0_introduction_to_sequential_circuits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5ebea6e",
   "metadata": {},
   "source": [
    "# Pennylane : Sequential Circuit Structure\n",
    "This notebook shows the expansion of Pennylane to build sequential circuits. <br>\n",
    "The specific expansion is the use of python classes to streamline the way that <br>\n",
    "Quantum Variational Circuits are constructed to fit common Deep Learning framework <br> architectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fbe16242",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pennylane as qml\n",
    "from pennylane import numpy as np\n",
    "from pennylane.templates.embeddings import AmplitudeEmbedding, AngleEmbedding\n",
    "from model import Conv1DLayer, PoolingLayer, unitarity_conv1d, unitarity_pool, unitarity_toffoli"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445c5fb0",
   "metadata": {},
   "source": [
    "# Defining the device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "eb0a5a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specify how many wires will be used\n",
    "num_wires = 12\n",
    "num_ancilla = 4\n",
    "num_work_wires = num_wires - num_ancilla\n",
    "\n",
    "#Specify what type of device you are using.\n",
    "#device_type = 'default.qubit'\n",
    "device_type = 'lightning.qubit'\n",
    "\n",
    "#Initialize Device\n",
    "dev = qml.device(device_type, wires=num_wires)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81372847",
   "metadata": {},
   "source": [
    "# Defining the sequential circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "fff5b152",
   "metadata": {},
   "outputs": [],
   "source": [
    "def circuit(num_wires, num_ancilla):\n",
    "    @qml.qnode(dev, interface='torch', diff_method ='adjoint')\n",
    "    def func(inputs, params): \n",
    "        work_wires = list(range(num_wires - num_ancilla))\n",
    "        ancilla_wires = list(range(len(work_wires),num_wires))\n",
    "\n",
    "        # Data Embedding Layer\n",
    "        AmplitudeEmbedding(inputs , wires=work_wires , normalize=True)\n",
    "\n",
    "        # Hidden Layers\n",
    "        work_wires , params = Conv1DLayer(unitarity_conv1d , 15)(work_wires , params)\n",
    "        work_wires , params = PoolingLayer(unitarity_pool , 2)(work_wires , params)\n",
    "        work_wires , params = Conv1DLayer(unitarity_conv1d , 15)(work_wires , params)\n",
    "        work_wires , params = PoolingLayer(unitarity_pool , 2)(work_wires , params)\n",
    "\n",
    "        #Toffili Structure\n",
    "        unitarity_toffoli(work_wires,ancilla_wires)\n",
    "\n",
    "\n",
    "        return [qml.expval(qml.PauliZ(wire)) for wire in ancilla_wires]\n",
    "    return func"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1adbd920",
   "metadata": {},
   "source": [
    "## Visualizing the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bf0f3b15",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 7704x936 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x is the encoded date sized to an array of x length, where x = 2^qubits.\n",
    "embedding_size = 2**num_work_wires\n",
    "dummy_embedding = [1] * embedding_size\n",
    "\n",
    "# Initializing parameters, this based off the unitarity, and model structure. \n",
    "params = np.random.normal(loc=0.0, scale=1.0, size = 198)\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "fig, ax = qml.draw_mpl(circuit(num_wires, num_ancilla), decimals=2)(dummy_embedding, params)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "instance_type": "ml.t3.medium",
  "kernelspec": {
   "display_name": "conda_pytorch_p38",
   "language": "python",
   "name": "conda_pytorch_p38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}