+TkZNSvXx9BQUH45ZdfCiy/c+dOhIaGYvTo0Vi+fHmh/SnsSJ6Pjw9u3LihhkR+UuKYOCaOiWPimDimyjumNz+y7Ejep6PLdkypqanw9PQs9py8Cn0kLy8vD0OGDMGGDRvwyiuvYOXKlSaPG4/IFXXkzfhVqnG50i5fGCcnJ/XK3vwcHBzUcwaN7OzsCv1a2Lixl7T9/vWa067T6aDTFTwFs6j2ovrOMXFMpW3nmDgmgGMqqo+lbeeYbDem0rDVmArUK9FSNpCXl4fBgwdj7dq16NevH7788ssCf5jizqG7/5w9V1dX1KtXDxcuXDBJ0kUtT0RERFRZVciQZwx469atw0svvYSvvvqqyAslvL29ERsbi8zMTJPHMjMzERsbi0aNGsHHx0dt79y5s/rY/Yz3x7v/1ipERERElU2FC3nGr2jXrVuHF154AevXry804AH3vsseOnQoMjIyMGvWLJPHZs2ahYyMDAwbNsykffjw4QCAadOmIScnR23fvn07oqOjERoaCj8/PyuPioiIiKh8VbgLL2bMmIGZM2fCzc0N//d//1fo9859+vRBYGAggHtH7IKCgnD8+HGEhoaidevWOHr0KCIjI9G2bVvExMTAxcXF5PnDhg3D6tWr0bx5c4SHh+PKlSv49ttv4ebmhgMHDqBp06Yl7i9vhkxERKQtWrkZcoW78OLPP/8EAGRkZGDOnDmFLtOwYUM15Lm6uiImJgYzZszApk2bEBUVhXr16mH8+PGYPn16gYAHAJ9++ilatGiBVatWYfny5XBzc0Pfvn0xZ84cNGnSpKyGRkRERFRuKtyRvMqGR/KIiIi0RStH8ircOXlEREREZDmGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIiIi0iCGPCIiIiINYsgjIqIKad68eWjbti3c3d3h5eWFPn364MyZMwWWO3DgALp27QpXV1dUq1YNwcHByMrKskGPiSoWhjwiqtBK+kYPACKCHj16QFEU/PDDD+VW/1//+heaNGkCFxcX1K5dG71798bp06etUr8qi4mJwciRIxEXF4edO3ciNzcXoaGhyMzMVJc5cOAAunfvjtDQUBw8eBCHDh3CqFGjoNPx7a0ys/V+rxXcCyqJkmzwq1atQpcuXVCtWjUoioJbt27ZprNEVlSSN3qjZcuWQVGUcq//xBNPYM2aNTh16hQiIiIgIggNDYXBYLBqX6qaHTt2YNCgQWjevDlatmyJL7/8EpcuXcKRI0fUZcaOHYvRo0dj0qRJaN68OZo1a4YXX3wRTk5OFtcv7nU3NTUVb731Fpo1awYXFxf4+vpi9OjRSEtLs7h2VWfr/V4rGPIqiZJs8Hfu3EH37t0xZcoUG/ZUe0oSsO/evYuRI0fC09MTbm5ueP7553H16lUb9VhbSvJGDwDx8fFYvHgxvvjii3KvP3z4cAQHB6Nhw4Zo3bo1Zs+ejaSkJPz5559W7UtVZwxPNWvWBABcu3YNv/76K7y8vNCxY0fUqVMHnTt3xi+//GKVesW97iYnJyM5ORmLFi3CyZMn8eWXX2LHjh14/fXXrVK/KrP1fq8VDHmVREk2+DFjxmDSpEl48sknbdhT7SlJwB47diy2bt2K7777DjExMUhOTsY//vEPq/Vh7969eO655+Dt7V3oVxIZGRkYNWoUGjRoABcXFzz66KNYuXJludVXFKXQfx988IHV+mB0/xs9cO8DTv/+/fHRRx+hbt26Vq9ZXP38MjMzsWbNGjRq1Ag+Pj5l2peqJC8vD2PGjEFQUBAee+wxAMD58+cBADNmzMCwYcOwY8cOtG7dGk8//TQSEhIsrlnc6+5jjz2GTZs24bnnnkOTJk3QtWtXzJkzB1u3boVer7e4fkXa72zN1vt9ZcWQV0kV90ajNcW92A0aNKjAC1337t2tUru4F/q0tDR8/vnnWLJkCbp27ap+dbd//37ExcVZpQ+ZmZlo2bIlPvroo0IfHzduHHbs2IH169fj1KlTGDNmDEaNGoWffvqpXOpfuXLF5N8XX3wBRVHw/PPPW6W+UWFv9MC9kN2xY0f07t3bqvVKWh8APv74Y7i5ucHNzQ3bt2/Hzp074ejoaHFNW277FcnIkSNx8uRJfPPNN2pbXl4egHvnRA4ePBitWrXC0qVL0axZszI5slOS1920tDRUq1YN9vb2Ftez9X5X3LYHAKdOnUKvXr3g4eEBV1dXtG3bFpcuXbJKfSNb7/eVGUNeJfSgN5qyUpKd3WjEiBFQFAXLli2zWv3iXuwAoHv37iYveBs3brRa/fzuf6E/cuQIcnNz8cwzz6jLBAQEwNfXFwcOHLBKzR49emD27Nno27dvoY/v378fAwcORJcuXdCwYUMMHz4cLVu2xMGDB8ulft26dU3+/fjjjwgJCUHjxo2tUt+osDf6n376CXv27LHq9laa+kYDBgzAsWPHEBMTg6ZNm+LFF1/E3bt3La5p623f1vs+AIwaNQr//e9/ERUVhQYNGqjt9erVAwA8+uijJss/8sgj5RY08rtx4wZmzZqF4cOHW6Wmrfe74ra9c+fOoVOnTggICEB0dDROnDiBadOmwdnZ2Sr1jWy931dmln/UoHJn3OCtdd5JSRh39iFDhjzwa8gtW7YgLi4O3t7eVq3fo0cP9OjR44HLODk5lfkh+8Je6FNSUuDo6Ijq1aubLFunTh2kpKSUaX+MOnbsiJ9++glDhgyBt7c3oqOjcfbsWSxdurRc6ud39epV/Pzzz1i7dq1V12t8o9+7d6/JG/2ePXtw7ty5An//559/Hk899RSio6PLtL6Rh4cHPDw84O/vjyeffBI1atTAli1b0K9fP4vq2nrbt+W+LyJ46623sGXLFkRHR6NRo0Ymjzds2BDe3t4FzpE9e/ZssX+z0irudTc9PR3h4eF49NFHMWPGDKvWLomy2O+K2/amTp2Knj17YuHChWpbkyZNrFYfsP1+X9kx5FUyxb3RlJWSvNH89ddfeOuttxAREYHw8PBy6tn/REdHw8vLCzVq1EDXrl0xe/ZseHp6WrWGLQJ2SaxYsQLDhw9HgwYNYG9vD51Oh88++wzBwcHl3pe1a9fC3d3dauckFvdGP2nSJAwdOtSkrUWLFli6dCmee+65Mq9f1HNEBNnZ2RbXL4my3PZtue+PHDkSGzZswI8//gh3d3f1Q5OHhwdcXFygKAreeecdTJ8+HS1btkRgYCDWrl2L06dP4/vvv7daP4p73b19+za6d+8Od3d3bNmyBQ4ODlarXVLW3u+Kk5eXh59//hkTJkxAWFgYjh07hkaNGmHy5Mno06ePxeu39X6vFQx5lYQ5bzTlKS8vD6+++ireeecdNG/evNzrd+/eHf/4xz/QqFEjnDt3DlOmTEGPHj1w4MAB2NnZWaVGUS/0devWRU5ODm7dumXyqfLq1avldjLwihUrEBcXh59++gl+fn7Yu3cvRo4cCW9vb5OvkcvDF198gQEDBljtK5vi3uiNX1Xdz9fX1yr7SXH1z58/j2+//RahoaGoXbs2Ll++jPnz58PFxQU9e/a0uH5xymPbf5Cy3Pc/+eQTAECXLl1M2tesWYNBgwYBuHfB2d27dzF27FikpqaiZcuW2Llzp1WOKJXkdTc9PR1hYWFwcnLCTz/9ZPWvKkvK2vtdca5du4aMjAzMnz8fs2fPxoIFC7Bjxw784x//QFRUFDp37mzR+m2932sFQ14lUdwGD9z72jAlJQWJiYkAgN9++w3u7u7w9fUt8ws0FixYAHt7e4wePbpM6xTl5ZdfVv+7RYsWePzxx9GkSRNER0fj6aeftmjdxb3QP/HEE3BwcMDu3bvVE57PnDmDS5cuoUOHDhbVLomsrCxMmTIFW7ZsUY+iPP7444iPj8eiRYvKNeTt27cPZ86cwbfffmu1dZbkjb4sFVff2dkZ+/btw7Jly/D333+jTp06CA4Oxv79++Hl5VXm/SvLbb8kynLfF5ESLTdp0iRMmjTJ6vWLe91NT09HaGgo7ty5g/Xr1yM9PR3p6ekAgNq1a5dLyAbKZr8rjvGil969e2Ps2LEAgMDAQOzfvx8rV660OOTZer/XCoa8SqIkG/zKlSsxc+ZM9THjV3VlvVMcOXIEy5cvx9GjRyvMDSkbN26MWrVqITEx0eI3uuJe6D08PPD6669j3LhxqFmzJqpVq4a33noLHTp0KJfb2eTm5iI3N7fAHf7t7OzUF+Ly8vnnn+OJJ55Ay5YtrbbOkr7RW/occ9fl7e2Nbdu2Wa2epay57RenIu771lTc6+7Ro0fx66+/AgAefvhhk2UuXLiAhg0blkc3y2S/K06tWrVgb29f6EUv1jidxdb7vVYw5FUSJdl4Z8yYYZMTfvft24dr167B19dXbTMYDBg/fjyWLVtmkxvCXr58GTdv3lSvvrNESQL20qVLodPp8PzzzyM7OxthYWH4+OOPLa5tlJGRoR6hBe69gcTHx6NmzZrw9fVF586d8c4778DFxQV+fn6IiYnBunXrsGTJknKpD9z72uq7777D4sWLrVKTzGPNbb84FXHft6biXne7dOlSpsGiIu93jo6OaNu2baEXvfj5+ZVrX6hoDHlksVdffbXAV4JhYWF49dVXMXjwYKvUeNCLXc2aNTFz5kw8//zzqFu3Ls6dO4cJEybg4YcfRlhYmMW1S/Ii7uzsjI8++uiBt7mwxOHDhxESEqL+/7hx4wAAAwcOxJdffolvvvkGkydPxoABA5Camgo/Pz/MmTMHI0aMKJf6APDNN99ARCy+mpRM2XLbL05Z7/vDlln2/M/GWNwFm7L1fldcyHznnXfw0ksvITg4GCEhIdixYwe2bt3KK1srEEV4fNMi6enp8PDwUG+AqVX5d/ZWrVphyZIlCAkJMflEmV/Dhg0xZswYjBkzxir1o6OjTV7sjAYOHIhPPvkEffr0wbFjx3Dr1i14e3sjNDQUs2bNQp06daxSn2ynqr/R23rbt+W+X9Xn3tYetO0ZQ+YXX3yBefPm4fLly2jWrBlmzpxplZsT23rubV2/OCXNHjySRyVSkk+UZam4r0UiIiLKvA9UNVnyYm+NF3pbb/u23vdtqaK/0Ze1knwdPWTIEAwZMqScekSlxZBXSdj6xaa0555U9nNx8rP1357Ilqryvk9U2THkEVVwDJlERGQOhjwieiCGTCLbsOW+x/1eG6psyDt06BCmT5+O/fv3Izc3Fy1atMC4cePw4osv2rprFRJfbIjKn623fVvXJyLLVMmQFxUVhbCwMDg7O+Pll1+Gu7s7Nm3ahJdeeglJSUkYP368rbtIREREZBFd8Ytoi16vx7Bhw6DT6bB3716sWrUKixcvxvHjx9G0aVNMmTIFFy9etHU3iYiIiCxS5ULenj17cO7cOfTv3x+BgYFqu4eHB6ZMmYKcnBysXbvWdh0kIiIisoIqF/KMd+IODQ0t8JjxDvExMTHl2SUiIiIiq6tyIS8hIQEA4O/vX+CxunXrws3NTV2GiIiIqLKqchdepKWlAbj39WxhqlWrpi5TmOzsbGRnZxdYX2pqKnJzcwEAOp0OdnZ2MBgMyMvLU5c1tuv1epObi9rZ2UGn0xXZnpubi5y7DmaM9n9u3cqDwWBQ/19RFNjb2yMvr/D2+/tuaf2bN+/9bfKPKT97+3ubol6vL9Cec1exqHZqqr7QMZV0nqw1dqOixurg4FDofOTctWw3vXkzl9sezNv27tU3f/vjtsdtD7DNtgcA6engtldJt73i5ik1NRVA8b+tXuV+uzY0NBQ7d+5EQkICHn744QKP169fHxkZGUUGvRkzZmDmzJll3U0iIiKiB0pKSkKDBg2KfLzKHckzHsErKsSlp6ejRo0aRT5/8uTJ6m83AkBeXh5SU1Ph6ekJRbHsU5e50tPT4ePjg6SkpAf+ULEW61flsbM+tz3WZ31u+1WvPnDvCN7t27fh7e39wOWqXMgznouXkJCAJ554wuSxlJQUZGRkoF27dkU+38nJCU5OTiZt1atXt3o/zVGtWjWbbXC2rl+Vx8763PZYn/WrWm3WL/q0s/yq3IUXnTt3BgBERkYWeCwiIsJkGSIiIqLKqsqFvKeffhqNGzfGhg0bEB8fr7anpaVh7ty5cHR0xGuvvWa7DhIRERFZQZX7utbe3h6rV69GWFgYgoODTX7W7OLFi1i0aBEaNmxo626WipOTE6ZPn17ga+SqUL8qj531ue2xPutz26969Uujyl1da3Tw4EFMnz4d+/fvR25uLlq0aIFx48bhpZdesnXXiIiIiCxWZUMeERERkZZVuXPyiIiIiKoChjwiIiIiDWLIIyIiItIghjyNMJ5ayVMsiYiICGDI0wzjT6rZ6qfVjD+gLCI2CZqsb5v6+evZYtxVvT5g223P1uO3ZX1bjx3g3Ffl+iXFq2srsTt37kBRFBw9ehSKoiArKwt///03mjdvjvr168PBwQGOjo6ws7OzdVeJiIionDHkVWJ9+vTB7t27AQCZmZkmj9WtWxdPPfUUOnfujG7duqm/2ZuXlwedzjoHcG/duoWsrCzExsbC0dER165dQ3Z2Nlq1agV3d3dUr14dtWrVgouLi1XqsX7FqX/16lWkpKRg7969cHBwwOXLlwEAjz/+OFxdXeHt7Y1GjRqV2e86V/X6tt72bD1+W9a39dg591W7fmkx5FVSt2/fxvDhw/HMM88gNzcXzs7O8PDwQHx8PBITE3Hu3DmcO3cON2/eRJMmTTBkyBCMGTPGqjt+165dER0dDRcXF2RlZZk8Vr16dbRo0QLt27dH9+7d8cQTT8DDw8OqIZP1bVe/Y8eOiIuLg4eHBzIzM2EwGNSvLJycnODr64vAwECEh4cjODgYDRs2RF5eHhRFscopBVW9vq23PVuP35b1bT12zn3Vrl9aDHkadenSJZw6dQpRUVH46aefcPr0aTzyyCN4//338fzzz1u8/jt37mD27Nno3Lkz7ty5g+rVq8PFxQXHjh3DX3/9hbNnz+LEiRNITExErVq18M9//hOTJ09G/fr1rTA61rdl/ezsbKxbtw5t2rTBnTt34O3tjezsbPz+++9ITU3F2bNnceDAARw+fBg6nQ5hYWGYMmUK2rZta4WRs76ttz1bj9+W9W09ds591a5vFqFKyWAwiIhIXl6eyX/r9foCyx09elRGjRoliqJIgwYNZOvWrWXev+vXr0t8fLx88sknEhoaKoqiSPXq1eXDDz+UO3fuqP1lfe3Vv3PnjqSkpMj27dtlyJAhUqNGDVEURSZNmiTXr18Xkf9tv6xvfbbe9mw9flvWt/XYOfdVu35hGPIqufwbjF6vV3fgvLy8Ajvz0aNHJSgoSBwdHWXNmjVWqZu/jsFgKLAB5+XlSWJiosyfP188PT3F2dlZVq5caVHtilbf+N9VpX7+bSx/W2FvHteuXZP169dLYGCgKIoikydPtqh2Ratf2HZY1vVtve1VpL9/edfn3Ffdub+/vi3m3xwMeZXQ/RvU3bt3CyyTf6fP/yJw+PBh6dKlizg7O8u2bdvKtqP3uXjxojz//POiKIpMnz7dKusszafSsq5fXF/Kon7+ec7NzTXpy/39KYv6JXXz5k0ZNWqU2NnZyWuvvSZZWVkWrzP/2Iv725dF/fxsUb+yzL2Ituefc/9gWp77ilC/OAx5lZTBYJCvvvpKxo8fL4MGDZL+/fvLkiVL5OzZswWWu38jzM3NlWeffVYmTZpUpofu8/fBuFNevHhRBgwYIIqiyPr168u8dlnUv/8F9u+//7ZZfRGRW7dulWv90sj/ppORkSHvvvuuKIoiy5YtM3t9+eV/gyvv+jdv3pQrV66Ua/3KNPci2pp/zn3paGnu769vi/k3l73tzgak0hIRKIqCixcvYubMmfjyyy8BAG5ubsjIyMDGjRsxfvx4tGnTBsOGDcNrr70GJycnk3UYDAbY29tj6dKluHnzpsVX+xj79CD5r+ry9fXF+vXr4erqivj4ePTr18/sq752796NY8eOoXr16vD390fnzp3Lpb5Op0N2djbGjx+PP//8Ey4uLujVqxdeffXVAssaDAYoiqLWsFb9nJwcLFu2DCdPnsSlS5eg1+vx1FNPISQkBIGBgfDy8lLr63Q6dY6s9feX/38j0OKem3/bcHV1xaxZs+Dk5ISLFy/CYDCU+h6OxvV99dVX+O233+Do6IiAgAA899xz8PDwKJf6t27dwptvvonExESICHr37o3x48cXuHLdeKNa49/IGvUrwtwDVXP+Off3VMW5N67PlvNvtnKNlGQR46eyN954QxwdHWXMmDESGxsr165dk/3798usWbOkS5cu4uDgIIqiiL+/v6xdu7bYTzyllZmZqZ7Ea1TUeQn56fV6ycnJERGRlJQUiY+PL1Vd4/pv3Lgh77//vuh0OlEURRRFEW9vb/nggw/U9RfGYDCofwtL6l+4cEH69+8viqJI7dq1xcXFRWrVqiWbN29Wl01MTJTs7OwC6zBeGGNJ/YSEBOnTp496UnXNmjXVv4OiKBIQECAzZsyQa9euWa2+sfatW7ckPT29wGMlmXvj3z4zM7PAEeeS1k9OTpaxY8eqY7W3t5dq1arJ+PHjJTMzs8jn5597S+onJibKc889J4qiSPPmzcXDw0NcXFxk1apVInLv6MKxY8cKPcpi3H8tqW+Luc9fvyrOP+e+6s59/vq2mn9LMeRVMgaDQdzd3WXo0KGFBhq9Xi9RUVHy6quviqIo4uXlJd9//736XCNzvqY1vlDMmTNHRowYIVu2bJHz588XWFdZfQVs7L/xsPczzzwjixcvlpkzZ0rDhg1FURT54osvyqwPxvpjx44VZ2dnmTp1qvz+++/y448/iqenp/Tq1UsuXrwozz33nHh5eUndunVl1KhRVtupjfWHDRsmrq6uMn36dDl//ryIiBw/flw+++wzGTRokDRu3FgURREXFxeZP39+sV/rlIRx7idOnCjdunWTf//73xIfH1/gK6TCTg+wBmOdCRMmiKIo8sILL8hXX30l69atk6CgIFEURWbMmCEiZTv3b731llSrVk3mzZsnN2/elBMnTkizZs2kY8eO8scff8iTTz4piqKInZ2d/POf/5SjR49atb4t5l6kas8/577qzn3++raaf0sx5FUyUVFR4uzsLEuXLhWR/+2AhV1htX//fvH29pY6derIyZMnrVLfYDCon6Rq1qwpXbt2lVmzZsmuXbskJSWlwLIiIlevXpUpU6bIl19+aZX61atXl2effVZu3LghIvd27Li4OPHz85MGDRoUCFWZmZmyfft2iYuLs0p9T09P6d+/v8mRupdeeknq168v7dq1k6ZNm0pISIh6RPXVV19V+2mN+tWqVSsy5BsMBjly5IhMmTJF3N3dRVEUWbFihfqYpbXt7OzUF7KmTZvK66+/LuvXr1ffdPIvK3Lv0/eQIUPkgw8+sKi2cZ0eHh7St29f9YiCXq+XU6dOSdu2bcXV1VWOHDkiIv/7W9++fVu++uorq9w2yGAwSI0aNeSVV14xOXl62LBhUq1aNQkMDJT27dtLv379pEmTJqIoinTu3PmBRxlKW99Wc29cR1Wdf8591Z17Y31bzr8lGPIqmePHj4uLi4tMmDChyGX0er26o61bt04URZHPPvvMorrGHWfXrl2iKIp069ZNXn31ValXr576dek//vEPWbFihcTFxUlaWpr63K1bt4qTk5O88847ImLei46x/vfffy/Ozs7y9ddfq2M1Wr58uSiKIlOnThWR/52YGxsbK40bN5Z58+aZrMuc+t999504OTmp9Y2PGY+czp07Vw27f//9t3Tv3l0URZHvvvuu1DUL88svv4irq6vMnTtXRB4c8i9cuCBt2rQRJycn2bt3r9k1jWPfs2ePKIoivXv3lilTpkjTpk1FURRxdHSUNm3ayNtvvy1bt241+bro559/Fp1OJ2+++abaT3Prb968WVxcXAqd+++//14URZHhw4eLyP/mfv/+/eLr6yvvvvuuybrMqf/999+Li4uLfPXVV+pjBoNBhg0bpr6pGt8A7t69K4MHDxZFUSy+XZGRLeZepGrPP+e+6s59/ufYev4twZBXydy5c0cef/xx8fDwkI0bNxZ6+xSR/+1QFy5ckOrVq8uoUaMsqmvc2I1Bau3atSJyL0AtWbJEwsLCpFq1aqLT6dRPeevWrZM//vhDvRHzb7/9ZtI3c+qPHTtWGjVqJIcPHzZpFxHJzs6Wp556SurWrSupqanqY8uWLRNFUeT48eMW13/jjTfkkUceMTkUf+7cOenYsaO0a9dObTO+0ERERIidnZ0sWrSo1DULc+7cOalZs6a88sorhfZPxPSFf9u2bWJnZycLFy60uPbHH38siqKoR2QvXrwomzdvloEDB0qdOnVEURTx8PCQp59+WubMmSO//PKLjBw5UhRFUY8kW/K3nzhxovj4+KhHZO9/0X755ZfF0dFRLl26pLZZc+5Hjx4tDz/8sLrtidybj/bt20vbtm3VZY1vQIcPHxZnZ2d57733Sl2zMLace5GqOf+c+3uq4tznr2Pr+bcEQ14ltHPnTtHpdOLm5iYzZsyQkydPFhn2tm/fLu7u7gW+3jWHXq+XOXPmiE6nk1OnTqntOTk5cvnyZdm1a5e8++670q5dO3F0dBRnZ2dp2bKluLu7yyOPPCIiln1lmZubKyNGjBB3d/cCFzUYQ9XatWvVI2oiIpcuXZLu3buLv7+/xfXv3r0rffr0EX9/f5O/96ZNm0RRFPn8889FxPRI6m+//SYODg4yfvx4s+vmZzAYpHPnzuLi4iIrVqwo9OuA/DfqTEpKknr16smgQYMsrrtixQpxdnYucK7J3bt3JSEhQT777DMJDw8XV1dX9eiug4ODPProo2q/zKXX62Xs2LHi6OhY4D5TxrFGRESIoigyduxYEbk3dmvNfXZ2tgwaNEiaNGliUn/79u3i5OSkfmLPf0PyU6dOibu7u3okw1K2mntj7ao6/5z7qjv3IhVj/i3BkFfJGDei//73vxIYGCg6nU4CAgJk4sSJEhkZKadOnVLv33P27Fnp1q2buLm5qYfRLdnY8/LyZNeuXfLmm2/KxYsXC13mzp07kpiYKJs2bZIxY8aIj4+PKIoiS5YsEZHi7230IAaDQWbNmiWPPPJIkfcoSktLkzZt2oi3t7fk5uZKdHS0uLq6yuzZsy2un5OTIytWrFDPsTMG5qSkJBk/fnyBm5KK/O8w/6effmryHEv89ttv4u3tLYqiyODBgyUiIkKuXr1a6Nz+/PPP4ubmph5JtKT+4cOHZc6cOXL58uUil8nIyJDDhw/LkiVLJCAgwGpzLyKyYsUKadq0aZEXsty9e1e6d+8uTk5O8vfff0tMTIy4urrKnDlzLK6v1+vl66+/Vt9EjG8uKSkpsnDhQpO/6/1fMX300UfqOixlq7kXqbrzz7mvunMvUnHm31wMeZXY0aNHZfz48er5EcZPTu3atVOPoDk7O8v7778vItb9zbySrOv69evSu3dvURTFKiFT5F5w/f7779XfASzMihUrRFEUWbp0qcyaNUsURZGrV69apX5+xa0rNzdX3nnnHavWN/7dDxw4IF27dhU7OzupV6+eDBgwQFatWiVRUVFy7tw5MRgMsm/fPunYsaNUr17dan//kq7j7t270q9fP6vOfWpqquzfv19u3rxZ5DLGo6pTp06V6dOnl9ncFycnJ0fefvttzc19SddTleefc191516kbObfEgx5lVx2drYcOnRIli1bJgMHDpTg4GBp0KCBeHp6Su/eveXnn39WP0VY6+rO4hjrxcfHi7e3twQHB5f4udbw119/SfPmzaV+/frStm1b9Vw5S+vnP+civ8JuJSAiEhcXJwEBAdKzZ0+r1L9fSkqKrFixQrp27Sru7u5iZ2cnderUES8vL2nQoIHY2dlJjRo11E/T1ri6tqTLHD58WOrWrVvuc5+eni5PPfWUuLm5ScuWLaV9+/ZWq1+aud+/f780bdpUnn32WavVz6+8576k69Dq/HPuq+7ci1Ss+S8thrxK7P7Qdvv2bbly5YrcvXu30BtilreNGzeKvb29erKuNW7KXFxQNe5Qc+bMUW/1smHDBhEp/0Pm3bp1Ey8vL/U3gsuivl6vl4SEBNm0aZO899578uKLL0pAQIA8/vjjMnz4cNm3b5+6bHl+mvzhhx/koYceUs9XsfYNuQtjHJ/xvExFUWTjxo0iUv5z37FjR6lXr55ERESUWf2KOvciVXv+OfdVd+5Fymf+S4MhTwOK+qRgy0PEIiKnT5+Wf//73zb5JHPp0iVp166deHh4lPtOZrxnVVBQkMyYMaPc5iE7O1u9h1Zxv6db1q5cuSJbt261yTZ4/fp1CQsLE09Pz3Kf+7y8PDl16pQ8++yzsnDhwio59yJVc/459/dUxbkXsd38F0cRESmfH1AjKl/R0dG4ffs2nnvuufL9rUAAmZmZuH79Ojw9PeHu7l6i3/g1V1HrLsuaFV1CQgJu3LiBDh06IC8vz+zf6TSHXq/H3bt34eDgUOC3o62Nc184W80/5972qsq+X1IMeVRmqvqLTVXGua/aOP9VF+e+YmHII83iiw0REVVl5Xcck6icMeAREVFVxpBHREREpEEMeRqSl5dn6y4QERFRBcGQpyHGq4hsFfZsHTKren0iIqL8GPIqudzcXADAjh07EBUVBQDlesl4frYOmVW9vpHBYKiStVmf9bnts35VrV8UhrxKIP/Gc//F0A4ODgCAnj174umnn8aLL76I69evl1vfbB0yq3p9vV4PAPjhhx/w+eefIzs7u9zuB2jL2qzP+tz2Wb+q1i+Vcr75MpWB3Nxceffdd6Vp06aiKIoMHjxYUlNTrbb+/HcOL+ou3safknnhhRes/pNqVb1+SdSsWVMURZGnnnpKfvvttypTW+v1DQZDsXfOZ/2yqW/rsev1etavwvWthSGvgrp586bs2LFD3n77bZk2bZqsW7dOdu3aJQkJCXL37t1Cn2MwGGThwoVSs2ZNmTRpUrn1taxDJus/mF6vl2XLlkmXLl1EURTp0aOHXLlyRfO1tVq/ND8DyPrWrW/rsZcG61ft+iXFkFeBGF9gdu/eLR07dlSPDhn/eXh4SKdOnWTGjBkSFxenHmHKyclRf7swKytLPvnkE9m1a5dFfbF1yKzq9c39Ue///Oc/0qBBAxk4cGClrM36IpmZmfLrr7/Kp59+Kps3b5YbN26ISPG/Rc36lte39dgTExNl9uzZ0qNHD1mwYIFcvXrV5PGi+sH62qhfFhjyKhi9Xi/NmzcXLy8vWbhwoezbt082bNggS5culYEDB0qTJk1Ep9NJ48aNZdGiRerzrPFjyLYOmVW9fmFzaDAYiv2h7by8PJNg8vPPP0tcXFylqc36/9v2jh8/Li+++KLJdhceHi6///57kfWN2x7rm1e/oox9x44d8sgjj5jUf/7559Uaxu0sMzNT/X/Wr/z1yxpDXgVhfJP56quvxNHRUVavXl1gmZs3b8qhQ4fkgw8+kFatWqmHiK9fv26yHksCny1DJuvfq//CCy/Ixo0b1ReT/I+V5uukylSb9e+92XTr1k0cHBwkNDRUZsyYIc8995zodDoJDg4u86+CqnJ9W49dr9dL+/btpU6dOvL999/LoUOH5JVXXhFFUeTAgQOyc+dO6dWrlwQGBsrIkSPlyJEjrK+h+mWJIa+CMIaE1157TXx8fOTMmTMicu8o0f0BIjc3Vw4dOiR9+vQRRVFkwoQJVqtvq5DJ+vee880336ifIn18fOTNN9+UmJiYAsvr9Xr1U+TBgwdl8+bNkpmZWelqs/7/6n/99deiKIpMnDhRfezWrVsyYsQIURRFPvjggwLPuXTpknqkqbijjqxfsWrnX9f69evFxcVFPvzwQ/WxpKQkcXJykv79+0v16tWlbt268vDDD4uDg4M4OTnJ1q1bzarJ+hWnfnlgyKtA9Hq9jBkzRqpVq6aGvAedH6TX66VLly5Sv359ycjIsKh2RQmZVb3+Sy+9JIqiSKdOnUSn06mh4/HHH5eZM2fKyZMnCzx38ODBoiiK2ufKVJv1//d1Ubdu3aR9+/Zy6tQpEflfcEhNTZWgoCCpUaOGyQcKEZFFixaJTqeT8+fPs34lq52/fmhoqLRp00ZOnz6tPnb8+HHx8vKSxo0by8qVKyUvL09OnDgh06dPF0VRJCQkRO7cuWN2bda3ff3ywJBXQRjfaL7//nv1Cs37H8//dZHx5P93331XPDw8ZN++fRb3wZYhk/XvnevRpUsXadCggYiI3L59Wz766CNp3769yXkiISEh8tFHH0laWppcu3ZN2rRpI48++qiImP+1sS1rs/69o0YPP/ywDBo0yGSbM+7zxteFBQsWqI9duXJFunfvLv7+/qxvQX1bj/3vv/+Whg0byogRI0zqb9y4UXQ6naxYsaLAcwYMGCDu7u6SnJxsdl3Wrxj1yxpvhlxBKIqCvLw8dO/eHS+//DK+/PJLhIaGIiIiAllZWVAURb3Jrl6vh5OTEwwGA3Jzc5GTk4PmzZtbVF9EYGdnh06dOuH27duYP38+AMDe3l59PP8vORhv/tipUydkZGTg2LFjrG+hlJQUpKSkoHr16gAANzc3vPnmm4iLi8PZs2fx7rvvolGjRoiOjsaoUaPg6+uL3r1748iRIxg8eDAA8++6bsvarA8kJSUhJycH7u7usLe3V7c14z7fq1cvtG7dGh9++CGuXLkCADhx4gT27duHIUOGsL4F9W099tOnTyM7OxseHh7q6012djYOHToEEcHQoUMB3HsNysnJAQDUrl0b9vb2+PPPP82uy/oVo36Zs0WypILyH6X7448/pEePHqIoiri6usoLL7wgn376qZw4ccLkOdu3b5f69etLt27dCqzD3D5kZGRIv379RFEU6datm+zYsaPAIWnjpx29Xi8TJ04UFxcXq9wXrqrXv3XrlsydO1c++eQTdS5zc3MLHCWIjY2V4cOHS/Xq1dUjTCW91UNFrM36IufPnxd7e3sZPny4iBS+L3/33XeiKIp89tlnYjAY5N133xVFUdSbb7O+efVtPfZTp05Jnz591Cvyja9DH3/8scybN09ETM/5y8nJkYEDB0qtWrXk1q1bZtdl/YpRv6wx5FVg69atk3bt2qlvJvXr15e2bdtK//79pVu3buLi4iL+/v4SFRUlIuaf/Cti+5DJ+veem5GRUegLh8FgKPDV8cGDB8Xd3V2eeeYZi+rbsjbr/++50dHR8p///KfIwJCZmSnNmjWTdu3aydGjR6V9+/YSFBTE+hbUt/XYjfX++uuvQl+/jafl5L+g69SpUxIYGCghISGsX8nrlweGvArggw8+UK/WMxgMJhvN9evX5eeff5ZRo0ZJQECAKIoiDg4O4uPjI926dZOjR4+WWb/KM2SyfskZt5UPP/xQFEWRr7/+utzq27I264usXLlSvX+XnZ2drF27lvXLqb6tx24MGR988IE4OzvLxo0bWb8K1TcXQ56NRUVFib29vWzatOmByxk3sKSkJPnll1/k2rVr6tEFSz9J2Dpksr5p/ZK4ffu2hISEiKIoFs2/LWuzvmn9krxZpKSkiJ+fnzg6OoqbmxvrW2nbt/XYS7quU6dOyaOPPirt27e3+N6crG/b+uWFIc+GDAaD5OTkyGuvvSZubm4PDBsihZ/3YemGZuuQyfolq38/g8Egu3fvlpUrV4qIeT/FZcvarG9+/QULFqhHlETMP5JQletXxrHn5OTIsGHDxMnJST2KZO5rD+vbtn55YsirAC5fvixdunQRJycnmThxoqSlpZk8bjAY1JBhzU8Ptg6ZrP/g+mX5SdGWtVnfsvq5ubny1VdfFbinG+tX/NqW1he5d6FIVlaWiJj3+sP6tq1f3hjyKgi9Xq/eXb1p06aycuVKOXfuXIHl8h81sNa5ALYKmaxfsvrm/opGRa/N+qzPbb/k9a3dF9a3bf3ywpBXARjD2uXLl2XChAlib28v9vb20rVrV1m0aJHs2rVLLl++XOhzjVf/WKMPtgqZrG9efXO/JqxItVmf9bnts35VrV8eGPIqoBMnTsjw4cPFy8tLFEURLy8veeKJJyQsLEzGjh0rCxculLffflveeustGTJkiHz77bcW1bN1yGR929WvymNn/apdvyqPnfVtX7+8KCIitr4hM90j90I3dDodbt++jePHj+PQoUPYt28fDhw4gBs3bsDJyQl6vR4PPfQQvLy80Lx5cyxevBgNGza0Wj9+++03/Pvf/8YPP/yA69evo3bt2vDx8UGtWrXw6KOPol69erh27Rqys7ORmZmJsLAwvPjii6yvgfpVeeysX7XrV+Wxs77t65cVhrwKTq/XQ0Tg4OCAs2fPIjU1FU2bNkVqaioaNWoEOzs7q9WydchkfdvVr8pjZ/2qXb8qj531bV+/rDHkVWIiAkVRyrRGeYZM1q9Y9avy2Fm/atevymNnfdvXtyaGPDJbeYRM1q+Y9avy2Fm/atevymNnfdvXLy2GPCIiIiIN0tm6A0RERERkfQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBrEkEdERESkQQx5RERERBr0/wDsLltUXM2+MwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import