From 163b01958d7355494798844f9a33d08d0cb2267a Mon Sep 17 00:00:00 2001 From: meandmytram Date: Mon, 28 Oct 2024 12:47:41 -0400 Subject: [PATCH] add comments --- examples/decoding/shor.ipynb | 99 +++++++++++++++--------------------- 1 file changed, 41 insertions(+), 58 deletions(-) diff --git a/examples/decoding/shor.ipynb b/examples/decoding/shor.ipynb index fca35f71..7e8e0dcb 100644 --- a/examples/decoding/shor.ipynb +++ b/examples/decoding/shor.ipynb @@ -12,7 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this experiment, we decode Shor's nine-qubit quantum error correcting code which protects a single qubit from all types of errors. Here, we demonstrate error-based correction, which means that the decoder takes a Pauli error as input and outputs the most likely logical operator. After one run of the algorithm we will end up with a probability distribution over I, X, Z, Y Pauli operators which are to be applied to the logical qubit encoded. In this experiment, we do not truncate thus perform exact maximum likelihood decoding." + "In this experiment, we decode Shor's nine-qubit quantum error correcting code which protects a single qubit from all types of errors by using ``mdopt``. Here, we demonstrate direct-error input decoding, which means that the decoder takes a Pauli error as input and outputs the most likely logical operator. This pipeline is sufficient for threshold computation. In reality, the decoder could be shown a syndrome measurement, from which possible error patterns would be sampled. After each run, the algorithm yields a probability distribution over the Pauli operators (I, X, Z, Y) to apply to the encoded logical qubit. Hereafter, we assume an independent noise model as well as perfect syndrome measurements. In this experiment, we do not truncate the tensor network thus perform exact maximum likelihood decoding." ] }, { @@ -120,7 +120,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, let us define the initial state. First of all we will check that no error implies no correction. This means starting from the all-zeros state followed by decoding will return all-zeros state for the logical operators (the final logical operator will thus be identity operator). Thus, we start from the all-zero state for the error and the $|+\\rangle$ state for the logicals." + "Now, let us define the initial state. First of all we will check that no error implies no correction. This means starting from the all-zero state followed by decoding will return all-zero state for the logical operators (the final logical operator will thus be identity operator). Thus, we start from the all-zero state for the error and the $|+\\rangle$ state for the logicals." ] }, { @@ -271,8 +271,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2, 4, 6]\n", - "[3, 9, 15]\n" + "[[2, 4, 6]]\n", + "[[3, 9, 15]]\n" ] } ], @@ -298,8 +298,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0], [2, 4], [1, 3, 5], [6]]\n", - "[[1], [3, 9], [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14], [15]]\n" + "[[[0], [2, 4], [1, 3, 5], [6]]]\n", + "[[[1], [3, 9], [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14], [15]]]\n" ] } ], @@ -351,37 +351,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2/2 [00:00<00:00, 331.78it/s]\n", - "100%|██████████| 6/6 [00:00<00:00, 1133.60it/s]\n", - "100%|██████████| 2/2 [00:00<00:00, 271.04it/s]\n" + "100%|██████████| 2/2 [00:00<00:00, 452.68it/s]\n", + "100%|██████████| 6/6 [00:00<00:00, 1558.83it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 476.19it/s]\n", + "100%|██████████| 1/1 [00:00<00:00, 321.75it/s]\n" ] } ], "source": [ - "error_mps = apply_constraints(\n", - " error_mps,\n", - " constraints_sites[0],\n", - " constraints_tensors,\n", - " renormalise=renormalise,\n", - " result_to_explicit=result_to_explicit,\n", - " strategy=\"Optimised\",\n", - ")\n", - "error_mps = apply_constraints(\n", - " error_mps,\n", - " constraints_sites[1],\n", - " constraints_tensors,\n", - " renormalise=renormalise,\n", - " result_to_explicit=result_to_explicit,\n", - " strategy=\"Optimised\",\n", - ")\n", - "error_mps = apply_constraints(\n", - " error_mps,\n", - " logicals_sites,\n", - " logicals_tensors,\n", - " renormalise=renormalise,\n", - " result_to_explicit=result_to_explicit,\n", - " strategy=\"Optimised\",\n", - ")" + "for i in [0, 1]:\n", + " error_mps = apply_constraints(\n", + " error_mps,\n", + " constraints_sites[i],\n", + " constraints_tensors,\n", + " renormalise=renormalise,\n", + " result_to_explicit=result_to_explicit,\n", + " strategy=\"Optimised\",\n", + " )\n", + "for i in [0, 1]:\n", + " error_mps = apply_constraints(\n", + " error_mps,\n", + " logicals_sites[i],\n", + " logicals_tensors,\n", + " renormalise=renormalise,\n", + " result_to_explicit=result_to_explicit,\n", + " strategy=\"Optimised\",\n", + " )" ] }, { @@ -457,15 +452,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 27/27 [00:00<00:00, 71.00it/s]\n" + "27it [00:00, 121.92it/s]\n" ] } ], "source": [ - "one_qubit_errors = [pauli_to_mps(pauli) for pauli in one_qubit_paulis]\n", "one_qubit_outputs = [\n", " decode_css(code, error, bias_type=\"Bitflip\", renormalise=renormalise, silent=True)\n", - " for error in tqdm(one_qubit_errors)\n", + " for error in tqdm(one_qubit_paulis)\n", "]\n", "one_qubit_corrections_distribution = [output[0] for output in one_qubit_outputs]" ] @@ -479,15 +473,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 324/324 [00:03<00:00, 98.65it/s] \n" + "324it [00:02, 120.44it/s]\n" ] } ], "source": [ - "two_qubit_errors = [pauli_to_mps(pauli) for pauli in two_qubit_paulis]\n", "two_qubit_outputs = [\n", " decode_css(code, error, bias_type=\"Bitflip\", renormalise=renormalise, silent=True)\n", - " for error in tqdm(two_qubit_errors)\n", + " for error in tqdm(two_qubit_paulis)\n", "]\n", "two_qubit_corrections_distribution = [output[0] for output in two_qubit_outputs]" ] @@ -501,15 +494,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2268/2268 [00:22<00:00, 99.11it/s] \n" + "2268it [00:19, 118.30it/s]\n" ] } ], "source": [ - "three_qubit_errors = [pauli_to_mps(pauli) for pauli in three_qubit_paulis]\n", "three_qubit_outputs = [\n", " decode_css(code, error, bias_type=\"Bitflip\", renormalise=renormalise, silent=True)\n", - " for error in tqdm(three_qubit_errors)\n", + " for error in tqdm(three_qubit_paulis)\n", "]\n", "three_qubit_corrections_distribution = [output[0] for output in three_qubit_outputs]" ] @@ -538,7 +530,7 @@ "outputs": [ { "data": { - "image/png": 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A1NXw+Fi8eHFs3749BgYGYuvWrXH06NH44he/GKdOnTrn+v7+/igWi7WtVCo1eiQA4CLSUq1WqxN5g5MnT8YVV1wRjzzySNx5551nna9UKlGpVGr75XI5SqVSjIyMRFtb20SOBkyCK+9/erJHqNtLP7plskeAi165XI5isfi+fn5P+JOgs2fPjo9//ONx+PDhc54vFApRKBQmegwA4CIx4f/Ox+nTp+PIkSMxb968ib4VANAEGh4f3/nOd2JwcDBeeuml+Mtf/hJf/epX45JLLonbb7+90bcCAJpQwz92eeWVV+L222+PEydOxGWXXRY33nhj7Nu3Ly677LJG3woAaEINj4/HH3+80ZcEAKYQv9sFAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVBMWH1u2bIkrr7wyZs6cGYsXL46//vWvE3UrAKCJTEh8/OY3v4ne3t546KGH4m9/+1ssWrQoVq5cGf/85z8n4nYAQBOZkPh45JFH4q677oo77rgjPvWpT8Wjjz4al156afziF7+YiNsBAE1keqMv+MYbb8T+/fujr6+vdmzatGmxfPny2Lt371nrK5VKVCqV2v7IyEhERJTL5UaPBlwExiqvTfYIdfP/I3hvb/09qVar77m24fHx73//O958883o7Owcd7yzszP+8Y9/nLW+v78/Nm3adNbxUqnU6NEAzktx82RPAM3j1KlTUSwW33VNw+OjXn19fdHb21vbHxsbi//85z8xZ86caGlpmcTJgEYrl8tRKpXi2LFj0dbWNtnjAA1UrVbj1KlT0d3d/Z5rGx4fc+fOjUsuuSSGh4fHHR8eHo6urq6z1hcKhSgUCuOOzZ49u9FjAReRtrY28QFT0Hu94/GWhj9wOmPGjLjuuuti165dtWNjY2Oxa9euWLJkSaNvBwA0mQn52KW3tzfWrVsXn/vc5+Lzn/98bN68OUZHR+OOO+6YiNsBAE1kQuLj61//evzrX/+KBx98MIaGhuKzn/1sDAwMnPUQKvDBUigU4qGHHjrro1bgg6Wl+n6+EwMA0CB+twsAkEp8AACpxAcAkEp8AACpxAeQav369bFmzZrJHgOYROIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEjVUq1Wq5M9BADwweGdDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFL9HxXGoosU7nOhAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -559,7 +551,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -639,7 +631,6 @@ "XIZIIIIII\n", "XIYIIIIII\n", "ZIXIIIIII\n", - "YIXIIIIII\n", "XIIZIIIII\n", "ZIIXIIIII\n" ] @@ -689,13 +680,12 @@ "YYIIIIIII\n", "ZIZIIIIII\n", "ZIYIIIIII\n", + "YIXIIIIII\n", "YIZIIIIII\n", - "YIYIIIIII\n", + "YIIZIIIII\n", "IZZIIIIII\n", - "IZYIIIIII\n", "IYZIIIIII\n", "IYYIIIIII\n", - "IIIZZIIII\n", "IIIZYIIII\n", "IIIYZIIII\n", "IIIYYIIII\n", @@ -706,11 +696,9 @@ "IIIIZZIII\n", "IIIIZYIII\n", "IIIIYZIII\n", - "IIIIYYIII\n", "IIIIIIZZI\n", "IIIIIIZYI\n", "IIIIIIYZI\n", - "IIIIIIYYI\n", "IIIIIIZIZ\n", "IIIIIIZIY\n", "IIIIIIYIZ\n", @@ -741,7 +729,7 @@ ], "metadata": { "kernelspec": { - "display_name": "mdopt-ZdbamFdU-py3.11", + "display_name": "mdopt-ZdbamFdU-py3.10", "language": "python", "name": "python3" }, @@ -757,12 +745,7 @@ "pygments_lexer": "ipython3", "version": "3.10.13" }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "64c06a7280c9749d5771a76ca6109d7df6b2615ddb3b9b0828f83fb315c7f8a2" - } - } + "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2