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  • qiskit. org
    This code is a part of Qiskit © Copyright IBM 2017, 2020 This code is licensed under the Apache License, Version 2 0 You may obtain a copy of this license in the LICENSE txt file in the root directory
  • qiskit. org
    Particularly, it is shown how to setup the `MinimumEigenOptimizer` using `VQE` accordingly \n", "For a given set of shots with corresponding objective values of the considered optimization problem, the CVaR with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots \n", "Thus, $\\alpha = 1$ corresponds to
  • qiskit. org
    "from qiskit providers aer noise import NoiseModel\n", "from qiskit providers aer noise errors standard_errors import depolarizing_error, thermal_relaxation_error\n",
  • qiskit. org
    "The *Qiskit Aer* device noise model automatically generates a simplified noise model for a real device This model is generated using the calibration information reported in the `BackendProperties` of a device and takes into account\n",
  • qiskit. org
    A basic workflow using Qiskit consists of two stages: **Build** and **Execute** **Build** allows you to make different quantum circuits that represent the problem you are solving, and **Execute** that allows you to run them on different backends
  • ParallelExperiment. copy — Qiskit Experiments 0. 5 0. 5. 4 documentation
    ParallelExperiment copy() ¶ Return a copy of the experiment Return type:
  • qiskit. org
    "Assume that we apply some noise model from Qiskit Aer to the 5 qubits, \n", "then the calibration matrix will have most of its mass on the main diagonal, with some additional 'noise' \n",
  • qiskit. org
    "from qiskit import QuantumCircuit\n", "\n", "circ = QuantumCircuit(2, 2)\n", "circ h(0)\n", "circ cx(0, 1)\n", "circ measure([0, 1], [0, 1])\n", "\n", "circ draw(\"mpl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ It contains default calibrations for measurement and for its basis gates
  • qiskit. org
    "Logistics is a major industry, with some estimates valuing it at USD 8183 billion globally in 2015 Most service providers operate a number of vehicles (e g , trucks and container ships), a number of depots, where the vehicles are based overnight, and serve a number of client locations with each vehicle during each day There are many optimization and control problems that consider these





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