Aer, QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "\n", + "qc = q_algo.export(\"qiskit\")\n", + "qc.measure_all()\n", + "simulator = Aer.get_backend(\"aer_simulator\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts, discard_lower=5)\n", + "plot_histogram(counts_readable)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wsixKhnLwo0i0JciGDRu0cOFClZSU6L777ut1m7POOkuSNHXq1KN/e+2112QYxkk/yTS1pJfrSiXDmlaHGz08957UnItakgJzbpRGjVLkp48r+uP/krlpswJzvyijotzr0BKibqW9BZEjPdbah37mp2vAy3KgxcNzH+Hp+0+CegDoj0gkotbWVkUiSbqCMwAg4agLgNTn5XOZl89jR7Q2HVsaw23RsNS+35tz95ef2kVisb/W/jP89ndSt+0zFn66Brxsp0/GtiESbQny9NNPKxqNas6cOcrPz+91m5ycHEknJtqO+PGPf6y33nrr6M+vfvWrhMYbq2jYGhbqlaRItHkYQ6Tb/fmv48XIyFDGvG9K3T2KvrBYxqSJClz9Wa/DSohoWNrtYHrR3eustb/8yi/XQFerd6NapeQoB728oWk/aI3sA1LVxo0bNX36dG3cuNHrUAAAHqEuAFKfl89lXia5jvC6kTsZnoud8Eu7SKycrKXVsic5ksmJ4qdrwMtyIBnLgAyvA/CrJUuWSJIuuuiiPrepq7MmMe0t0TZhwgSde+65iQmuH7rbvV10tfOwd+c+GoMH69OdcP7DUu5Ab2NwLC9PysyUwmEZ08+WEfBnrr9lr/VdsSvSLR3aLQ2qintIycMH10AylAFe8zqGzhYpP9vbGAAAAACkLy+fC82I1fkz5OEqNJ4/EybBc7FjPmgXiYVpSvsdrifYtE0qHBbfeJKKT64BL7+H3W3WFLLJ9NGRaEuQHTt2SJJGjBjR6+vhcFhLly6V1HuiLd6qq6sViMOVV1o0Ugvmvt7n6zNukrJOsZ5zdt6x37Nu6Xu77jbp3V+f/Pd9e5pUXj4ttmAT5Duff041ZTN6fS1e71/q+zO4ec4Xtap2SYzROmdmZUmP/Th+xzNNRR56WAr3SJUViv7mfxS44HwZw+NXc9ZU18jo9n5I2KQRF+hbn3vK0b5//+VbtGzL4jhHZF+8//2lxF8Dbv3715Sdo+98/tleXztdGSD1vxxcu2a9ri7/ZIzRJsb9c9/QkKKqXl9zoxz81KWXace+tbEFC7hg7ty5MW/b0GCt2vziiy9qxYrYV3J/8sknbUYFJKcnbt+hQCCoaCSi8vLen5WAVGOnHpCc1QXUA0ByeeRrKzUgt6TX1xLdNiZJ533s49rT7DCLEQefn/UdXXn213t9zY1nwh/+x4/07HULYozWOdrGnMvOzNVjtzpbcO2xnzyu31z/vfgG5BDXQO8MI6Cf37Gzz9fdKAerR9eoq8fBSIePiB636OOsWbNsPacfj0RbgrS1WfOKdXR09Pr6woUL1dTUpIKCAo0cOfKk16+//no1NTVp0KBBmj17tr7//e+rpKT3CjwWR27k+yvcfupLJitPChWc/jhGILbtPqqru1P19fX2d4yjto7WPl9L9PuXpMa9De58BqFsZcbxcNHnF8lctVqBL39JgZnnKnzrPyjy0MMKPrhAhmHE5Ry7G3ZLnd7PKTcwq++K5nR2N+7y/BqXFPd/fynx14Bb//4DMnb3+VqsZYDkvBzo6Gzz/Brp7Oq9bpPcKQd3N9Spfk8SfE+AD7W3x35z39nZefS3nf28/t4D8WLKPPqb6xp+Yac8l5zVBXxfgOTS3dP3s6cbz0R19Tu1t9m7cuFgc9+LpLnx/g8276dtrA/J0jYWMJwP+Gg6sCd56j2ugT5Fo9E+B/a4UQ7s3FWrcKTH2c592LPH+ZyUJNoSZOjQoTp48KCWL1+umTNnnvBaQ0OD5s2bJ0maMmXKCV+iwsJCzZs3T+eff77y8/P11ltv6b777tPbb7+t9957T6FQyFE8w4YNi8uItlDmqefqOt26Rdl51hfIjEpdp9i2r+O0dzerrKzsNFEmVk+078Dj9f5PdazsPMOVz8DMylK8luMz6+sVfeJJGWNrFLjuWhnBoAI3zVH0579Q9PlFCn7uM3E5z/Bhw5Oi106XcUDd4U5lZdj7vkaiYbVF93h+jUvx/feX3LkG3Pr3D+UF+3wtlrXb+lsOdoZbPL9GOsJ9zw/gRjmYOyBTZRnef0+AI3Jzc2Pe9si9XCgUsrWf1997IF4MGUd/c13DL+yU55KzuoDvC5Bc2rubJfX+vUx021jUjCq/KFuZed6VC0ZGuM/X3HgmNDJ6aBvrQ7K0jUlS7Z7VqiqdYnu/pvZtSVPvcQ30raVjvwrzBvf6WsJzBF2HVTp0SIyRnlo0Gj06SKm0tNTxcQzTNM24RIQT3H777frRj36kiooKvfLKK6qpqZEkLVu2TDfffLO2bdumnp4e3XrrrXr00UdPeaw//vGPmj17tp544gl9+ctfjjmGtrY25efnS5JaW1uVl3ea+cxi9ObPpPaDzvaddYuVpe5skd54zP7+wydJEy5zdu54qX1H2tr37Jmn1N/3bwSli26XAn2388dNWySsgUte7vdxzGhUkW/Ok7llqzL+80cyKiutv0ciitzxTZk7dirjsZ/EZYj0wYsvVV4wOfoPrHtJarA5s92QGmnK7MTEY1e8/v0l964Bt/79TVN67UfWmnpO9LccqDpXGjPL2bnjZdMSaddyZ/v29/1n5Unn/19n5wYSZePGjTFv29PTo5aWFhUUFCgzM/a+kePGjXMSGpB0XnlIkinJkC65y+togPiwUw9IzuoC6gEguTh55j+iv89EucXSx/7O2bnj5WCd9P7/ONu3v+9fks7+glQ03Nm+dtA21j/1a6QN/2tvn+wC6byvJs/aW1wDfVvxjLS/1tm+/S0HBlZIZ13v7NwfFa8cSpJcsv4zf/58DRo0SLt27dLEiRM1efJkVVdXa8aMGRo1apQuvvhiSbGtz/bpT39aeXl5eu+99xIddkwKnCd2U/rcyRBDQYk7SbZ4ij7znMz1GxT40k1HKxFJMoJBBb/1TSkaUeShh+W3nH/FGZJsjviuODMhoXjOb9eAYUgDPCwHvDz3EV6Wg8nw/oH+yMzMVHFxsa0kGwDAX6gLgNSX9s+E8RlI4owhFfQ+iCZp+a1dJFZDx0mZOfb2qTgjeZJs8eTHa6BgqIfnToJy8KN8eNkmh/Lycr3++uu68sorFQqFVFtbq+LiYj322GNavHixNm+2FoOMJdF2RLzmae2v4srTb5MoAyu8O/cRhcOkgEcdQ5Lh/dth7typ6C9+JWP8OAWuufqk142qEQrcNEfmmrWKPr/IgwgTZ0CpNO4TsW8/5nxpYHni4vGKX68Br76LRsCdXnun4+W1OtDDOgiIh507d+rrX/+6du50vp4nACC1URcAqc/L9plkaBvKyJIG9H/gjSOFw6RgCvVT8Gu7SCyCmdLUz8Xejjq4WhpxdmJj8oJfr4HiNC8HPyo5xpH61Pjx4/XCCy+c9PfW1lbV1tYqEAho0qRJpz3OokWL1NbWphkzZiQiTNtKx0mbX3M+bZpTReVSfom75+xNRrY0dLy0e4375y6LPS+bFIzKSmUu/sMptwneeL2CN8ZprG+SKZ9m3UxsWtL39yWQIdVcaG3rR369BoZPlra/ZU0j6aYh1dbUiV7LKZRKRklN29w9byBDGjbR3XMC8dbS0qJXX31Vt956q9ehAAA8Ql0ApL78EqudqrnO3fMGs6x2uWRQPlVa3+DNeVOJX9tFYlU03Jrib80ia5rAvpRNlcZebHUw9hu/XgMDK6Xcgc6XmHIqVCCVjHT3nLEg0eaBdevWyTRN1dTUnLTw8U033aRRo0bpzDPPVH5+vt566y3df//9mjZtmm644QaPIj5RRpbV0Fm3wt3zJlMionya+4m2QVVW4YXUMnyStfZa43pp91rp8B5JpnXjUH2hNHyilbxFagkVSIPHSHu3uHveZCsH3U60lY6VsmxOOwEAAAAAiVA+zf1E2/CJVrtcMigda3XED3e6d87MHGnIWPfOh/goHCZ97KtS0wdS/Spp/w5ZbWOGNGKGVDbF6tCL1GIYVoJ0y2vunrdsWnImZJMwJP9bs8bK0PQ2beTEiRP1+9//Xl/84hd1+eWX64knntBXv/pVvfbaa8rKSpKaVNYwXjeHaeeVWCM5ksWAUqlktLvnHDnT3fMhfjKyrBvwGTdJ2R+ORsrKlSrPJMmWyqrOtW4q3DKwwuoxmSwGVbk7VUggKFUlx8BuAAAAANCQaqu9yi3BTKkyiabVC2ZKVdPdPWfVDCnIsJGUFAhY35kzrj2ubSxPGvNxkmyprGyylJ3v3vmycqXyKe6dzw4SbR44VaLt29/+ttasWaPDhw+rp6dH27dv1w9+8AMVFiZXiZNTaK0p5QbDkCZeZjWyJpPxl7qXJKk4Syoqc+dcAGIzoFSqOsedcwUzpQmfcjexdzpGwN2yedR5Ut4gd84FAAAAAKcTCFrPRG49p1VfkHwJicrp0oCh7pyrcJhUeZY75wIQm4xsafwn3TvfuEutka3JiESbB06VaEsl5dOsuVjt6G6z5uPtbot9n6pz3Ku07cjOl8Z+wt4+Tt5/7kBpzCx75wHgjpEzpfzB9vZxUg6MOV/KKbJ3HjfkDbISYHY4ef+Fw5Kr5ybQH6Wlpbr77rtVWlrqdSgAAI9QFwD+MWCo/Q6YTp6JikdYU7Qlm0BAmnCZtZ52rJy8/0CGdZ5knC4OSHclo6Thk+3t46QcGDo+uWa8+ygG23pgyZIlXocQF4YhTblKen+h1NoU2z7v/treOYaOt9+I66ZhE6wFH7e/Fdv2dt9/Vp407Rp3p+kEELtAUJp2tfTe01Ln4dj2sVsOVJ6VXGuzfdSI6VJHs1S/Orbt7b7/3IHSlM9aD3CAH5SUlGju3LlehwEA8BB1AeAvo86TOg5JjRti297uM1H+YGnyVck1w8nx8kus9sFVf5DM6Om3t/v+jYA0ZTYznADJbNwnrHaxAzti295uOVBU7u7IOSdotkK/ZOZIZ14nFSSgI96wCdKEy5P3RuKIUR+zfuItVCCddb2UWxT/YwOInyPf1USMOBtxtlR9YXKXg4ZhDd0vT0DvyrxB1md7ZP52wA8OHTqkl15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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc.draw(\"mpl\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover_hash.ipynb.txt b/_sources/example_grover_hash.ipynb.txt new file mode 100644 index 00000000..2dc622a1 --- /dev/null +++ b/_sources/example_grover_hash.ipynb.txt @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover search: hash function preimage attack\n", + "\n", + "In the ever-evolving landscape of cybersecurity, the advent of quantum computing presents both an extraordinary opportunity and an unprecedented challenge. In this notebook we exploit a Grover Search to perform a preimage attack on a toy hash function.\n", + "\n", + "A preimage attack on a cryptographic hash function `h(m)` tries to find a message `m` that has a specific hash value. Using qlasskit it is easy to write an hash function like the following `hash_simp`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint4, Qint8, Qlist\n", + "\n", + "\n", + "@qlassf\n", + "def hash_simp(m: Qlist[Qint4, 2]) -> Qint8:\n", + " hv = 0\n", + " for i in m:\n", + " hv = ((hv << 4) ^ (hv >> 1) ^ i) & 0xFF\n", + "\n", + " return hv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thanks to the fact that qlasskit function are standard python functions, we can call the `original_f` to perform some kind of analysis on the hash function. Since the input space is tiny (it is a toy hash function), we can detect if the hash function is uniform (if it maps equally to the output space)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hash function output space: 256\n" + ] + } + ], + "source": [ + "from collections import Counter\n", + "\n", + "d = Counter(\n", + " hex(hash_simp.original_f((x, y))) for x in range(2**4) for y in range(2**4)\n", + ")\n", + "\n", + "print(\"Hash function output space:\", len(d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We got that `hash_simp` is following an uniform distribution. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hash_simp.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now we use our quantum function as an oracle for a Grover search, in order to find which input maps to the value `0xca`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Grover\n", + "\n", + "q_algo = Grover(hash_simp, Qint8(0xCA))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import Aer, QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "\n", + "qc = q_algo.export(\"qiskit\")\n", + "qc.measure_all()\n", + "simulator = Aer.get_backend(\"aer_simulator\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts, discard_lower=5)\n", + "plot_histogram(counts_readable)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `QlassF.original_f` we can double check the result without invoking a quantum simulator; calling it with the tuple `(12,12)` must result in the hash value `0xca`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0xca\n" + ] + } + ], + "source": [ + "print(hex(hash_simp.original_f((12, 12))))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover_subset.ipynb.txt b/_sources/example_grover_subset.ipynb.txt new file mode 100644 index 00000000..d3c34102 --- /dev/null +++ b/_sources/example_grover_subset.ipynb.txt @@ -0,0 +1,109 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover search: subset problem\n", + "\n", + "We define a function named `subset_sum(i,j)` that returns the sum of the elements `i` and `j` of a list `l`. We want to use a Grover search to find which `i` `j` combination led to a given value." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint2, Qint3\n", + "from typing import Tuple\n", + "\n", + "\n", + "@qlassf\n", + "def subset_sum(ii: Tuple[Qint2, Qint2]) -> Qint3:\n", + " l = [0, 5, 2, 3]\n", + " return l[ii[0]] + l[ii[1]] if ii[0] != ii[1] else 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our quantum function `subset_sum` will be used as an oracle for a Grover search. For instance, here we want to find the input value that produce the value `7`. Since we know that there are at least two result (`(i,j)` and `(j,i)`), we set `n_matching=2`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Grover\n", + "\n", + "q_algo = Grover(subset_sum, Qint3(7), n_matching=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`.\n", + "\n", + "In the output histogram, it's now evident that the input leading to a value of `7` are the tuples `(1,2)` and `(2,1)` (5+2 and 2+5), aligning with our expectations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import Aer, QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "\n", + "qc = q_algo.export(\"qiskit\")\n", + "qc.measure_all()\n", + "simulator = Aer.get_backend(\"aer_simulator\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_grover_sudoku.ipynb.txt b/_sources/example_grover_sudoku.ipynb.txt new file mode 100644 index 00000000..d29a793f --- /dev/null +++ b/_sources/example_grover_sudoku.ipynb.txt @@ -0,0 +1,141 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover search: sudoku solver\n", + "\n", + "In this example we are going to solve a sudoku puzzle. Since we have few qubits, we cannot solve a real 9x9 sudoku puzzle; our toy examples uses a 2x2 matrix where a valid solution is when in every row and every column there are no repeated values (`0` or `1`). We encode these xor-ing the values for each row and column. \n", + "Since we want a specific solution, we add a constraint `constr`: we want the `[0][0]` element to be `True`.\n", + "\n", + "`sudoku_check` is already an oracle so this time we instantiate the `Grover` algorithm without value." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qmatrix\n", + "from qlasskit.algorithms import Grover\n", + "\n", + "@qlassf\n", + "def sudoku_check(m: Qmatrix[bool, 2, 2]) -> bool:\n", + " constr = m[0][0]\n", + " sub0 = m[0][0] ^ m[0][1]\n", + " sub1 = m[1][0] ^ m[1][1]\n", + " sub2 = m[0][0] ^ m[1][0]\n", + " sub3 = m[0][1] ^ m[1][1]\n", + " return sub0 and sub1 and sub2 and sub3 and constr\n", + "\n", + "q_algo = Grover(sudoku_check)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`.\n", + "\n", + "We obtain that the solution for this puzzle is the matrix `[[True, False], [False, True]]`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import Aer, QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "\n", + "qc = q_algo.export(\"qiskit\")\n", + "qc.measure_all()\n", + "simulator = Aer.get_backend(\"aer_simulator\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts, discard_lower=20)\n", + "plot_histogram(counts_readable)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can create a more realistic sudoku game using numbers instead of booleans, but the resources required will scale exponentially. In the following code snippets, we recreate `sudoku_check` using `Qint2` and a 4x4 matrix. The sum of each column and row must be equal to 6 (3+2+1+0). As we can see, the resulting circuit of the checker requires more than 100 qubits, way above our simulation capabilities." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "QCircuit(1309 gates, 178 qubits)\n" + ] + } + ], + "source": [ + "from qlasskit import Qint2, Qint4\n", + "\n", + "@qlassf\n", + "def sudoku_check(m: Qmatrix[Qint2, 4, 4]) -> bool:\n", + " res = True\n", + " \n", + " # Constraints\n", + " res = (m[0][2] == 3) and (m[0][0] == 1)\n", + " \n", + " # Check every row and column\n", + " for i in range(4):\n", + " c = (Qint4(0) + m[i][0] + m[i][1] + m[i][2] + m[i][3]) == 6 \n", + " r = (Qint4(0) + m[0][i] + m[1][i] + m[2][i] + m[3][i]) == 6 \n", + " res = res and c and r\n", + " \n", + " return res\n", + "\n", + "#q_algo = Grover(sudoku_check)\n", + "print(sudoku_check.circuit())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_simon.ipynb.txt b/_sources/example_simon.ipynb.txt new file mode 100644 index 00000000..05728d9b --- /dev/null +++ b/_sources/example_simon.ipynb.txt @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simon function periodicity" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint4\n", + "\n", + "\n", + "@qlassf\n", + "def f(a: Qint4) -> Qint4:\n", + " return (a >> 3) + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.export(\"qiskit\").draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Simon\n", + "\n", + "q_algo = Simon(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = q_algo.export(\"qiskit\")\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import Aer, QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "\n", + "qc.measure_all()\n", + "simulator = Aer.get_backend(\"aer_simulator\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/example_unitary_of_f.ipynb.txt b/_sources/example_unitary_of_f.ipynb.txt new file mode 100644 index 00000000..4feb6d3a --- /dev/null +++ b/_sources/example_unitary_of_f.ipynb.txt @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unitary of qlasskit function\n", + "\n", + "In qlasskit, we can exploit external low-level frameworks to perform operations on the resulting quantum circuit. In this example, we use qiskit in order to obtain the unitary matrix of our `QlassF` function." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QlassF(a:bool, b:bool) -> bool:\n", + "\t_ret = a ^ ~b\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit, transpile, execute\n", + "from qiskit_aer import AerSimulator\n", + "from qiskit.visualization import array_to_latex\n", + "from qlasskit import qlassf\n", + "\n", + "\n", + "@qlassf\n", + "def f(a: bool, b: bool) -> bool:\n", + " return a ^ (not b)\n", + "\n", + "\n", + "print(f\"\\n{f}\\n\")\n", + "\n", + "qc = QuantumCircuit(f.num_qubits, f.num_qubits)\n", + "qc.append(f.gate(), f.qubits)\n", + "\n", + "qc.save_state()\n", + "qc.decompose().draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$$\n", + "\n", + "\\begin{bmatrix}\n", + "0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\\\\n", + " 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n", + " 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\\\\n", + " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\\\\n", + " 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n", + " 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\\\\n", + " 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\\\\n", + " 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\\\\n", + " \\end{bmatrix}\n", + "$$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simulator = AerSimulator(method=\"unitary\")\n", + "job = execute(qc, simulator, shots=8192)\n", + "result = job.result()\n", + "array_to_latex(result.get_unitary(qc, 3), max_size=16)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/exporter.ipynb.txt b/_sources/exporter.ipynb.txt new file mode 100644 index 00000000..87301c8c --- /dev/null +++ b/_sources/exporter.ipynb.txt @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exporting to other frameworks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qlasskit implements circuit / gate exporters for Qiskit, Cirq, Qasm, Sympy and Pennylane. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import Qint2, qlassf\n", + "\n", + "\n", + "@qlassf\n", + "def hello_world(a: bool, b: Qint2) -> Qint2:\n", + " return b + (1 if a else 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qiskit" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = hello_world.export(\"qiskit\")\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## QASM" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OPENQASM 3.0;\n", + "\n", + "gate hello_world a b.0 b.1 anc_0 _ret.0 _ret.1 {\n", + "\tcx a _ret.0\n", + "\tcx b.0 _ret.0\n", + "\tcx b.1 anc_0\n", + "\tccx a b.0 anc_0\n", + "}\n", + "\n", + "\n" + ] + } + ], + "source": [ + "qc = hello_world.export(\"qasm\")\n", + "print(qc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cirq" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
