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Minor presentation edits to notebooks
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"cell_type": "markdown",
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"source": [
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"# Resource Estimation for Tensor Network Contraction Simulation of a Fixed Circuit\n",
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"# Resource Estimation of Fixed Circuit Used in Tensor Network Contraction Simulation\n",
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"\n",
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"This notebook gives the resource estimation of random quantum circuits used for the tensor network contraction simulation in\n",
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"[Alibaba Cloud Quantum Development Platform: Large-Scale Classical Simulation of Quantum Circuits](https://arxiv.org/abs/1907.11217).\n",
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"\n",
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"## Getting started\n",
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"\n",
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"logical_qubits = result['physicalCounts']['breakdown']['algorithmicLogicalQubits']\n",
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"logical_depth = result['physicalCounts']['breakdown']['algorithmicLogicalDepth']\n",
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"# We take the runtime of the circuit from the paper https://arxiv.org/pdf/1907.11217.pdf, table 1.\n",
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"target_runtime = 412.25\n",
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"target_runtime = 580.7\n",
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"target_rqops = logical_qubits * logical_depth / target_runtime\n",
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"\n",
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"print(f\"Logical qubits = {logical_qubits}\")\n",
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{
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"cell_type": "markdown",
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"source": [
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"# Resource Estimation for State Vector Simulation of a Random Circuit\n",
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"# Resource Estimation of Random Circuit Used in State Vector Simulation\n",
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"\n",
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"This notebook gives the resource estimation of random quantum circuits used for the full state vector simulation in\n",
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"[Simulating 44-Qubit quantum circuits using AWS ParallelCluster](https://aws.amazon.com/blogs/hpc/simulating-44-qubit-quantum-circuits-using-aws-parallelcluster/).\n",
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"\n",
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"## Getting started\n",
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"\n",
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