0: ───hello_world───\n",
+       "      │\n",
+       "1: ───hello_world───\n",
+       "      │\n",
+       "2: ───hello_world───\n",
+       "      │\n",
+       "3: ───hello_world───\n",
+       "      │\n",
+       "4: ───hello_world───
" + ], + "text/plain": [ + "0: ───hello_world───\n", + " │\n", + "1: ───hello_world───\n", + " │\n", + "2: ───hello_world───\n", + " │\n", + "3: ───hello_world───\n", + " │\n", + "4: ───hello_world───" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import cirq\n", + "\n", + "qc = hello_world.export(\"cirq\")\n", + "qc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pennylane" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pennylane as qml\n", + "\n", + "tape = hello_world.export(\"pennylane\")\n", + "tape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sympy" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle C_{0,1}{\\left(X_{4}\\right)} \\text{CNOT}_{2,4} \\text{CNOT}_{1,3} \\text{CNOT}_{0,3} {\\left|00000\\right\\rangle }$" + ], + "text/plain": [ + "C((0,1),X(4))*CNOT(2,4)*CNOT(1,3)*CNOT(0,3)*|00000>" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = hello_world.export(\"sympy\")\n", + "qc" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/generated/qlasskit.algorithms.grover.Grover.rst.txt b/_sources/generated/qlasskit.algorithms.grover.Grover.rst.txt new file mode 100644 index 00000000..c5426915 --- /dev/null +++ b/_sources/generated/qlasskit.algorithms.grover.Grover.rst.txt @@ -0,0 +1,40 @@ +qlasskit.algorithms.grover.Grover +================================= + +.. currentmodule:: qlasskit.algorithms.grover + +.. autoclass:: Grover + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~Grover.__init__ + ~Grover.circuit + ~Grover.decode_counts + ~Grover.decode_output + ~Grover.draw + ~Grover.encode_input + ~Grover.export + ~Grover.gate + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~Grover.input_qubits + ~Grover.input_size + ~Grover.num_qubits + ~Grover.output_qubits + ~Grover.output_size + ~Grover.qubits + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.algorithms.qalgorithm.rst.txt b/_sources/generated/qlasskit.algorithms.qalgorithm.rst.txt new file mode 100644 index 00000000..d819a261 --- /dev/null +++ b/_sources/generated/qlasskit.algorithms.qalgorithm.rst.txt @@ -0,0 +1,41 @@ +qlasskit.algorithms.qalgorithm +============================== + +.. automodule:: qlasskit.algorithms.qalgorithm + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + oraclize + + + + + + .. rubric:: Classes + + .. autosummary:: + + QAlgorithm + + + + + + .. rubric:: Exceptions + + .. autosummary:: + + ConstantOracleException + + + + + diff --git a/_sources/generated/qlasskit.qcircuit.gates.rst.txt b/_sources/generated/qlasskit.qcircuit.gates.rst.txt new file mode 100644 index 00000000..f95b2ca8 --- /dev/null +++ b/_sources/generated/qlasskit.qcircuit.gates.rst.txt @@ -0,0 +1,53 @@ +qlasskit.qcircuit.gates +======================= + +.. automodule:: qlasskit.qcircuit.gates + + + + + + + + .. rubric:: Functions + + .. autosummary:: + + apply + + + + + + .. rubric:: Classes + + .. autosummary:: + + Barrier + CCX + CP + CX + H + I + MCX + MCtrl + NopGate + P + QControlledGate + QGate + S + Swap + T + Toffoli + X + Y + Z + + + + + + + + + diff --git a/_sources/generated/qlasskit.qcircuit.qcircuit.QCircuit.rst.txt b/_sources/generated/qlasskit.qcircuit.qcircuit.QCircuit.rst.txt new file mode 100644 index 00000000..9dd024df --- /dev/null +++ b/_sources/generated/qlasskit.qcircuit.qcircuit.QCircuit.rst.txt @@ -0,0 +1,53 @@ +qlasskit.qcircuit.qcircuit.QCircuit +=================================== + +.. currentmodule:: qlasskit.qcircuit.qcircuit + +.. autoclass:: QCircuit + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~QCircuit.__init__ + ~QCircuit.add_qubit + ~QCircuit.append + ~QCircuit.append_circuit + ~QCircuit.barrier + ~QCircuit.ccx + ~QCircuit.copy + ~QCircuit.cp + ~QCircuit.cx + ~QCircuit.draw + ~QCircuit.export + ~QCircuit.get_key_by_index + ~QCircuit.h + ~QCircuit.iqft + ~QCircuit.mctrl + ~QCircuit.mcx + ~QCircuit.qft + ~QCircuit.random + ~QCircuit.repeat + ~QCircuit.s + ~QCircuit.swap + ~QCircuit.t + ~QCircuit.x + ~QCircuit.y + ~QCircuit.z + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~QCircuit.num_gates + ~QCircuit.used_qubits + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.rst.txt b/_sources/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.rst.txt new file mode 100644 index 00000000..fc0e682b --- /dev/null +++ b/_sources/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.rst.txt @@ -0,0 +1,40 @@ +qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper +================================================= + +.. currentmodule:: qlasskit.qcircuit.qcircuitwrapper + +.. autoclass:: QCircuitWrapper + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~QCircuitWrapper.__init__ + ~QCircuitWrapper.circuit + ~QCircuitWrapper.decode_counts + ~QCircuitWrapper.decode_output + ~QCircuitWrapper.draw + ~QCircuitWrapper.encode_input + ~QCircuitWrapper.export + ~QCircuitWrapper.gate + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~QCircuitWrapper.input_qubits + ~QCircuitWrapper.input_size + ~QCircuitWrapper.num_qubits + ~QCircuitWrapper.output_qubits + ~QCircuitWrapper.output_size + ~QCircuitWrapper.qubits + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.qlassfun.QlassF.rst.txt b/_sources/generated/qlasskit.qlassfun.QlassF.rst.txt new file mode 100644 index 00000000..148773ed --- /dev/null +++ b/_sources/generated/qlasskit.qlassfun.QlassF.rst.txt @@ -0,0 +1,52 @@ +qlasskit.qlassfun.QlassF +======================== + +.. currentmodule:: qlasskit.qlassfun + +.. autoclass:: QlassF + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~QlassF.__init__ + ~QlassF.bind + ~QlassF.circuit + ~QlassF.compile + ~QlassF.decode_counts + ~QlassF.decode_output + ~QlassF.draw + ~QlassF.encode_input + ~QlassF.export + ~QlassF.f + ~QlassF.from_function + ~QlassF.gate + ~QlassF.to_logicfun + ~QlassF.truth_table + ~QlassF.truth_table_header + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~QlassF.input_qubits + ~QlassF.input_size + ~QlassF.num_qubits + ~QlassF.output_qubits + ~QlassF.output_size + ~QlassF.qubits + ~QlassF.name + ~QlassF.original_f + ~QlassF.args + ~QlassF.returns + ~QlassF.expressions + + \ No newline at end of file diff --git a/_sources/generated/qlasskit.qlassfun.qlassf.rst.txt b/_sources/generated/qlasskit.qlassfun.qlassf.rst.txt new file mode 100644 index 00000000..51d99df6 --- /dev/null +++ b/_sources/generated/qlasskit.qlassfun.qlassf.rst.txt @@ -0,0 +1,6 @@ +qlasskit.qlassfun.qlassf +======================== + +.. currentmodule:: qlasskit.qlassfun + +.. autofunction:: qlassf \ No newline at end of file diff --git a/_sources/generated/qlasskit.qlassfun.qlassfa.rst.txt b/_sources/generated/qlasskit.qlassfun.qlassfa.rst.txt new file mode 100644 index 00000000..cb63d945 --- /dev/null +++ b/_sources/generated/qlasskit.qlassfun.qlassfa.rst.txt @@ -0,0 +1,6 @@ +qlasskit.qlassfun.qlassfa +========================= + +.. currentmodule:: qlasskit.qlassfun + +.. autofunction:: qlassfa \ No newline at end of file diff --git a/_sources/how_it_works.ipynb.txt b/_sources/how_it_works.ipynb.txt new file mode 100644 index 00000000..fe3b9699 --- /dev/null +++ b/_sources/how_it_works.ipynb.txt @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How it works\n", + "\n", + "To convert Python code into a quantum circuit, qlasskit implements a series of transformations:\n", + "\n", + "1. It begins with the Python *AST* (Abstract Syntax Tree), converting it into a more streamlined form using the `ast2ast` module. \n", + "2. Next, the streamlined AST is translated into *boolean expressions* as an intermediate step by the `ast2logic` module. \n", + "During this phase, boolean expressions are refined and optimized in preparation for the final transformation.\n", + "3. Finally, the `compiler` module takes these optimized boolean expressions and compiles them into a \n", + "*quantum circuit*.\n", + "\n", + "Unlike other libraries that translate individual operations into quantum circuits before combining them, \n", + "qlasskit constructs a single boolean expression for each output qubit of the entire function. \n", + "This unique approach facilitates advanced optimization leveraging boolean algebraic properties.\n", + "\n", + "\n", + "For instance, let assume we have the following function:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint2, Qint4\n", + "from qiskit import QuantumCircuit\n", + "\n", + "\n", + "@qlassf\n", + "def f_comp(b: bool, n: Qint2) -> Qint2:\n", + " for i in range(3):\n", + " n += 1 if b else 2\n", + " return n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we decompose the algorithm in 3 separate additions and we compile them separately, we obtain the following circuit:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Operations: OrderedDict([('cx', 12), ('barrier', 3), ('x', 3), ('ccx', 3)])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@qlassf\n", + "def f1(b: bool, n: Qint2) -> Qint2:\n", + " return n + (1 if b else 2)\n", + "\n", + "\n", + "qc = QuantumCircuit(f_comp.num_qubits * 2 - 1)\n", + "\n", + "for i in range(3):\n", + " qc.barrier(label=f\"it_{i}\")\n", + " qc.append(f1.gate(), [0] + list(range(1 + i * 2, 5 + i * 2)))\n", + "\n", + "print(\"Operations:\", qc.decompose().count_ops())\n", + "qc.decompose().draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While if we compile the whole function to a quantum circuit using qlasskit, we obtain the following quantum circuit:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Operations: OrderedDict([('cx', 4), ('x', 1), ('ccx', 1)])\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = QuantumCircuit(f_comp.num_qubits)\n", + "qc.append(f_comp.gate(), f_comp.qubits)\n", + "\n", + "print(\"Operations:\", qc.decompose().count_ops())\n", + "qc.decompose().draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the circuit drawings, qlasskit approach needs half the number of qubits and half the number of gates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AST Traslator\n", + "Given a python function, the `qlasskit.ast2logic` module walks its syntax tree translating all the statements / \n", + "expressions to boolean expressions.\n", + "\n", + "\n", + "For instance, the following function:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "@qlassf\n", + "def f(n: Qint4) -> bool:\n", + " return n == 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is translated to this boolean expression:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(_ret, n.0 & n.1 & ~n.2 & ~n.3)]\n" + ] + } + ], + "source": [ + "print(f.expressions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Compiler\n", + "\n", + "The boolean expressions are then being fed to the `qlasskit.compiler`` which compiles boolean expressions\n", + "to invertible circuits, introducing auxiliary qubits. In this step, the compiler will automatically uncompute \n", + "auxiliary qubits in order to reduce the number of qubits needed and the circuit footprint. \n", + "\n", + "For the compilation, two backends are supported:\n", + "\n", + "- InternalCompiler\n", + "- Tweedledum.xag_synth\n", + "\n", + "\n", + "## Result \n", + "\n", + "The result of the compiler is a quantum circuit represented with qlasskit `QCircuit`. This circuit\n", + "can now be exported to one of the supported framework as a gate or as a standalone circuit.\n", + "\n", + "\n", + "The previous example function `f`, is translated to the following quantum circuit: the \n", + "result is available at qubit `q6`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.export().draw(\"mpl\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..5b4865de --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,51 @@ +Qlasskit +==================================== + +Qlasskit is a Python library that allows quantum developers to write classical algorithms in pure +Python and translate them into unitary operators (gates) for use in quantum circuits. + +.. toctree:: + :maxdepth: 2 + :caption: Qlasskit + + quickstart.ipynb + how_it_works.ipynb + supported + algorithms + exporter.ipynb + api + +.. toctree:: + :maxdepth: 2 + :caption: Examples + + example_grover.ipynb + example_grover_subset.ipynb + example_grover_hash.ipynb + example_grover_sudoku.ipynb + example_grover_factorize.ipynb + example_simon.ipynb + example_deutsch_jozsa.ipynb + example_unitary_of_f.ipynb + example_big_circuit.ipynb + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` + + +Cite +====== + +.. code-block:: latex + + @software{qlasskit2023, + author = {Davide Gessa}, + title = {qlasskit: a python-to-quantum circuit compiler}, + url = {https://github.com/dakk/qlasskit}, + year = {2023}, + } \ No newline at end of file diff --git a/_sources/quickstart.ipynb.txt b/_sources/quickstart.ipynb.txt new file mode 100644 index 00000000..f1fe0361 --- /dev/null +++ b/_sources/quickstart.ipynb.txt @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quickstart\n", + "\n", + "First install qlasskit using pip.\n", + "\n", + "```pip install qlasskit```\n", + "\n", + "We now define a qlassf function that sums two numbers:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qint2\n", + "\n", + "\n", + "@qlassf\n", + "def sum_two_numbers(a: Qint2, b: Qint2) -> Qint2:\n", + " return a + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now export the resulting quantum circuit to any supported framework:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuit = sum_two_numbers.export(\"qiskit\")\n", + "circuit.draw(\"mpl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The qlassf function can be also exported as a gate, if the destination framwork supports it. We can use `encode_input` and `decode_output` in order to convert from/to high level types of qlasskit without worrying about the binary representation." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import QuantumCircuit\n", + "\n", + "qc = QuantumCircuit(sum_two_numbers.num_qubits, len(sum_two_numbers.output_qubits))\n", + "\n", + "qc.initialize(\n", + " sum_two_numbers.encode_input(Qint2(1), Qint2(2)), sum_two_numbers.input_qubits\n", + ")\n", + "qc.append(sum_two_numbers.gate(\"qiskit\"), sum_two_numbers.qubits)\n", + "qc.measure(sum_two_numbers.output_qubits, range(len(sum_two_numbers.output_qubits)))\n", + "qc.draw(\"mpl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import Aer, QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "\n", + "simulator = Aer.get_backend(\"aer_simulator\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = sum_two_numbers.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/supported.rst.txt b/_sources/supported.rst.txt new file mode 100644 index 00000000..b1e371f6 --- /dev/null +++ b/_sources/supported.rst.txt @@ -0,0 +1,259 @@ +Supported python subset +==================================== + +Qlasskit supports a subset of python. This subset will be expanded, but it is +limited by the linearity of quantum circuits and by the number of qubits. + +The structure of a qlasskit function has the following pattern: + +.. code-block:: python + + @qlasskit + def f(param: type, [...param: type]) -> type: + statement + ... + statement + + +Types +----- + +All types has a static size. + +bool +^^^^ + +Boolean type. + + +Qint +^^^^ + +Unsigned integers; this type has subtypes for different Qint sizes (Qint2, Qint4, Qint8, Qint12, Qint16). +Single bit of the Qint are accessible by the subscript operator `[]`. + + +Tuple +^^^^^ + +Container type holding different types. + + +List +^^^^ + +Qlist[T, size] denotes a fixed-size list in qlasskit. +For example, the list `[1,2,3]` is typed as `Qlist[Qint2,3]`. + + +Matrix +^^^^ + +Qmatrix[T, m, n] denotes a fixed-size list in qlasskit. +For example, the matrix `[[1,2],[3,4]]` is typed as `Qmatrix[Qint2,2,2]`. + + + + + +Expressions +----------- + +Constants +^^^^^^^^^^^^^ + +.. code-block:: python + + True + +.. code-block:: python + + 42 + + +Tuple +^^^^^ + +.. code-block:: python + + (a, b) + +List (fixed size) +^^^^^^^^^^^^^^^^^ + +.. code-block:: python + + [a, b] + + +2D Matrix (fixed size) +^^^^^^^^^^^^^^^^^ + +.. code-block:: python + + [[a, b], [c,d]] + + +Subscript +^^^^^^^^^ + +.. code-block:: python + + a[0] + +Boolean operators +^^^^^^^^^^^^^^^^^ + +.. code-block:: python + + not a + +.. code-block:: python + + a and b + +.. code-block:: python + + a or b + + + +If expressions +^^^^^^^^^^^^^^ + +.. code-block:: python + + a if b else c + +Comparators +^^^^^^^^^^^ + +.. code-block:: python + + a > b or b <= c and c == d or c != a + + +Unary Op +^^^^^^^^^ + +.. code-block:: python + + ~a + + + +Bin Op +^^^^^^^^^ + +.. code-block:: python + + a << 1 + +.. code-block:: python + + a >> 2 + +.. code-block:: python + + a + b + +.. code-block:: python + + a - b + +.. code-block:: python + + a * b + +.. code-block:: python + + a % 2 + +.. note:: + Modulo operator only works with 2^n values. + + +Function call +^^^^^^^^^^^^^ + +Bultin functions: +- `print()`: debug function, ignore by conversion +- `len(Tuple)`, `len(Qlist)``: returns the length of a tuple +- `max(a, b, ...)`, `max(Tuple)`, `max(Qlist)`: returns the max of a tuple +- `min(a, b, ...)`, `min(Tuple)`, `min(Qlist)`: returns the min of a tuple +- `sum(Tuple)`, `sum(Qlist)`: returns the sum of the elemnts of a tuple / list +- `all(Tuple)`, `all(Qlist)`: returns True if all of the elemnts are True +- `any(Tuple)`, `any(Qlist)`: returns True if any of the elemnts are True + + + +Statements +---------- + +Assign +^^^^^^ + +.. code-block:: python + + c = not a + +Return +^^^^^^ + +.. code-block:: python + + return b+1 + + +For loop +^^^^^^^^ + +.. code-block:: python + + for i in range(4): + a += i + + +.. note:: + Please note that in qlasskit, for loops are unrolled during compilation. Therefore, + it is essential that the number of iterations for each for loop is known at the + time of compilation. + +Function def +^^^^^^^^^^^^ + +.. code-block:: python + + def f(t: Qlist[Qint4,2]) -> Qint4: + return t[0] + t[1] + + +If then else +^^^^^^^^^^^^ + +.. code-block:: python + + c = 0 + if cond: + c += 12 + else: + c += 13 + +.. note:: + At present, the if-then-else statement in qlasskit is designed to support branch bodies + that exclusively contain assignment statements. + + + +Quantum Hybrid +--------------- + +In a qlassf function, you have the option to utilize quantum gates through the Q module. It's +important to keep in mind that incorporating quantum gates within a qlasskit function leads +to a Python function that exhibits distinct behaviors compared to its quantum counterpart. + +.. code-block:: python + + def bell(a: bool, b: bool) -> bool: + return Q.CX(Q.H(a), b) + diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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.rst-content .wy-alert-warning.hint, +html[data-theme="dark"] .rst-content .wy-alert-warning.important, +html[data-theme="dark"] .rst-content .wy-alert-warning.note, +html[data-theme="dark"] .rst-content .wy-alert-warning.seealso, +html[data-theme="dark"] .rst-content .wy-alert-warning.tip, +html[data-theme="dark"] .wy-alert.wy-alert-warning { + background-color: #533500; +} + +html[data-theme="dark"] .rst-content .admonition-todo .admonition-title, +html[data-theme="dark"] .rst-content .admonition-todo .wy-alert-title, +html[data-theme="dark"] .rst-content .attention .admonition-title, +html[data-theme="dark"] .rst-content .attention .wy-alert-title, +html[data-theme="dark"] .rst-content .caution .admonition-title, +html[data-theme="dark"] .rst-content .caution .wy-alert-title, +html[data-theme="dark"] .rst-content .warning .admonition-title, +html[data-theme="dark"] .rst-content .warning .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.admonition + .admonition-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.admonition + .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.danger .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.danger .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.error .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.error .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.hint .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.hint .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.important + .admonition-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.important + .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.note .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.note .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-warning.seealso + .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.seealso .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.tip .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-warning.tip .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert.wy-alert-warning + .admonition-title, +html[data-theme="dark"] + .wy-alert.wy-alert-warning + .rst-content + .admonition-title, +html[data-theme="dark"] .wy-alert.wy-alert-warning .wy-alert-title { + background-color: #803b00; +} + +html[data-theme="dark"] .rst-content .danger, +html[data-theme="dark"] .rst-content .error, +html[data-theme="dark"] .rst-content .wy-alert-danger.admonition, +html[data-theme="dark"] .rst-content .wy-alert-danger.admonition-todo, +html[data-theme="dark"] .rst-content .wy-alert-danger.attention, +html[data-theme="dark"] .rst-content .wy-alert-danger.caution, +html[data-theme="dark"] .rst-content .wy-alert-danger.hint, +html[data-theme="dark"] .rst-content .wy-alert-danger.important, +html[data-theme="dark"] .rst-content .wy-alert-danger.note, +html[data-theme="dark"] .rst-content .wy-alert-danger.seealso, +html[data-theme="dark"] .rst-content .wy-alert-danger.tip, +html[data-theme="dark"] .rst-content .wy-alert-danger.warning, +html[data-theme="dark"] .wy-alert.wy-alert-danger { + background-color: #82231a; +} + +html[data-theme="dark"] .rst-content .danger .admonition-title, +html[data-theme="dark"] .rst-content .danger .wy-alert-title, +html[data-theme="dark"] .rst-content .error .admonition-title, +html[data-theme="dark"] .rst-content .error .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.admonition-todo + .admonition-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.admonition-todo + .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.admonition + .admonition-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.admonition + .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.attention + .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.attention .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.caution .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.caution .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.hint .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.hint .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert-danger.important + .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.important .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.note .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.note .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.seealso .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.seealso .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.tip .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.tip .wy-alert-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.warning .admonition-title, +html[data-theme="dark"] .rst-content .wy-alert-danger.warning .wy-alert-title, +html[data-theme="dark"] + .rst-content + .wy-alert.wy-alert-danger + .admonition-title, +html[data-theme="dark"] + .wy-alert.wy-alert-danger + .rst-content + .admonition-title, +html[data-theme="dark"] .wy-alert.wy-alert-danger .wy-alert-title { + background-color: #b9372b; +} + +html[data-theme="dark"] .wy-nav-top { + background-color: #0b152d; +} + +html[data-theme="dark"] .rst-content table.docutils thead, +html[data-theme="dark"] .rst-content table.field-list thead, +html[data-theme="dark"] .wy-table thead { + color: var(--dark-text-color); +} + +html[data-theme="dark"] + .rst-content + table.docutils:not(.field-list) + tr:nth-child(2n-1) + td, +html[data-theme="dark"] .wy-table-backed, +html[data-theme="dark"] html[data-theme="dark"] .wy-table-odd td, +html[data-theme="dark"] .wy-table-striped tr:nth-child(2n-1) td { + background-color: #181818; +} + +html[data-theme="dark"] .rst-content table.docutils td, +html[data-theme="dark"] .wy-table-bordered-all td, +html[data-theme="dark"].writer-html5 .rst-content table.docutils th, +html[data-theme="dark"] .rst-content table.docutils, +html[data-theme="dark"] .wy-table-bordered-all { + border-color: #262626; +} + +html[data-theme="dark"] .rst-content table.docutils caption, +html[data-theme="dark"] .rst-content table.field-list caption, +html[data-theme="dark"] .wy-table caption { + color: var(--dark-text-color); +} + +html[data-theme="dark"] .wy-menu-vertical li.toctree-l3.current > a, +html[data-theme="dark"] + .wy-menu-vertical + li.toctree-l3.current + li.toctree-l4 + > a { + background-color: #18181a; +} + +html[data-theme="dark"] .guilabel { + background-color: #343434; + border-color: #4d4d4d; +} diff --git a/_static/dark_mode_css/general.css b/_static/dark_mode_css/general.css new file mode 100644 index 00000000..aa614f81 --- /dev/null +++ b/_static/dark_mode_css/general.css @@ -0,0 +1,68 @@ +input[type='color'], +input[type='date'], +input[type='datetime-local'], +input[type='datetime'], +input[type='email'], +input[type='month'], +input[type='number'], +input[type='password'], +input[type='search'], +input[type='tel'], +input[type='text'], +input[type='time'], +input[type='url'], +input[type='week'] { + box-shadow: none; +} + +.theme-switcher { + border-radius: 50%; + position: fixed; + right: 1.6em; + bottom: 1.4em; + z-index: 3; + border: none; + height: 2.2em; + width: 2.2em; + background-color: #fcfcfc; + font-size: 20px; + -webkit-box-shadow: 0px 3px 14px 4px rgba(0, 0, 0, 0.62); + box-shadow: 0px 3px 14px 4px rgba(0, 0, 0, 0.62); + color: #404040; + transition: all 0.3s ease-in-out; +} + +body, +.wy-nav-content-wrap, +.wy-nav-content, +.section, +.highlight, +.rst-content div[class^='highlight'], +.wy-nav-content a, +.btn-neutral, +.btn, +footer, +.wy-nav-side, +.wy-menu-vertical li, +.wy-menu-vertical a, +.wy-side-nav-search .wy-dropdown, +.wy-side-nav-search a, +.wy-side-nav-search input, +html.writer-html4 .rst-content dl:not(.docutils) > dt, +html.writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + > dt, +.rst-content code, +.rst-content tt, +html.writer-html4 .rst-content dl:not(.docutils) dl:not(.field-list) > dt, +html.writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + dl:not(.field-list) + > dt, +code, +.rst-content code.xref, +.rst-content tt.xref { + transition: all 0.2s ease-in-out; +} diff --git a/_static/dark_mode_js/default_dark.js b/_static/dark_mode_js/default_dark.js new file mode 100644 index 00000000..ea63e072 --- /dev/null +++ b/_static/dark_mode_js/default_dark.js @@ -0,0 +1,13 @@ +const loadTheme = () => { + let theme = localStorage.getItem('theme'); + + if (theme !== null) { + if (theme === 'dark') + document.documentElement.setAttribute('data-theme', 'dark'); + } else { + localStorage.setItem('theme', 'dark'); + document.documentElement.setAttribute('data-theme', 'dark'); + } +}; + +loadTheme(); diff --git a/_static/dark_mode_js/default_light.js b/_static/dark_mode_js/default_light.js new file mode 100644 index 00000000..2b19f92e --- /dev/null +++ b/_static/dark_mode_js/default_light.js @@ -0,0 +1,13 @@ +const loadTheme = () => { + let theme = localStorage.getItem('theme'); + + if (theme !== null) { + if (theme === 'dark') + document.documentElement.setAttribute('data-theme', 'dark'); + } else { + localStorage.setItem('theme', 'light'); + document.documentElement.setAttribute('data-theme', 'light'); + } +}; + +loadTheme(); diff --git a/_static/dark_mode_js/theme_switcher.js b/_static/dark_mode_js/theme_switcher.js new file mode 100644 index 00000000..8e260552 --- /dev/null +++ b/_static/dark_mode_js/theme_switcher.js @@ -0,0 +1,39 @@ +const createThemeSwitcher = () => { + let btn = document.createElement('BUTTON'); + btn.className = 'theme-switcher'; + btn.id = 'themeSwitcher'; + btn.innerHTML = + ''; + document.body.appendChild(btn); + + if (localStorage.getItem('theme') === 'dark') $('#themeMoon').hide(0); + else $('#themeSun').hide(0); +}; + +$(document).ready(() => { + createThemeSwitcher(); + $('#themeSwitcher').click(switchTheme); + + $('footer').html( + $('footer').html() + + 'Dark theme provided by MrDogeBro.' + ); +}); + +const switchTheme = () => { + if (localStorage.getItem('theme') === 'dark') { + localStorage.setItem('theme', 'light'); + document.documentElement.setAttribute('data-theme', 'light'); + + $('#themeSun').fadeOut(200, () => { + $('#themeMoon').fadeIn(200); + }); + } else { + localStorage.setItem('theme', 'dark'); + document.documentElement.setAttribute('data-theme', 'dark'); + + $('#themeMoon').fadeOut(200, () => { + $('#themeSun').fadeIn(200); + }); + } +}; diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 00000000..d06a71d7 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install 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+} + +/* Math align to the left */ +.cell_output .MathJax_Display { + text-align: left !important; +} + +/* Pandas tables. 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translated ANSI escape sequences +Color values are copied from Jupyter Notebook +https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21 +Background colors from +https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors +*/ +div.highlight .-Color-Bold { + font-weight: bold; +} + +div.highlight .-Color[class*=-Black] { + color: #3E424D +} + +div.highlight .-Color[class*=-Red] { + color: #E75C58 +} + +div.highlight .-Color[class*=-Green] { + color: #00A250 +} + +div.highlight .-Color[class*=-Yellow] { + color: #DDB62B +} + +div.highlight .-Color[class*=-Blue] { + color: #208FFB +} + +div.highlight .-Color[class*=-Magenta] { + color: #D160C4 +} + +div.highlight .-Color[class*=-Cyan] { + color: #60C6C8 +} + +div.highlight .-Color[class*=-White] { + color: #C5C1B4 +} + +div.highlight .-Color[class*=-BGBlack] { + background-color: #3E424D +} + +div.highlight .-Color[class*=-BGRed] { + background-color: #E75C58 +} + +div.highlight .-Color[class*=-BGGreen] { + background-color: #00A250 +} + +div.highlight .-Color[class*=-BGYellow] { + background-color: #DDB62B +} + +div.highlight .-Color[class*=-BGBlue] { + background-color: #208FFB +} + +div.highlight .-Color[class*=-BGMagenta] { + background-color: #D160C4 +} + +div.highlight .-Color[class*=-BGCyan] { + background-color: #60C6C8 +} + +div.highlight .-Color[class*=-BGWhite] { + background-color: #C5C1B4 +} + +/* Font colors for 8-bit ANSI */ + +div.highlight .-Color[class*=-C0] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC0] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C1] { + color: #800000 +} + +div.highlight .-Color[class*=-BGC1] { + background-color: #800000 +} + +div.highlight .-Color[class*=-C2] { + color: #008000 +} + +div.highlight .-Color[class*=-BGC2] { + background-color: #008000 +} + +div.highlight .-Color[class*=-C3] { + color: #808000 +} + +div.highlight .-Color[class*=-BGC3] { + background-color: #808000 +} + +div.highlight .-Color[class*=-C4] { + color: #000080 +} + +div.highlight .-Color[class*=-BGC4] { + background-color: #000080 +} + +div.highlight .-Color[class*=-C5] { + color: #800080 +} + +div.highlight .-Color[class*=-BGC5] { + background-color: #800080 +} + +div.highlight .-Color[class*=-C6] { + color: #008080 +} + +div.highlight .-Color[class*=-BGC6] { + background-color: #008080 +} + +div.highlight .-Color[class*=-C7] { + color: #C0C0C0 +} + +div.highlight .-Color[class*=-BGC7] { + background-color: #C0C0C0 +} + +div.highlight .-Color[class*=-C8] { + color: #808080 +} + +div.highlight .-Color[class*=-BGC8] { + background-color: #808080 +} + +div.highlight .-Color[class*=-C9] { + color: #FF0000 +} + +div.highlight .-Color[class*=-BGC9] { + background-color: #FF0000 +} + +div.highlight .-Color[class*=-C10] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC10] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C11] { + color: #FFFF00 +} + +div.highlight .-Color[class*=-BGC11] { + background-color: #FFFF00 +} + +div.highlight .-Color[class*=-C12] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC12] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C13] { + color: #FF00FF +} + +div.highlight .-Color[class*=-BGC13] { + background-color: #FF00FF +} + +div.highlight .-Color[class*=-C14] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC14] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C15] { + color: #FFFFFF +} + +div.highlight .-Color[class*=-BGC15] { + background-color: #FFFFFF +} + +div.highlight .-Color[class*=-C16] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC16] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C17] { + color: #00005F +} + +div.highlight .-Color[class*=-BGC17] { + background-color: #00005F +} + +div.highlight .-Color[class*=-C18] { + color: #000087 +} + +div.highlight .-Color[class*=-BGC18] { + background-color: #000087 +} + +div.highlight .-Color[class*=-C19] { + color: #0000AF +} + +div.highlight .-Color[class*=-BGC19] { + background-color: #0000AF +} + +div.highlight .-Color[class*=-C20] { + color: #0000D7 +} + +div.highlight .-Color[class*=-BGC20] { + background-color: #0000D7 +} + +div.highlight .-Color[class*=-C21] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC21] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C22] { + color: #005F00 +} + +div.highlight .-Color[class*=-BGC22] { + background-color: #005F00 +} + +div.highlight .-Color[class*=-C23] { + color: #005F5F +} + +div.highlight .-Color[class*=-BGC23] { + background-color: #005F5F +} + +div.highlight .-Color[class*=-C24] { + color: #005F87 +} + +div.highlight .-Color[class*=-BGC24] { + background-color: #005F87 +} + +div.highlight .-Color[class*=-C25] { + color: #005FAF +} + +div.highlight .-Color[class*=-BGC25] { + background-color: #005FAF +} + +div.highlight .-Color[class*=-C26] { + color: #005FD7 +} + +div.highlight .-Color[class*=-BGC26] { + background-color: #005FD7 +} + +div.highlight .-Color[class*=-C27] { + color: #005FFF +} + +div.highlight .-Color[class*=-BGC27] { + background-color: #005FFF +} + +div.highlight .-Color[class*=-C28] { + color: #008700 +} + +div.highlight .-Color[class*=-BGC28] { + background-color: #008700 +} + +div.highlight .-Color[class*=-C29] { + color: #00875F +} + +div.highlight .-Color[class*=-BGC29] { + background-color: #00875F +} + +div.highlight .-Color[class*=-C30] { + color: #008787 +} + +div.highlight .-Color[class*=-BGC30] { + background-color: #008787 +} + +div.highlight .-Color[class*=-C31] { + color: #0087AF +} + +div.highlight .-Color[class*=-BGC31] { + background-color: #0087AF +} + +div.highlight .-Color[class*=-C32] { + color: #0087D7 +} + +div.highlight .-Color[class*=-BGC32] { + background-color: #0087D7 +} + +div.highlight .-Color[class*=-C33] { + color: #0087FF +} + +div.highlight .-Color[class*=-BGC33] { + background-color: #0087FF +} + +div.highlight .-Color[class*=-C34] { + color: #00AF00 +} + +div.highlight .-Color[class*=-BGC34] { + background-color: #00AF00 +} + +div.highlight .-Color[class*=-C35] { + color: #00AF5F +} + +div.highlight .-Color[class*=-BGC35] { + background-color: #00AF5F +} + +div.highlight .-Color[class*=-C36] { + color: #00AF87 +} + +div.highlight .-Color[class*=-BGC36] { + background-color: #00AF87 +} + +div.highlight .-Color[class*=-C37] { + color: #00AFAF +} + +div.highlight .-Color[class*=-BGC37] { + background-color: #00AFAF +} + +div.highlight .-Color[class*=-C38] { + color: #00AFD7 +} + +div.highlight .-Color[class*=-BGC38] { + background-color: #00AFD7 +} + +div.highlight .-Color[class*=-C39] { + color: #00AFFF +} + +div.highlight .-Color[class*=-BGC39] { + background-color: #00AFFF +} + +div.highlight .-Color[class*=-C40] { + color: #00D700 +} + +div.highlight .-Color[class*=-BGC40] { + background-color: #00D700 +} + +div.highlight .-Color[class*=-C41] { + color: #00D75F +} + +div.highlight .-Color[class*=-BGC41] { + background-color: #00D75F +} + +div.highlight .-Color[class*=-C42] { + color: #00D787 +} + +div.highlight .-Color[class*=-BGC42] { + background-color: #00D787 +} + +div.highlight .-Color[class*=-C43] { + color: #00D7AF +} + +div.highlight .-Color[class*=-BGC43] { + background-color: #00D7AF +} + +div.highlight .-Color[class*=-C44] { + color: #00D7D7 +} + +div.highlight .-Color[class*=-BGC44] { + background-color: #00D7D7 +} + +div.highlight .-Color[class*=-C45] { + color: #00D7FF +} + +div.highlight .-Color[class*=-BGC45] { + background-color: #00D7FF +} + +div.highlight .-Color[class*=-C46] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC46] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C47] { + color: #00FF5F +} + +div.highlight .-Color[class*=-BGC47] { + background-color: #00FF5F +} + +div.highlight .-Color[class*=-C48] { + color: #00FF87 +} + +div.highlight .-Color[class*=-BGC48] { + background-color: #00FF87 +} + +div.highlight .-Color[class*=-C49] { + color: #00FFAF +} + +div.highlight .-Color[class*=-BGC49] { + background-color: #00FFAF +} + +div.highlight .-Color[class*=-C50] { + color: #00FFD7 +} + +div.highlight .-Color[class*=-BGC50] { + background-color: #00FFD7 +} + +div.highlight .-Color[class*=-C51] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC51] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C52] { + color: #5F0000 +} + +div.highlight .-Color[class*=-BGC52] { + background-color: #5F0000 +} + +div.highlight .-Color[class*=-C53] { + color: #5F005F +} + +div.highlight .-Color[class*=-BGC53] { + background-color: #5F005F +} + +div.highlight .-Color[class*=-C54] { + color: #5F0087 +} + +div.highlight .-Color[class*=-BGC54] { + background-color: #5F0087 +} + +div.highlight .-Color[class*=-C55] { + color: #5F00AF +} + +div.highlight .-Color[class*=-BGC55] { + background-color: #5F00AF +} + +div.highlight .-Color[class*=-C56] { + color: #5F00D7 +} + +div.highlight .-Color[class*=-BGC56] { + background-color: #5F00D7 +} + +div.highlight .-Color[class*=-C57] { + color: #5F00FF +} + +div.highlight .-Color[class*=-BGC57] { + background-color: #5F00FF +} + +div.highlight .-Color[class*=-C58] { + color: #5F5F00 +} + +div.highlight .-Color[class*=-BGC58] { + background-color: #5F5F00 +} + +div.highlight .-Color[class*=-C59] { + color: #5F5F5F +} + +div.highlight .-Color[class*=-BGC59] { + background-color: #5F5F5F +} + +div.highlight .-Color[class*=-C60] { + color: #5F5F87 +} + +div.highlight .-Color[class*=-BGC60] { + background-color: #5F5F87 +} + +div.highlight .-Color[class*=-C61] { + color: #5F5FAF +} + +div.highlight .-Color[class*=-BGC61] { + background-color: #5F5FAF +} + +div.highlight .-Color[class*=-C62] { + color: #5F5FD7 +} + +div.highlight .-Color[class*=-BGC62] { + background-color: #5F5FD7 +} + +div.highlight .-Color[class*=-C63] { + color: #5F5FFF +} + +div.highlight .-Color[class*=-BGC63] { + background-color: #5F5FFF +} + +div.highlight .-Color[class*=-C64] { + color: #5F8700 +} + +div.highlight .-Color[class*=-BGC64] { + background-color: #5F8700 +} + +div.highlight .-Color[class*=-C65] { + color: #5F875F +} + +div.highlight .-Color[class*=-BGC65] { + background-color: #5F875F +} + +div.highlight .-Color[class*=-C66] { + color: #5F8787 +} + +div.highlight .-Color[class*=-BGC66] { + background-color: #5F8787 +} + +div.highlight .-Color[class*=-C67] { + color: #5F87AF +} + +div.highlight .-Color[class*=-BGC67] { + background-color: #5F87AF +} + +div.highlight .-Color[class*=-C68] { + color: #5F87D7 +} + +div.highlight .-Color[class*=-BGC68] { + background-color: #5F87D7 +} + +div.highlight .-Color[class*=-C69] { + color: #5F87FF +} + +div.highlight .-Color[class*=-BGC69] { + background-color: #5F87FF +} + +div.highlight .-Color[class*=-C70] { + color: #5FAF00 +} + +div.highlight .-Color[class*=-BGC70] { + background-color: #5FAF00 +} + +div.highlight .-Color[class*=-C71] { + color: #5FAF5F +} + +div.highlight .-Color[class*=-BGC71] { + background-color: #5FAF5F +} + +div.highlight .-Color[class*=-C72] { + color: #5FAF87 +} + +div.highlight .-Color[class*=-BGC72] { + background-color: #5FAF87 +} + +div.highlight .-Color[class*=-C73] { + color: #5FAFAF +} + +div.highlight .-Color[class*=-BGC73] { + background-color: #5FAFAF +} + +div.highlight .-Color[class*=-C74] { + color: #5FAFD7 +} + +div.highlight .-Color[class*=-BGC74] { + background-color: #5FAFD7 +} + +div.highlight .-Color[class*=-C75] { + color: #5FAFFF +} + +div.highlight .-Color[class*=-BGC75] { + background-color: #5FAFFF +} + +div.highlight .-Color[class*=-C76] { + color: #5FD700 +} + +div.highlight .-Color[class*=-BGC76] { + background-color: #5FD700 +} + +div.highlight .-Color[class*=-C77] { + color: #5FD75F +} + +div.highlight .-Color[class*=-BGC77] { + background-color: #5FD75F +} + +div.highlight .-Color[class*=-C78] { + color: #5FD787 +} + +div.highlight .-Color[class*=-BGC78] { + background-color: #5FD787 +} + +div.highlight .-Color[class*=-C79] { + color: #5FD7AF +} + +div.highlight .-Color[class*=-BGC79] { + background-color: #5FD7AF +} + +div.highlight .-Color[class*=-C80] { + color: #5FD7D7 +} + +div.highlight .-Color[class*=-BGC80] { + background-color: #5FD7D7 +} + +div.highlight .-Color[class*=-C81] { + color: #5FD7FF +} + +div.highlight .-Color[class*=-BGC81] { + background-color: #5FD7FF +} + +div.highlight .-Color[class*=-C82] { + color: #5FFF00 +} + +div.highlight .-Color[class*=-BGC82] { + background-color: #5FFF00 +} + +div.highlight .-Color[class*=-C83] { + color: #5FFF5F +} + +div.highlight .-Color[class*=-BGC83] { + background-color: #5FFF5F +} + +div.highlight .-Color[class*=-C84] { + color: #5FFF87 +} + +div.highlight .-Color[class*=-BGC84] { + background-color: #5FFF87 +} + +div.highlight .-Color[class*=-C85] { + color: #5FFFAF +} + +div.highlight .-Color[class*=-BGC85] { + background-color: #5FFFAF +} + +div.highlight .-Color[class*=-C86] { + color: #5FFFD7 +} + +div.highlight .-Color[class*=-BGC86] { + background-color: #5FFFD7 +} + +div.highlight .-Color[class*=-C87] { + color: #5FFFFF +} + +div.highlight .-Color[class*=-BGC87] { + background-color: #5FFFFF +} + +div.highlight .-Color[class*=-C88] { + color: #870000 +} + +div.highlight .-Color[class*=-BGC88] { + background-color: #870000 +} + +div.highlight .-Color[class*=-C89] { + color: #87005F +} + +div.highlight .-Color[class*=-BGC89] { + background-color: #87005F +} + +div.highlight .-Color[class*=-C90] { + color: #870087 +} + +div.highlight .-Color[class*=-BGC90] { + background-color: #870087 +} + +div.highlight .-Color[class*=-C91] { + color: #8700AF +} + +div.highlight .-Color[class*=-BGC91] { + background-color: #8700AF +} + +div.highlight .-Color[class*=-C92] { + color: #8700D7 +} + +div.highlight .-Color[class*=-BGC92] { + background-color: #8700D7 +} + +div.highlight .-Color[class*=-C93] { + color: #8700FF +} + +div.highlight .-Color[class*=-BGC93] { + background-color: #8700FF +} + +div.highlight .-Color[class*=-C94] { + color: #875F00 +} + +div.highlight .-Color[class*=-BGC94] { + background-color: #875F00 +} + +div.highlight .-Color[class*=-C95] { + color: #875F5F +} + +div.highlight .-Color[class*=-BGC95] { + background-color: #875F5F +} + +div.highlight .-Color[class*=-C96] { + color: #875F87 +} + +div.highlight .-Color[class*=-BGC96] { + background-color: #875F87 +} + +div.highlight .-Color[class*=-C97] { + color: #875FAF +} + +div.highlight .-Color[class*=-BGC97] { + background-color: #875FAF +} + +div.highlight .-Color[class*=-C98] { + color: #875FD7 +} + +div.highlight .-Color[class*=-BGC98] { + background-color: #875FD7 +} + +div.highlight .-Color[class*=-C99] { + color: #875FFF +} + +div.highlight .-Color[class*=-BGC99] { + background-color: #875FFF +} + +div.highlight .-Color[class*=-C100] { + color: #878700 +} + +div.highlight .-Color[class*=-BGC100] { + background-color: #878700 +} + +div.highlight .-Color[class*=-C101] { + color: #87875F +} + +div.highlight .-Color[class*=-BGC101] { + background-color: #87875F +} + +div.highlight .-Color[class*=-C102] { + color: #878787 +} + +div.highlight .-Color[class*=-BGC102] { + background-color: #878787 +} + +div.highlight .-Color[class*=-C103] { + color: #8787AF +} + +div.highlight .-Color[class*=-BGC103] { + background-color: #8787AF +} + +div.highlight .-Color[class*=-C104] { + color: #8787D7 +} + +div.highlight .-Color[class*=-BGC104] { + background-color: #8787D7 +} + +div.highlight .-Color[class*=-C105] { + color: #8787FF +} + +div.highlight .-Color[class*=-BGC105] { + background-color: #8787FF +} + +div.highlight .-Color[class*=-C106] { + color: #87AF00 +} + +div.highlight .-Color[class*=-BGC106] { + background-color: #87AF00 +} + +div.highlight .-Color[class*=-C107] { + color: #87AF5F +} + +div.highlight .-Color[class*=-BGC107] { + background-color: #87AF5F +} + +div.highlight .-Color[class*=-C108] { + color: #87AF87 +} + +div.highlight .-Color[class*=-BGC108] { + background-color: #87AF87 +} + +div.highlight .-Color[class*=-C109] { + color: #87AFAF +} + +div.highlight .-Color[class*=-BGC109] { + background-color: #87AFAF +} + +div.highlight .-Color[class*=-C110] { + color: #87AFD7 +} + +div.highlight .-Color[class*=-BGC110] { + background-color: #87AFD7 +} + +div.highlight .-Color[class*=-C111] { + color: #87AFFF +} + +div.highlight .-Color[class*=-BGC111] { + background-color: #87AFFF +} + +div.highlight .-Color[class*=-C112] { + color: #87D700 +} + +div.highlight .-Color[class*=-BGC112] { + background-color: #87D700 +} + +div.highlight .-Color[class*=-C113] { + color: #87D75F +} + +div.highlight .-Color[class*=-BGC113] { + background-color: #87D75F +} + +div.highlight .-Color[class*=-C114] { + color: #87D787 +} + +div.highlight .-Color[class*=-BGC114] { + background-color: #87D787 +} + +div.highlight .-Color[class*=-C115] { + color: #87D7AF +} + +div.highlight .-Color[class*=-BGC115] { + background-color: #87D7AF +} + +div.highlight .-Color[class*=-C116] { + color: #87D7D7 +} + +div.highlight .-Color[class*=-BGC116] { + background-color: #87D7D7 +} + +div.highlight .-Color[class*=-C117] { + color: #87D7FF +} + +div.highlight .-Color[class*=-BGC117] { + background-color: #87D7FF +} + +div.highlight .-Color[class*=-C118] { + color: #87FF00 +} + +div.highlight .-Color[class*=-BGC118] { + background-color: #87FF00 +} + +div.highlight .-Color[class*=-C119] { + color: #87FF5F +} + +div.highlight .-Color[class*=-BGC119] { + background-color: #87FF5F +} + +div.highlight .-Color[class*=-C120] { + color: #87FF87 +} + +div.highlight .-Color[class*=-BGC120] { + background-color: #87FF87 +} + +div.highlight .-Color[class*=-C121] { + color: #87FFAF +} + +div.highlight .-Color[class*=-BGC121] { + background-color: #87FFAF +} + +div.highlight .-Color[class*=-C122] { + color: #87FFD7 +} + +div.highlight .-Color[class*=-BGC122] { + background-color: #87FFD7 +} + +div.highlight .-Color[class*=-C123] { + color: #87FFFF +} + +div.highlight .-Color[class*=-BGC123] { + background-color: #87FFFF +} + +div.highlight .-Color[class*=-C124] { + color: #AF0000 +} + +div.highlight .-Color[class*=-BGC124] { + background-color: #AF0000 +} + +div.highlight .-Color[class*=-C125] { + color: #AF005F +} + +div.highlight .-Color[class*=-BGC125] { + background-color: #AF005F +} + +div.highlight .-Color[class*=-C126] { + color: #AF0087 +} + +div.highlight .-Color[class*=-BGC126] { + background-color: #AF0087 +} + +div.highlight .-Color[class*=-C127] { + color: #AF00AF +} + +div.highlight .-Color[class*=-BGC127] { + background-color: #AF00AF +} + +div.highlight .-Color[class*=-C128] { + color: #AF00D7 +} + +div.highlight .-Color[class*=-BGC128] { + background-color: #AF00D7 +} + +div.highlight .-Color[class*=-C129] { + color: #AF00FF +} + +div.highlight .-Color[class*=-BGC129] { + background-color: #AF00FF +} + +div.highlight .-Color[class*=-C130] { + color: #AF5F00 +} + 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background-color: #FFD7FF +} + +div.highlight .-Color[class*=-C226] { + color: #FFFF00 +} + +div.highlight .-Color[class*=-BGC226] { + background-color: #FFFF00 +} + +div.highlight .-Color[class*=-C227] { + color: #FFFF5F +} + +div.highlight .-Color[class*=-BGC227] { + background-color: #FFFF5F +} + +div.highlight .-Color[class*=-C228] { + color: #FFFF87 +} + +div.highlight .-Color[class*=-BGC228] { + background-color: #FFFF87 +} + +div.highlight .-Color[class*=-C229] { + color: #FFFFAF +} + +div.highlight .-Color[class*=-BGC229] { + background-color: #FFFFAF +} + +div.highlight .-Color[class*=-C230] { + color: #FFFFD7 +} + +div.highlight .-Color[class*=-BGC230] { + background-color: #FFFFD7 +} + +div.highlight .-Color[class*=-C231] { + color: #FFFFFF +} + +div.highlight .-Color[class*=-BGC231] { + background-color: #FFFFFF +} + +div.highlight .-Color[class*=-C232] { + color: #080808 +} + +div.highlight .-Color[class*=-BGC232] { + background-color: #080808 +} + +div.highlight 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Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/algorithms.html b/algorithms.html new file mode 100644 index 00000000..6f2319fc --- /dev/null +++ b/algorithms.html @@ -0,0 +1,147 @@ + + + + + + + Algorithms — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Algorithms

+

Qlasskit implements high level representation of quantum algorithms that relies on black boxes functions and oracles.

+ +
+

Simon periodicity

+
+
+

Deutsch Jozsa

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/api.html b/api.html new file mode 100644 index 00000000..a1bb7a79 --- /dev/null +++ b/api.html @@ -0,0 +1,170 @@ + + + + + + + API — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

API

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

qlasskit.qlassfun.qlassf(f[, types, defs, ...])

Decorator / function creating a QlassF object

qlasskit.qlassfun.qlassfa([types, defs, ...])

Decorator with parameters for qlassf

qlasskit.qlassfun.QlassF(name, original_f, ...)

Class representing a qlassf function

qlasskit.algorithms.qalgorithm

qlasskit.algorithms.grover.Grover(oracle[, ...])

qlasskit.qcircuit.qcircuit.QCircuit([...])

qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper()

Wrapper interface for a class containing a qcircuit

qlasskit.qcircuit.gates

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_big_circuit.html b/example_big_circuit.html new file mode 100644 index 00000000..5ca29b23 --- /dev/null +++ b/example_big_circuit.html @@ -0,0 +1,158 @@ + + + + + + + Working with big circuits — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Working with big circuits

+

Qlasskit is capable of producing large circuit without any issue. The only thing that you have to do, is to use the fastOptimizer, since running CSE is too slow on large expressions lists.

+

In the next example we are going to create a quantum circuit with 64 Qint8 in input, and one Qint8 in output, resulting on a circuit of ~21984 qubits and ~1044 gates in around 5 seconds.

+
+
+
from qlasskit import Qint8, Qlist, boolopt, qlassfa
+
+@qlassfa(bool_optimizer=boolopt.fastOptimizer)
+def test(a_list: Qlist[Qint8, 64]) -> Qint8:
+    h_val = Qint8(0)
+    for c in a_list:
+        h_val = h_val + c
+    return h_val
+
+
+
+
+
+
+
print(test.circuit())
+
+
+
+
+
QCircuit<test>(21984 gates, 1044 qubits)
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_deutsch_jozsa.html b/example_deutsch_jozsa.html new file mode 100644 index 00000000..0f02188f --- /dev/null +++ b/example_deutsch_jozsa.html @@ -0,0 +1,198 @@ + + + + + + + Deutsch Jozsa algorithm — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Deutsch Jozsa algorithm

+
+
+
from qlasskit import qlassf, Qint4
+
+
+@qlassf
+def f(a: Qint4) -> bool:
+    return a > 7
+
+
+
+
+
+
+
f.export("qiskit").draw("mpl")
+
+
+
+
+
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to "iqp" in a following release. To silence this warning, specify the current default explicitly as style="clifford", or the new default as style="iqp".
+  self._style, def_font_ratio = load_style(self._style)
+
+
+_images/1be99e37f13150ae81db33daa1ab2beb28fb6ea6b341a213e05f0f61f52f1d0c.png +
+
+
+
+
from qlasskit.algorithms import DeutschJozsa
+
+q_algo = DeutschJozsa(f)
+
+
+
+
+
+
+
qc = q_algo.export("qiskit")
+qc.draw("mpl")
+
+
+
+
+_images/5174bb0c6ea65db88c99511f9658fc0a3c9afceb5058f8a187fa4d462337954a.png +
+
+
+
+
from qiskit import Aer, QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+
+qc.measure_all()
+simulator = Aer.get_backend("aer_simulator")
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts)
+plot_histogram(counts_readable)
+
+
+
+
+_images/f3b3957c4548aaf8d56ce1f0541c259dddd1458d1a1e74443a16ae7a87e145f7.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover.html b/example_grover.html new file mode 100644 index 00000000..6c1a8ef5 --- /dev/null +++ b/example_grover.html @@ -0,0 +1,208 @@ + + + + + + + Grover search — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover_factors.html b/example_grover_factors.html new file mode 100644 index 00000000..2f0f5125 --- /dev/null +++ b/example_grover_factors.html @@ -0,0 +1,179 @@ + + + + + + + Grover: factorize number — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Grover: factorize number

+
+
+
from typing import Tuple
+from qlasskit import qlassf, Qint2
+from qlasskit.algorithms import Grover
+
+
+@qlassf
+def factorize(a: Tuple[Qint2, Qint2]) -> bool:
+    return a[0] * a[1] == 9
+
+
+q_algo = Grover(factorize)
+
+
+
+
+
+
+
from qiskit import Aer, QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+
+qc = q_algo.export("qiskit")
+qc.measure_all()
+simulator = Aer.get_backend("aer_simulator")
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts, discard_lower=5)
+plot_histogram(counts_readable)
+
+
+
+
+_images/5180fed86778dda3aef0affffed4a07321f62d24284f239022a14036426635b7.png +
+
+
+
+
qc.draw("mpl")
+
+
+
+
+
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to "iqp" in a following release. To silence this warning, specify the current default explicitly as style="clifford", or the new default as style="iqp".
+  self._style, def_font_ratio = load_style(self._style)
+
+
+_images/fc1d93821ee269bc89af7272200aae50aed37301baea1590dd42a82a9a297029.png +
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Davide Gessa (dakk).

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover_hash.html b/example_grover_hash.html new file mode 100644 index 00000000..2aaea456 --- /dev/null +++ b/example_grover_hash.html @@ -0,0 +1,229 @@ + + + + + + + Grover search: hash function preimage attack — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Grover search: hash function preimage attack

+

In the ever-evolving landscape of cybersecurity, the advent of quantum computing presents both an extraordinary opportunity and an unprecedented challenge. In this notebook we exploit a Grover Search to perform a preimage attack on a toy hash function.

+

A preimage attack on a cryptographic hash function h(m) tries to find a message m that has a specific hash value. Using qlasskit it is easy to write an hash function like the following hash_simp:

+
+
+
from qlasskit import qlassf, Qint4, Qint8, Qlist
+
+
+@qlassf
+def hash_simp(m: Qlist[Qint4, 2]) -> Qint8:
+    hv = 0
+    for i in m:
+        hv = ((hv << 4) ^ (hv >> 1) ^ i) & 0xFF
+
+    return hv
+
+
+
+
+

Thanks to the fact that qlasskit function are standard python functions, we can call the original_f to perform some kind of analysis on the hash function. Since the input space is tiny (it is a toy hash function), we can detect if the hash function is uniform (if it maps equally to the output space).

+
+
+
from collections import Counter
+
+d = Counter(
+    hex(hash_simp.original_f((x, y))) for x in range(2**4) for y in range(2**4)
+)
+
+print("Hash function output space:", len(d))
+
+
+
+
+
Hash function output space: 256
+
+
+
+
+

We got that hash_simp is following an uniform distribution.

+
+
+
hash_simp.export("qiskit").draw("mpl")
+
+
+
+
+
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to "iqp" in a following release. To silence this warning, specify the current default explicitly as style="clifford", or the new default as style="iqp".
+  self._style, def_font_ratio = load_style(self._style)
+
+
+_images/c7ef1f01b80363cad58e444c34d85d42122df00d7131ad1f1fec37e2b481452c.png +
+
+

Now we use our quantum function as an oracle for a Grover search, in order to find which input maps to the value 0xca.

+
+
+
from qlasskit.algorithms import Grover
+
+q_algo = Grover(hash_simp, Qint8(0xCA))
+
+
+
+
+

Then we use our prefered framework and simulator for sampling the result; this is an example using qiskit with aer_simulator.

+
+
+
from qiskit import Aer, QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+
+qc = q_algo.export("qiskit")
+qc.measure_all()
+simulator = Aer.get_backend("aer_simulator")
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts, discard_lower=5)
+plot_histogram(counts_readable)
+
+
+
+
+_images/54dbe21e590e5ff31a74aa41c1e7db7e8b05a5eea8718ec877d21d82a5b931ae.png +
+
+

Using QlassF.original_f we can double check the result without invoking a quantum simulator; calling it with the tuple (12,12) must result in the hash value 0xca.

+
+
+
print(hex(hash_simp.original_f((12, 12))))
+
+
+
+
+
0xca
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover_subset.html b/example_grover_subset.html new file mode 100644 index 00000000..d334119f --- /dev/null +++ b/example_grover_subset.html @@ -0,0 +1,180 @@ + + + + + + + Grover search: subset problem — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Grover search: subset problem

+

We define a function named subset_sum(i,j) that returns the sum of the elements i and j of a list l. We want to use a Grover search to find which i j combination led to a given value.

+
+
+
from qlasskit import qlassf, Qint2, Qint3
+from typing import Tuple
+
+
+@qlassf
+def subset_sum(ii: Tuple[Qint2, Qint2]) -> Qint3:
+    l = [0, 5, 2, 3]
+    return l[ii[0]] + l[ii[1]] if ii[0] != ii[1] else 0
+
+
+
+
+

Our quantum function subset_sum will be used as an oracle for a Grover search. For instance, here we want to find the input value that produce the value 7. Since we know that there are at least two result ((i,j) and (j,i)), we set n_matching=2.

+
+
+
from qlasskit.algorithms import Grover
+
+q_algo = Grover(subset_sum, Qint3(7), n_matching=2)
+
+
+
+
+

Then we use our prefered framework and simulator for sampling the result; this is an example using qiskit with aer_simulator.

+

In the output histogram, it’s now evident that the input leading to a value of 7 are the tuples (1,2) and (2,1) (5+2 and 2+5), aligning with our expectations.

+
+
+
from qiskit import Aer, QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+
+qc = q_algo.export("qiskit")
+qc.measure_all()
+simulator = Aer.get_backend("aer_simulator")
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts)
+plot_histogram(counts_readable)
+
+
+
+
+_images/274e5b7a04d1b45c692c85a2df1435fc8fead62305c8707868952747e0ca9a74.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_grover_sudoku.html b/example_grover_sudoku.html new file mode 100644 index 00000000..d25e30a7 --- /dev/null +++ b/example_grover_sudoku.html @@ -0,0 +1,208 @@ + + + + + + + Grover search: sudoku solver — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Grover search: sudoku solver

+

In this example we are going to solve a sudoku puzzle. Since we have few qubits, we cannot solve a real 9x9 sudoku puzzle; our toy examples uses a 2x2 matrix where a valid solution is when in every row and every column there are no repeated values (0 or 1). We encode these xor-ing the values for each row and column. +Since we want a specific solution, we add a constraint constr: we want the [0][0] element to be True.

+

sudoku_check is already an oracle so this time we instantiate the Grover algorithm without value.

+
+
+
from qlasskit import qlassf, Qmatrix
+from qlasskit.algorithms import Grover
+
+@qlassf
+def sudoku_check(m: Qmatrix[bool, 2, 2]) -> bool:
+    constr = m[0][0]
+    sub0 = m[0][0] ^ m[0][1]
+    sub1 = m[1][0] ^ m[1][1]
+    sub2 = m[0][0] ^ m[1][0]
+    sub3 = m[0][1] ^ m[1][1]
+    return sub0 and sub1 and sub2 and sub3 and constr
+
+q_algo = Grover(sudoku_check)
+
+
+
+
+

Then we use our prefered framework and simulator for sampling the result; this is an example using qiskit with aer_simulator.

+

We obtain that the solution for this puzzle is the matrix [[True, False], [False, True]].

+
+
+
from qiskit import Aer, QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+
+qc = q_algo.export("qiskit")
+qc.measure_all()
+simulator = Aer.get_backend("aer_simulator")
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts, discard_lower=20)
+plot_histogram(counts_readable)
+
+
+
+
+_images/2915c0b805d9c8511df8953092a6bcabff47bf8837755424101f63b16dfa26c7.png +
+
+

We can create a more realistic sudoku game using numbers instead of booleans, but the resources required will scale exponentially. In the following code snippets, we recreate sudoku_check using Qint2 and a 4x4 matrix. The sum of each column and row must be equal to 6 (3+2+1+0). As we can see, the resulting circuit of the checker requires more than 100 qubits, way above our simulation capabilities.

+
+
+
from qlasskit import Qint2, Qint4
+
+@qlassf
+def sudoku_check(m: Qmatrix[Qint2, 4, 4]) -> bool:
+    res = True
+    
+    # Constraints
+    res = (m[0][2] == 3) and (m[0][0] == 1)
+    
+    # Check every row and column
+    for i in range(4):
+        c = (Qint4(0) + m[i][0] + m[i][1] + m[i][2] + m[i][3]) == 6 
+        r = (Qint4(0) + m[0][i] + m[1][i] + m[2][i] + m[3][i]) == 6 
+        res = res and c and r
+        
+    return res
+
+#q_algo = Grover(sudoku_check)
+print(sudoku_check.circuit())
+
+
+
+
+
QCircuit<sudoku_check>(1309 gates, 178 qubits)
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_simon.html b/example_simon.html new file mode 100644 index 00000000..3d3f2c5b --- /dev/null +++ b/example_simon.html @@ -0,0 +1,198 @@ + + + + + + + Simon function periodicity — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Simon function periodicity

+
+
+
from qlasskit import qlassf, Qint4
+
+
+@qlassf
+def f(a: Qint4) -> Qint4:
+    return (a >> 3) + 1
+
+
+
+
+
+
+
f.export("qiskit").draw("mpl")
+
+
+
+
+
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to "iqp" in a following release. To silence this warning, specify the current default explicitly as style="clifford", or the new default as style="iqp".
+  self._style, def_font_ratio = load_style(self._style)
+
+
+_images/8e1bf07bbbab0c0a595d9144710a8af6c22cf9a2661a42ccd01e899e85ffb12e.png +
+
+
+
+
from qlasskit.algorithms import Simon
+
+q_algo = Simon(f)
+
+
+
+
+
+
+
qc = q_algo.export("qiskit")
+qc.draw("mpl")
+
+
+
+
+_images/18da646c905ecbbabade2933b32d33141624835efe4bc3d00deb9641993228fc.png +
+
+
+
+
from qiskit import Aer, QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+
+qc.measure_all()
+simulator = Aer.get_backend("aer_simulator")
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = q_algo.decode_counts(counts)
+plot_histogram(counts_readable)
+
+
+
+
+_images/d5eecd09f0811026c2add991efdd35dd7e04fdefb326b0414c542dd0e32b203a.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/example_unitary_of_f.html b/example_unitary_of_f.html new file mode 100644 index 00000000..25acb628 --- /dev/null +++ b/example_unitary_of_f.html @@ -0,0 +1,189 @@ + + + + + + + Unitary of qlasskit function — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Unitary of qlasskit function

+

In qlasskit, we can exploit external low-level frameworks to perform operations on the resulting quantum circuit. In this example, we use qiskit in order to obtain the unitary matrix of our QlassF function.

+
+
+
from qiskit import QuantumCircuit, transpile, execute
+from qiskit_aer import AerSimulator
+from qiskit.visualization import array_to_latex
+from qlasskit import qlassf
+
+
+@qlassf
+def f(a: bool, b: bool) -> bool:
+    return a ^ (not b)
+
+
+print(f"\n{f}\n")
+
+qc = QuantumCircuit(f.num_qubits, f.num_qubits)
+qc.append(f.gate(), f.qubits)
+
+qc.save_state()
+qc.decompose().draw("mpl")
+
+
+
+
+
QlassF<f>(a:bool, b:bool) -> bool:
+	_ret = a ^ ~b
+
+
+_images/37afe42937c3ad9b42eae8b9bb2821891d10c2c845dd4d77462ec4486194799a.png +
+
+
+
+
simulator = AerSimulator(method="unitary")
+job = execute(qc, simulator, shots=8192)
+result = job.result()
+array_to_latex(result.get_unitary(qc, 3), max_size=16)
+
+
+
+
+
+\[\begin{split}\begin{bmatrix} +0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ + 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ + 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ + 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\ + 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ + \end{bmatrix} +\end{split}\]
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/exporter.html b/exporter.html new file mode 100644 index 00000000..315e9c62 --- /dev/null +++ b/exporter.html @@ -0,0 +1,250 @@ + + + + + + + Exporting to other frameworks — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Exporting to other frameworks

+

Qlasskit implements circuit / gate exporters for Qiskit, Cirq, Qasm, Sympy and Pennylane.

+
+
+
from qlasskit import Qint2, qlassf
+
+
+@qlassf
+def hello_world(a: bool, b: Qint2) -> Qint2:
+    return b + (1 if a else 0)
+
+
+
+
+
+

Qiskit

+
+
+
qc = hello_world.export("qiskit")
+qc.draw("mpl")
+
+
+
+
+
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to "iqp" in a following release. To silence this warning, specify the current default explicitly as style="clifford", or the new default as style="iqp".
+  self._style, def_font_ratio = load_style(self._style)
+
+
+_images/be3abba3958dd65682b17997972df3f803e8698846e140e95797a368e23e2236.png +
+
+
+
+

QASM

+
+
+
qc = hello_world.export("qasm")
+print(qc)
+
+
+
+
+
OPENQASM 3.0;
+
+gate hello_world a b.0 b.1 _ret.0 _ret.1 {
+	cx a _ret.0
+	cx b.0 _ret.0
+	cx b.1 _ret.1
+	ccx a b.0 _ret.1
+}
+
+
+
+
+
+
+

Cirq

+
+
+
import cirq
+
+qc = hello_world.export("cirq")
+qc
+
+
+
+
+
0: ───hello_world───
+      │
+1: ───hello_world───
+      │
+2: ───hello_world───
+      │
+3: ───hello_world───
+      │
+4: ───hello_world───
+
+
+
+

Pennylane

+
+
+
import pennylane as qml
+
+tape = hello_world.export("pennylane")
+tape
+
+
+
+
+
<QuantumTape: wires=[0, 3, 1, 2, 4], params=0>
+
+
+
+
+
+
+

Sympy

+
+
+
qc = hello_world.export("sympy")
+qc
+
+
+
+
+
+\[\displaystyle C_{0,1}{\left(X_{4}\right)} \text{CNOT}_{2,4} \text{CNOT}_{1,3} \text{CNOT}_{0,3} {\left|00000\right\rangle }\]
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.algorithms.grover.Grover.html b/generated/qlasskit.algorithms.grover.Grover.html new file mode 100644 index 00000000..949346cd --- /dev/null +++ b/generated/qlasskit.algorithms.grover.Grover.html @@ -0,0 +1,228 @@ + + + + + + + qlasskit.algorithms.grover.Grover — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.algorithms.grover.Grover

+
+
+class qlasskit.algorithms.grover.Grover(oracle: QlassF, element_to_search: Qtype | None = None, n_iterations: int | None = None, n_matching: int = 1)
+
+
+__init__(oracle: QlassF, element_to_search: Qtype | None = None, n_iterations: int | None = None, n_matching: int = 1)
+
+
Parameters:
+
    +

  • oracle (QlassF) – our f(x) -> bool that returns True if x satisfies the function or +a generic function f(x) = y that we want to compare with element_to_search

    • +

  • element_to_search (Qtype, optional) – the element we want to search

    • +

  • n_iterations (int, optional) – force a number of iterations +(otherwise, pi/4*sqrt(N/n_matching))

    • +

  • n_matching (int) – the number of expected matching values (default: 1)

    • +
+
+
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__(oracle[, element_to_search, ...])

+
param oracle:
+

our f(x) -> bool that returns True if x satisfies the function or

+
+
+

circuit()

decode_counts(counts[, discard_lower])

Decode data from a circuit counts dict

decode_output(istr)

draw()

encode_input(*qvals)

export([framework])

Export the circuit to a supported framework

gate([framework])

Returns the gate for a specific framework

+

Attributes

+ + + + + + + + + + + + + + + + + + + + + +

input_qubits

Returns the list of input qubits

input_size

num_qubits

output_qubits

Returns the list of output qubits

output_size

qubits

Returns all the qubits of the circuit

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.algorithms.qalgorithm.html b/generated/qlasskit.algorithms.qalgorithm.html new file mode 100644 index 00000000..20c6cd1a --- /dev/null +++ b/generated/qlasskit.algorithms.qalgorithm.html @@ -0,0 +1,167 @@ + + + + + + + qlasskit.algorithms.qalgorithm — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.algorithms.qalgorithm

+

Functions

+ + + + + + +

oraclize(qf, element[, name])

Transform a QlassF qf and an element to an oracle {f(x) = x == element}

+

Classes

+ + + + + + +

QAlgorithm()

+

Exceptions

+ + + + + + +

ConstantOracleException

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qcircuit.gates.html b/generated/qlasskit.qcircuit.gates.html new file mode 100644 index 00000000..643c443d --- /dev/null +++ b/generated/qlasskit.qcircuit.gates.html @@ -0,0 +1,213 @@ + + + + + + + qlasskit.qcircuit.gates — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qcircuit.gates

+

Functions

+ + + + + + +

apply(gate, qubits[, param])

+

Classes

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Barrier()

CCX()

CP()

CX()

H()

I()

MCX(n_controls)

MCtrl(gate, n_controls)

NopGate([name])

P()

QControlledGate(gate, n_controls)

QGate(name[, n_qubits])

S()

Swap()

T()

Toffoli

alias of CCX

X()

Y()

Z()

+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qcircuit.qcircuit.QCircuit.html b/generated/qlasskit.qcircuit.qcircuit.QCircuit.html new file mode 100644 index 00000000..9f540752 --- /dev/null +++ b/generated/qlasskit.qcircuit.qcircuit.QCircuit.html @@ -0,0 +1,256 @@ + + + + + + + qlasskit.qcircuit.qcircuit.QCircuit — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qcircuit.qcircuit.QCircuit

+
+
+class qlasskit.qcircuit.qcircuit.QCircuit(num_qubits=0, name='qc', native=None)
+
+
+__init__(num_qubits=0, name='qc', native=None)
+

Initialize a quantum circuit.

+
+
Parameters:
+

num_qubits (int, optional) – The number of qubits in the circuit. Defaults to 0.

+
+
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__([num_qubits, name, native])

Initialize a quantum circuit.

add_qubit([name])

Add a qubit to the circuit.

append(gate, qubits[, param])

Append a gate operation to the circuit.

append_circuit(other[, qubits])

Add a qcircuit, remapping to qubits

barrier([label])

Add a barrier to the circuit

ccx(w1, w2, w3)

CCX gate

copy()

cp(phase, w1, w2)

CP gate

cx(w1, w2)

CX gate

draw()

Draw the circuit

export([mode, framework])

Exports the circuit to another framework.

get_key_by_index(i)

Return the qubit name given its index

h(w)

H gate

iqft(wl)

Apply the inverse quantum fourier transform

mctrl(g, wl, target[, param])

Multi controlled gate

mcx(wl, target)

Multi CX gate

qft(wl)

Apply the quantum fourier transform

random(qubits_n, depth[, gate_list])

repeat(n)

Return a copy of the QCircuit repeated n times

s(w)

S gate

swap(w1, w2)

t(w)

T gate

x(w)

X gate

y(w)

Y gate

z(w)

Z gate

+

Attributes

+ + + + + + + + + +

num_gates

used_qubits

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.html b/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.html new file mode 100644 index 00000000..0e2236b3 --- /dev/null +++ b/generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.html @@ -0,0 +1,212 @@ + + + + + + + qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper

+
+
+class qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper
+

Wrapper interface for a class containing a qcircuit

+
+
+__init__()
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__()

circuit()

decode_counts(counts[, discard_lower])

Decode data from a circuit counts dict

decode_output(istr)

draw()

encode_input(*qvals)

export([framework])

Export the circuit to a supported framework

gate([framework])

Returns the gate for a specific framework

+

Attributes

+ + + + + + + + + + + + + + + + + + + + + +

input_qubits

Returns the list of input qubits

input_size

num_qubits

output_qubits

Returns the list of output qubits

output_size

qubits

Returns all the qubits of the circuit

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qlassfun.QlassF.html b/generated/qlasskit.qlassfun.QlassF.html new file mode 100644 index 00000000..53c83812 --- /dev/null +++ b/generated/qlasskit.qlassfun.QlassF.html @@ -0,0 +1,248 @@ + + + + + + + qlasskit.qlassfun.QlassF — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qlassfun.QlassF

+
+
+class qlasskit.qlassfun.QlassF(name: str, original_f: Callable, args: List[Arg], returns: Arg, exps: List[Tuple[Symbol, Boolean]])
+

Class representing a qlassf function

+
+
+__init__(name: str, original_f: Callable, args: List[Arg], returns: Arg, exps: List[Tuple[Symbol, Boolean]])
+
+ +

Methods

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

__init__(name, original_f, args, returns, exps)

bind(**kwargs)

Returns a new QlassF with defined params

circuit()

compile([compiler, uncompute])

decode_counts(counts[, discard_lower])

Decode data from a circuit counts dict

decode_output(istr)

draw()

encode_input(*qvals)

export([framework])

Export the circuit to a supported framework

f()

Returns the classical python function

from_function(f[, types, defs, to_compile, ...])

Create a QlassF from a function or a string containing a function

gate([framework])

Returns the gate for a specific framework

to_logicfun()

truth_table([max])

Returns the truth table for the function using the sympy boolean for computing

truth_table_header()

Returns the list of string containing the truth table header

+

Attributes

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

input_qubits

Returns the list of input qubits

input_size

num_qubits

output_qubits

Returns the list of output qubits

output_size

Return the size of the return type (in bits)

qubits

Returns all the qubits of the circuit

name

original_f

args

returns

expressions

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qlassfun.qlassf.html b/generated/qlasskit.qlassfun.qlassf.html new file mode 100644 index 00000000..c6f5d225 --- /dev/null +++ b/generated/qlasskit.qlassfun.qlassf.html @@ -0,0 +1,167 @@ + + + + + + + qlasskit.qlassfun.qlassf — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qlassfun.qlassf

+
+
+qlasskit.qlassfun.qlassf(f: str | ~typing.Callable, types: ~typing.List[~qlasskit.types.qtype.Qtype] = [], defs: ~typing.List[~qlasskit.qlassfun.QlassF] = [], to_compile: bool = True, compiler: ~typing.Literal['internal', 'recompiler', 'tweedledum'] = 'internal', bool_optimizer: ~qlasskit.boolopt.bool_optimizer.BoolOptimizerProfile = <qlasskit.boolopt.bool_optimizer.BoolOptimizerProfile object>, uncompute: bool = True) → QlassF
+

Decorator / function creating a QlassF object

+
+
Parameters:
+
    +

  • f (Union[str, Callable]) – the function to be parsed, as a str code or callable

    • +

  • types (List[Qtype]) – list of qtypes to inject

    • +

  • defs (List[Qlassf]) – list of qlassf to inject

    • +

  • to_compile (boolean, optional) – if True, compile to quantum circuit (default: True)

    • +

  • compiler (SupportedCompiler, optional) – override default compiler (default: internal)

    • +

  • bool_optimizer (BoolOptimizerProfile, optional) – override default optimizer +(default: defaultOptimizer)

    • +

  • uncompute (bool, optional) – whenever uncompute input qubits during compilation +(default: True)

    • +
+
+
+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/generated/qlasskit.qlassfun.qlassfa.html b/generated/qlasskit.qlassfun.qlassfa.html new file mode 100644 index 00000000..788335e0 --- /dev/null +++ b/generated/qlasskit.qlassfun.qlassfa.html @@ -0,0 +1,152 @@ + + + + + + + qlasskit.qlassfun.qlassfa — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

qlasskit.qlassfun.qlassfa

+
+
+qlasskit.qlassfun.qlassfa(types: ~typing.List[~qlasskit.types.qtype.Qtype] = [], defs: ~typing.List[~qlasskit.qlassfun.QlassF] = [], to_compile: bool = True, compiler: ~typing.Literal['internal', 'recompiler', 'tweedledum'] = 'internal', bool_optimizer: ~qlasskit.boolopt.bool_optimizer.BoolOptimizerProfile = <qlasskit.boolopt.bool_optimizer.BoolOptimizerProfile object>, uncompute: bool = True)
+

Decorator with parameters for qlassf

+
+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..35a7d824 --- /dev/null +++ b/genindex.html @@ -0,0 +1,203 @@ + + + + + + Index — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+ +
+
+ +
+ +
+

© Copyright 2023, Davide Gessa (dakk).

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/how_it_works.html b/how_it_works.html new file mode 100644 index 00000000..5509510a --- /dev/null +++ b/how_it_works.html @@ -0,0 +1,269 @@ + + + + + + + How it works — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

How it works

+

To convert Python code into a quantum circuit, qlasskit implements a series of transformations:

+
    +

  1. It begins with the Python AST (Abstract Syntax Tree), converting it into a more streamlined form using the ast2ast module.

    2. +

  2. Next, the streamlined AST is translated into boolean expressions as an intermediate step by the ast2logic module. +During this phase, boolean expressions are refined and optimized in preparation for the final transformation.

    3. +

  3. Finally, the compiler module takes these optimized boolean expressions and compiles them into a +quantum circuit.

    4. +
+

Unlike other libraries that translate individual operations into quantum circuits before combining them, +qlasskit constructs a single boolean expression for each output qubit of the entire function. +This unique approach facilitates advanced optimization leveraging boolean algebraic properties.

+

For instance, let assume we have the following function:

+
+
+
from qlasskit import qlassf, Qint2, Qint4
+from qiskit import QuantumCircuit
+
+
+@qlassf
+def f_comp(b: bool, n: Qint2) -> Qint2:
+    for i in range(3):
+        n += 1 if b else 2
+    return n
+
+
+
+
+

If we decompose the algorithm in 3 separate additions and we compile them separately, we obtain the following circuit:

+
+
+
@qlassf
+def f1(b: bool, n: Qint2) -> Qint2:
+    return n + (1 if b else 2)
+
+
+qc = QuantumCircuit(f_comp.num_qubits * 2 - 1)
+
+for i in range(3):
+    qc.barrier(label=f"it_{i}")
+    qc.append(f1.gate(), [0] + list(range(1 + i * 2, 5 + i * 2)))
+
+print("Operations:", qc.decompose().count_ops())
+qc.decompose().draw("mpl")
+
+
+
+
+
Operations: OrderedDict([('cx', 12), ('barrier', 3), ('x', 3), ('ccx', 3)])
+
+
+
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to "iqp" in a following release. To silence this warning, specify the current default explicitly as style="clifford", or the new default as style="iqp".
+  self._style, def_font_ratio = load_style(self._style)
+
+
+_images/b05e4f779a0b4670c648fd32d723f5b20c77402dfdbb9e21f6f2d293de8c4fa6.png +
+
+

While if we compile the whole function to a quantum circuit using qlasskit, we obtain the following quantum circuit:

+
+
+
qc = QuantumCircuit(f_comp.num_qubits)
+qc.append(f_comp.gate(), f_comp.qubits)
+
+print("Operations:", qc.decompose().count_ops())
+qc.decompose().draw("mpl")
+
+
+
+
+
Operations: OrderedDict([('cx', 7), ('x', 1), ('ccx', 1)])
+
+
+_images/491cb9b1407c5f41003d5da03c7ef3a35fa8acf294c42364e650599ca92ede80.png +
+
+

As we can see from the circuit drawings, qlasskit approach needs half the number of qubits and half the number of gates.

+
+

AST Traslator

+

Given a python function, the qlasskit.ast2logic module walks its syntax tree translating all the statements / +expressions to boolean expressions.

+

For instance, the following function:

+
+
+
@qlassf
+def f(n: Qint4) -> bool:
+    return n == 3
+
+
+
+
+

Is translated to this boolean expression:

+
+
+
print(f.expressions)
+
+
+
+
+
[(_ret, n.0 & n.1 & ~n.2 & ~n.3)]
+
+
+
+
+
+
+

Compiler

+

The boolean expressions are then being fed to the `qlasskit.compiler`` which compiles boolean expressions +to invertible circuits, introducing auxiliary qubits. In this step, the compiler will automatically uncompute +auxiliary qubits in order to reduce the number of qubits needed and the circuit footprint.

+

For the compilation, two backends are supported:

+
    +

  • InternalCompiler

    • +

  • Tweedledum.xag_synth

    • +
+
+
+

Result

+

The result of the compiler is a quantum circuit represented with qlasskit QCircuit. This circuit +can now be exported to one of the supported framework as a gate or as a standalone circuit.

+

The previous example function f, is translated to the following quantum circuit: the +result is available at qubit q6.

+
+
+
f.export().draw("mpl")
+
+
+
+
+_images/293285b423513d39c4a73e871f2211a526039c0ce9d988d5f7d9abe704adabc2.png +
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..4ef379cd --- /dev/null +++ b/index.html @@ -0,0 +1,208 @@ + + + + + + + Qlasskit — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Qlasskit

+

Qlasskit is a Python library that allows quantum developers to write classical algorithms in pure +Python and translate them into unitary operators (gates) for use in quantum circuits.

+
+

Qlasskit

+ +
+
+

Examples

+ +
+
+
+

Indices and tables

+ +
+
+

Cite

+
@software{qlasskit2023,
+  author = {Davide Gessa},
+  title = {qlasskit: a python-to-quantum circuit compiler},
+  url = {https://github.com/dakk/qlasskit},
+  year = {2023},
+}
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 00000000..4785a519 Binary files /dev/null and b/objects.inv differ diff --git a/py-modindex.html b/py-modindex.html new file mode 100644 index 00000000..bd5eb9c2 --- /dev/null +++ b/py-modindex.html @@ -0,0 +1,153 @@ + + + + + + Python Module Index — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Python Module Index
    • +
  • +
    • +
+
+
+
+
+ + +

Python Module Index

+ +
+ q +
+ + + + + + + + + + + + + +
 
+ q
+ qlasskit +
    + qlasskit.algorithms.qalgorithm +
    + qlasskit.qcircuit.gates +
+ + +
+
+
+ +
+ +
+

© Copyright 2023, Davide Gessa (dakk).

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/quickstart.html b/quickstart.html new file mode 100644 index 00000000..4336af06 --- /dev/null +++ b/quickstart.html @@ -0,0 +1,202 @@ + + + + + + + Quickstart — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Quickstart

+

First install qlasskit using pip.

+

pip install qlasskit

+

We now define a qlassf function that sums two numbers:

+
+
+
from qlasskit import qlassf, Qint2
+
+
+@qlassf
+def sum_two_numbers(a: Qint2, b: Qint2) -> Qint2:
+    return a + b
+
+
+
+
+

We can now export the resulting quantum circuit to any supported framework:

+
+
+
circuit = sum_two_numbers.export("qiskit")
+circuit.draw("mpl")
+
+
+
+
+
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to "iqp" in a following release. To silence this warning, specify the current default explicitly as style="clifford", or the new default as style="iqp".
+  self._style, def_font_ratio = load_style(self._style)
+
+
+_images/27a749800d923fb69b5d38e7722422621f8acf4ee5c2b6f517f89d74a936444a.png +
+
+

The qlassf function can be also exported as a gate, if the destination framwork supports it. We can use encode_input and decode_output in order to convert from/to high level types of qlasskit without worrying about the binary representation.

+
+
+
from qiskit import QuantumCircuit
+
+qc = QuantumCircuit(sum_two_numbers.num_qubits, len(sum_two_numbers.output_qubits))
+
+qc.initialize(
+    sum_two_numbers.encode_input(Qint2(1), Qint2(2)), sum_two_numbers.input_qubits
+)
+qc.append(sum_two_numbers.gate("qiskit"), sum_two_numbers.qubits)
+qc.measure(sum_two_numbers.output_qubits, range(len(sum_two_numbers.output_qubits)))
+qc.draw("mpl")
+
+
+
+
+_images/c11daf869c135eac3524304f0b40bd3d1ae514f73d769f0de4a9d577f9da51ba.png +
+
+
+
+
from qiskit import Aer, QuantumCircuit, transpile
+from qiskit.visualization import plot_histogram
+
+simulator = Aer.get_backend("aer_simulator")
+circ = transpile(qc, simulator)
+result = simulator.run(circ).result()
+counts = result.get_counts(circ)
+
+counts_readable = sum_two_numbers.decode_counts(counts)
+plot_histogram(counts_readable)
+
+
+
+
+_images/beafab15f915183b637909d537345580eba0f3a7fb66329311dd39909074d252.png +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/search.html b/search.html new file mode 100644 index 00000000..b47c9a49 --- /dev/null +++ b/search.html @@ -0,0 +1,143 @@ + + + + + + Search — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Search
    • +
  • +
    • +
+
+
+
+
+ + + + +
+ +
+ +
+
+
+ +
+ +
+

© Copyright 2023, Davide Gessa (dakk).

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 00000000..dc94279b --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"docnames": ["algorithms", "api", "example_big_circuit", "example_deutsch_jozsa", "example_grover", "example_grover_factors", "example_grover_hash", "example_grover_subset", "example_grover_sudoku", "example_simon", "example_unitary_of_f", "exporter", "generated/qlasskit.algorithms.grover.Grover", "generated/qlasskit.algorithms.qalgorithm", "generated/qlasskit.qcircuit.gates", "generated/qlasskit.qcircuit.qcircuit.QCircuit", "generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper", "generated/qlasskit.qlassfun.QlassF", "generated/qlasskit.qlassfun.qlassf", "generated/qlasskit.qlassfun.qlassfa", "how_it_works", "index", "quickstart", "supported"], "filenames": ["algorithms.rst", "api.rst", "example_big_circuit.ipynb", "example_deutsch_jozsa.ipynb", "example_grover.ipynb", "example_grover_factors.ipynb", "example_grover_hash.ipynb", "example_grover_subset.ipynb", "example_grover_sudoku.ipynb", "example_simon.ipynb", "example_unitary_of_f.ipynb", "exporter.ipynb", "generated/qlasskit.algorithms.grover.Grover.rst", "generated/qlasskit.algorithms.qalgorithm.rst", "generated/qlasskit.qcircuit.gates.rst", "generated/qlasskit.qcircuit.qcircuit.QCircuit.rst", "generated/qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.rst", "generated/qlasskit.qlassfun.QlassF.rst", "generated/qlasskit.qlassfun.qlassf.rst", "generated/qlasskit.qlassfun.qlassfa.rst", "how_it_works.ipynb", "index.rst", "quickstart.ipynb", "supported.rst"], "titles": ["Algorithms", "API", "Working with big circuits", "Deutsch Jozsa algorithm", "Grover search", "Grover: factorize number", "Grover search: hash function preimage attack", "Grover search: subset problem", "Grover search: sudoku solver", "Simon function periodicity", "Unitary of qlasskit function", "Exporting to other frameworks", "qlasskit.algorithms.grover.Grover", "qlasskit.algorithms.qalgorithm", "qlasskit.qcircuit.gates", "qlasskit.qcircuit.qcircuit.QCircuit", "qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper", "qlasskit.qlassfun.QlassF", "qlasskit.qlassfun.qlassf", "qlasskit.qlassfun.qlassfa", "How it works", "Qlasskit", "Quickstart", "Supported python subset"], "terms": {"qlasskit": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 20, 22, 23], "implement": [0, 11, 20], "high": [0, 4, 22], "level": [0, 4, 10, 22], "represent": [0, 22], "quantum": [0, 2, 4, 6, 7, 10, 15, 18, 20, 21, 22], "reli": 0, "black": 0, "box": 0, "function": [0, 4, 7, 12, 13, 14, 17, 18, 20, 21, 22], "oracl": [0, 4, 6, 7, 8, 12], "i": [2, 4, 6, 7, 8, 20, 21, 23], "capabl": [2, 8], "produc": [2, 4, 7], "larg": 2, "without": [2, 6, 8, 22], "ani": [2, 22, 23], "issu": 2, "The": [2, 3, 4, 5, 6, 8, 9, 11, 15, 20, 22, 23], "onli": [2, 23], "thing": 2, "you": [2, 23], "have": [2, 8, 20, 23], "do": 2, "us": [2, 4, 6, 7, 8, 10, 20, 21, 22], "fastoptim": 2, "sinc": [2, 6, 7, 8], "run": [2, 3, 4, 5, 6, 7, 8, 9, 22], "cse": 2, "too": 2, "slow": 2, "express": [2, 20, 21], "list": [2, 4, 7, 17, 18, 19, 20], "In": [2, 4, 6, 7, 8, 10, 20, 23], "next": [2, 20], "exampl": [2, 4, 6, 7, 8, 10, 20, 23], "we": [2, 4, 6, 7, 8, 10, 12, 20, 22], "ar": [2, 4, 6, 7, 8, 20, 23], "go": [2, 8], "creat": [2, 8, 18], "64": 2, "qint8": [2, 6, 23], "input": [2, 4, 6, 7, 18], "one": [2, 20], "output": [2, 4, 6, 7, 20], "result": [2, 3, 4, 5, 6, 7, 8, 9, 10, 21, 22], "21984": 2, "qubit": [2, 8, 10, 15, 18, 20, 22, 23], "1044": 2, "gate": [2, 8, 10, 11, 20, 21, 22, 23], "around": 2, "5": [2, 5, 6, 7, 20], "second": 2, "from": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 22], "import": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 22, 23], "qlist": [2, 4, 6, 23], "boolopt": [2, 18, 19], "qlassfa": [2, 21], "bool_optim": [2, 18, 19], "def": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 18, 19, 20, 22], "test": 2, "a_list": [2, 4], "h_val": 2, "0": [2, 5, 6, 7, 8, 10, 11, 15, 20, 23], "c": [2, 8, 23], "return": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 20, 22], "print": [2, 6, 8, 10, 11, 20, 23], "qcircuit": [2, 8, 20, 21], "qlassf": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 19, 20, 21, 22, 23], "qint4": [3, 6, 8, 9, 20, 23], "f": [3, 9, 10, 12, 18, 20, 23], "bool": [3, 4, 5, 8, 10, 11, 12, 18, 19, 20], "7": [3, 7, 20], "export": [3, 4, 5, 6, 7, 8, 9, 20, 21, 22], "qiskit": [3, 4, 5, 6, 7, 8, 9, 10, 20, 21, 22], "draw": [3, 4, 5, 6, 9, 10, 11, 20, 22], "mpl": [3, 4, 5, 6, 9, 10, 11, 20, 22], "opt": [3, 4, 5, 6, 9, 11, 20, 22], "hostedtoolcach": [3, 4, 5, 6, 9, 11, 20, 22], "python": [3, 4, 5, 6, 9, 11, 20, 21, 22], "3": [3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 22, 23], "10": [3, 4, 5, 6, 9, 11, 20, 22], "13": [3, 4, 5, 6, 9, 11, 20, 22, 23], "x64": [3, 4, 5, 6, 9, 11, 20, 22], "lib": [3, 4, 5, 6, 9, 11, 20, 22], "python3": [3, 4, 5, 6, 9, 11, 20, 22], "site": [3, 4, 5, 6, 9, 11, 20, 22], "packag": [3, 4, 5, 6, 9, 11, 20, 22], "visual": [3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 22], "circuit": [3, 4, 5, 6, 8, 9, 10, 11, 15, 18, 20, 21, 22, 23], "matplotlib": [3, 4, 5, 6, 9, 11, 20, 22], "py": [3, 4, 5, 6, 9, 11, 20, 22], "266": [3, 4, 5, 6, 9, 11, 20, 22], "futurewarn": [3, 4, 5, 6, 9, 11, 20, 22], "default": [3, 4, 5, 6, 9, 11, 12, 15, 18, 20, 22], "drawer": [3, 4, 5, 6, 9, 11, 20, 22], "scheme": [3, 4, 5, 6, 9, 11, 20, 22], "chang": [3, 4, 5, 6, 9, 11, 20, 22], "iqp": [3, 4, 5, 6, 9, 11, 20, 22], "follow": [3, 4, 5, 6, 8, 9, 11, 20, 22, 23], "releas": [3, 4, 5, 6, 9, 11, 20, 22], "To": [3, 4, 5, 6, 9, 11, 20, 22], "silenc": [3, 4, 5, 6, 9, 11, 20, 22], "thi": [3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 22, 23], "warn": [3, 4, 5, 6, 9, 11, 20, 22], "specifi": [3, 4, 5, 6, 9, 11, 20, 22], "current": [3, 4, 5, 6, 9, 11, 20, 22], "explicitli": [3, 4, 5, 6, 9, 11, 20, 22], "style": [3, 4, 5, 6, 9, 11, 20, 22], "clifford": [3, 4, 5, 6, 9, 11, 20, 22], "new": [3, 4, 5, 6, 9, 11, 20, 22], "self": [3, 4, 5, 6, 9, 11, 20, 22], "_style": [3, 4, 5, 6, 9, 11, 20, 22], "def_font_ratio": [3, 4, 5, 6, 9, 11, 20, 22], "load_styl": [3, 4, 5, 6, 9, 11, 20, 22], "deutschjozsa": 3, "q_algo": [3, 4, 5, 6, 7, 8, 9], "qc": [3, 4, 5, 6, 7, 8, 9, 10, 11, 15, 20, 22], "aer": [3, 4, 5, 6, 7, 8, 9, 22], "quantumcircuit": [3, 4, 5, 6, 7, 8, 9, 10, 20, 22], "transpil": [3, 4, 5, 6, 7, 8, 9, 10, 22], "plot_histogram": [3, 4, 5, 6, 7, 8, 9, 22], "measure_al": [3, 4, 5, 6, 7, 8, 9], "simul": [3, 4, 5, 6, 7, 8, 9, 10, 22], "get_backend": [3, 4, 5, 6, 7, 8, 9, 22], "aer_simul": [3, 4, 5, 6, 7, 8, 9, 22], "circ": [3, 4, 5, 6, 7, 8, 9, 22], "count": [3, 4, 5, 6, 7, 8, 9, 22], "get_count": [3, 4, 5, 6, 7, 8, 9, 22], "counts_read": [3, 4, 5, 6, 7, 8, 9, 22], "decode_count": [3, 4, 5, 6, 7, 8, 9, 22], "offer": 4, "class": [4, 12, 13, 14, 15, 16, 17], "easili": 4, "perform": [4, 6, 10], "over": 4, "first": [4, 22], "defin": [4, 7, 22], "name": [4, 7, 15, 17], "and_al": 4, "true": [4, 8, 12, 18, 19, 23], "iff": 4, "all": [4, 20, 23], "element": [4, 7, 8, 12], "an": [4, 6, 7, 8, 20], "want": [4, 7, 8, 12], "find": [4, 6, 7], "valu": [4, 6, 7, 8, 12, 23], "led": [4, 7], "qint2": [4, 5, 7, 8, 11, 20, 22, 23], "4": [4, 6, 8, 11, 12, 23], "r": [4, 8], "compil": [4, 18, 19, 21, 23], "optim": [4, 18, 20], "given": [4, 7, 20], "now": [4, 6, 7, 20, 22], "can": [4, 6, 8, 10, 20, 22], "our": [4, 6, 7, 8, 10, 12], "For": [4, 7, 20], "instanc": [4, 7, 20], "yeld": 4, "algorithm": [4, 5, 6, 7, 8, 9, 20, 21], "prepar": [4, 20], "Then": [4, 6, 7, 8], "prefer": [4, 6, 7, 8], "framework": [4, 6, 7, 8, 10, 20, 21, 22], "sampl": [4, 6, 7, 8], "along": 4, "wrapper": [4, 16], "provid": 4, "util": [4, 23], "encod": [4, 8], "decod": 4, "type": [4, 5, 7, 18, 19, 21, 22], "definit": 4, "histogram": [4, 7], "": [4, 7, 23], "evid": [4, 7], "lead": [4, 7, 23], "where": [4, 8], "set": [4, 7], "align": [4, 7], "expect": [4, 7, 12], "tupl": [5, 6, 7, 17], "1": [5, 6, 7, 8, 9, 10, 11, 12, 20, 22, 23], "9": 5, "discard_low": [5, 6, 8], "ever": 6, "evolv": 6, "landscap": 6, "cybersecur": 6, "advent": 6, "comput": 6, "present": [6, 23], "both": 6, "extraordinari": 6, "opportun": 6, "unpreced": 6, "challeng": 6, "notebook": 6, "exploit": [6, 10], "toi": [6, 8], "A": 6, "cryptograph": 6, "h": [6, 23], "m": [6, 8, 23], "tri": 6, "messag": 6, "ha": [6, 23], "specif": [6, 8], "easi": 6, "write": [6, 21], "like": 6, "hash_simp": 6, "2": [6, 7, 8, 11, 20, 22, 23], "hv": 6, "0xff": 6, "thank": 6, "fact": 6, "standard": 6, "call": 6, "original_f": [6, 17], "some": 6, "kind": 6, "analysi": 6, "space": 6, "tini": 6, "detect": 6, "uniform": 6, "map": 6, "equal": [6, 8], "collect": 6, "counter": 6, "d": [6, 23], "hex": 6, "x": [6, 12, 20], "y": [6, 12], "rang": [6, 8, 20, 22, 23], "len": [6, 22, 23], "256": 6, "got": 6, "distribut": 6, "order": [6, 10, 20, 22], "which": [6, 7, 20], "0xca": 6, "doubl": 6, "check": [6, 8], "invok": 6, "12": [6, 20, 23], "must": [6, 8], "subset_sum": 7, "j": 7, "sum": [7, 8, 22, 23], "l": 7, "combin": [7, 20], "qint3": 7, "ii": 7, "els": [7, 11, 20], "here": 7, "know": 7, "least": 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20, 23], "num_qubit": [10, 15, 20, 22], "append": [10, 20, 22], "save_st": 10, "decompos": [10, 20], "_ret": [10, 11, 20], "method": [10, 12, 15, 16, 17], "job": 10, "shot": 10, "8192": 10, "get_unitari": 10, "max_siz": 10, "16": 10, "begin": [10, 20], "bmatrix": 10, "end": 10, "hello_world": 11, "openqasm": 11, "cx": [11, 20, 23], "ccx": [11, 20], "qml": 11, "tape": 11, "quantumtap": 11, "wire": 11, "param": [11, 23], "displaystyl": 11, "c_": 11, "left": 11, "x_": 11, "right": 11, "text": 11, "cnot": 11, "_": 11, "00000": 11, "rangl": 11, "element_to_search": 12, "qtype": [12, 18, 19], "none": [12, 15], "n_iter": 12, "int": [12, 15], "__init__": [12, 15, 16, 17], "paramet": [12, 15, 18, 19], "satisfi": 12, "gener": 12, "compar": 12, "option": [12, 15, 18, 23], "search": [12, 21], "forc": 12, "iter": [12, 23], "otherwis": 12, "pi": 12, "sqrt": 12, "match": 12, "attribut": [12, 15, 16, 17], "except": 13, "nativ": 15, "initi": [15, 22], "interfac": 16, "contain": [16, 23], "str": [17, 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"boolean": 23, "oper": 23, "If": 23, "compar": 23, "unari": 23, "op": 23, "bin": 23, "call": 23, "statement": 23, "assign": 23, "return": 23, "For": 23, "loop": 23, "def": 23, "els": 23, "quantum": 23, "hybrid": 23}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx": 60}, "alltitles": {"Algorithms": [[0, "algorithms"]], "Grover search": [[0, "grover-search"], [4, "grover-search"]], "Simon periodicity": [[0, "simon-periodicity"]], "Deutsch Jozsa": [[0, "deutsch-jozsa"]], "API": [[1, "api"]], "Working with big circuits": [[2, "working-with-big-circuits"]], "Deutsch Jozsa algorithm": [[3, "deutsch-jozsa-algorithm"]], "Grover: factorize number": [[5, "grover-factorize-number"]], "Grover search: hash function preimage attack": [[6, 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"boolean-operators"]], "If expressions": [[23, "if-expressions"]], "Comparators": [[23, "comparators"]], "Unary Op": [[23, "unary-op"]], "Bin Op": [[23, "bin-op"]], "Function call": [[23, "function-call"]], "Statements": [[23, "statements"]], "Assign": [[23, "assign"]], "Return": [[23, "return"]], "For loop": [[23, "for-loop"]], "Function def": [[23, "function-def"]], "If then else": [[23, "if-then-else"]], "Quantum Hybrid": [[23, "quantum-hybrid"]]}, "indexentries": {"grover (class in qlasskit.algorithms.grover)": [[12, "qlasskit.algorithms.grover.Grover"]], "__init__() (qlasskit.algorithms.grover.grover method)": [[12, "qlasskit.algorithms.grover.Grover.__init__"]], "module": [[13, "module-qlasskit.algorithms.qalgorithm"], [14, "module-qlasskit.qcircuit.gates"]], "qlasskit.algorithms.qalgorithm": [[13, "module-qlasskit.algorithms.qalgorithm"]], "qlasskit.qcircuit.gates": [[14, "module-qlasskit.qcircuit.gates"]], "qcircuit (class in qlasskit.qcircuit.qcircuit)": [[15, "qlasskit.qcircuit.qcircuit.QCircuit"]], "__init__() (qlasskit.qcircuit.qcircuit.qcircuit method)": [[15, "qlasskit.qcircuit.qcircuit.QCircuit.__init__"]], "qcircuitwrapper (class in qlasskit.qcircuit.qcircuitwrapper)": [[16, "qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper"]], "__init__() (qlasskit.qcircuit.qcircuitwrapper.qcircuitwrapper method)": [[16, "qlasskit.qcircuit.qcircuitwrapper.QCircuitWrapper.__init__"]], "qlassf (class in qlasskit.qlassfun)": [[17, "qlasskit.qlassfun.QlassF"]], "__init__() (qlasskit.qlassfun.qlassf method)": [[17, "qlasskit.qlassfun.QlassF.__init__"]], "qlassf() (in module qlasskit.qlassfun)": [[18, "qlasskit.qlassfun.qlassf"]], "qlassfa() (in module qlasskit.qlassfun)": [[19, "qlasskit.qlassfun.qlassfa"]]}}) \ No newline at end of file diff --git a/supported.html b/supported.html new file mode 100644 index 00000000..3edf77d3 --- /dev/null +++ b/supported.html @@ -0,0 +1,364 @@ + + + + + + + Supported python subset — qlasskit documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Supported python subset

+

Qlasskit supports a subset of python. This subset will be expanded, but it is +limited by the linearity of quantum circuits and by the number of qubits.

+

The structure of a qlasskit function has the following pattern:

+
@qlasskit
+def f(param: type, [...param: type]) -> type:
+   statement
+   ...
+   statement
+
+
+
+

Types

+

All types has a static size.

+
+

bool

+

Boolean type.

+
+
+

Qint

+

Unsigned integers; this type has subtypes for different Qint sizes (Qint2, Qint4, Qint8, Qint12, Qint16). +Single bit of the Qint are accessible by the subscript operator [].

+
+
+

Tuple

+

Container type holding different types.

+
+
+

List

+

Qlist[T, size] denotes a fixed-size list in qlasskit. +For example, the list [1,2,3] is typed as Qlist[Qint2,3].

+
+
+

Matrix

+

Qmatrix[T, m, n] denotes a fixed-size list in qlasskit. +For example, the matrix [[1,2],[3,4]] is typed as Qmatrix[Qint2,2,2].

+
+
+
+

Expressions

+
+

Constants

+
True
+
+
+
42
+
+
+
+
+

Tuple

+
(a, b)
+
+
+
+
+

List (fixed size)

+
[a, b]
+
+
+
+
+

2D Matrix (fixed size)

+
[[a, b], [c,d]]
+
+
+
+
+

Subscript

+
a[0]
+
+
+
+
+

Boolean operators

+
not a
+
+
+
a and b
+
+
+
a or b
+
+
+
+
+

If expressions

+
a if b else c
+
+
+
+
+

Comparators

+
a > b or b <= c and c == d or c != a
+
+
+
+
+

Unary Op

+
~a
+
+
+
+
+

Bin Op

+
a << 1
+
+
+
a >> 2
+
+
+
a + b
+
+
+
a - b
+
+
+
a * b
+
+
+
a % 2
+
+
+
+

Note

+

Modulo operator only works with 2^n values.

+
+
+
+

Function call

+

Bultin functions: +- print(): debug function, ignore by conversion +- len(Tuple), len(Qlist)`: returns the length of a tuple +- max(a, b, …), max(Tuple), max(Qlist): returns the max of a tuple +- min(a, b, …), min(Tuple), min(Qlist): returns the min of a tuple +- sum(Tuple), sum(Qlist): returns the sum of the elemnts of a tuple / list +- all(Tuple), all(Qlist): returns True if all of the elemnts are True +- any(Tuple), any(Qlist): returns True if any of the elemnts are True

+
+
+
+

Statements

+
+

Assign

+
c = not a
+
+
+
+
+

Return

+
return b+1
+
+
+
+
+

For loop

+
for i in range(4):
+   a += i
+
+
+
+

Note

+

Please note that in qlasskit, for loops are unrolled during compilation. Therefore, +it is essential that the number of iterations for each for loop is known at the +time of compilation.

+
+
+
+

Function def

+
def f(t: Qlist[Qint4,2]) -> Qint4:
+   return t[0] + t[1]
+
+
+
+
+

If then else

+
c = 0
+if cond:
+   c += 12
+else:
+   c += 13
+
+
+
+

Note

+

At present, the if-then-else statement in qlasskit is designed to support branch bodies +that exclusively contain assignment statements.

+
+
+
+
+

Quantum Hybrid

+

In a qlassf function, you have the option to utilize quantum gates through the Q module. It’s +important to keep in mind that incorporating quantum gates within a qlasskit function leads +to a Python function that exhibits distinct behaviors compared to its quantum counterpart.

+
def bell(a: bool, b: bool) -> bool:
+   return Q.CX(Q.H(a), b)
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file