qiskit-documentation/translations/ja/verify/simulate-with-qiskit-aer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Exact and noisy simulation with Qiskit Aer primitives\n",
        "\n",
        "[Exact simulation with Qiskit primitives](simulate-with-qiskit-primitives) demonstrates how to use the reference primitives included with Qiskit to perform exact simulation of quantum circuits. Currently existing quantum processors suffer from errors, or noise, so the results of an exact simulation do not necessarily reflect the results you would expect when running circuits on real hardware. While the reference primitives in Qiskit do not support modeling noise, [Qiskit Aer](https://qiskit.org/ecosystem/aer/) includes implementations of the primitives that do support modeling noise. Qiskit Aer is a high-performance quantum circuit simulator that you can use in place of the reference primitives for better performance and more features. It is part of the [Qiskit Ecosystem](https://qiskit.github.io/ecosystem/). In this article, we demonstrate the use of Qiskit Aer primitives for exact and noisy simulation.\n",
        "\n",
        "Let's create an example circuit on 8 qubits."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1708.89x702.333 with 1 Axes>"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from qiskit.circuit.library import EfficientSU2\n",
        "\n",
        "n_qubits = 8\n",
        "circuit = EfficientSU2(n_qubits)\n",
        "circuit.decompose().draw(\"mpl\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This circuit contains parameters to represent the rotation angles for $R_y$ and $R_z$ gates. When simulating this circuit, we need to specify explicit values for these parameters. In the next cell, we specify some values for these parameters and use the [Estimator](https://qiskit.org/ecosystem/aer/stubs/qiskit_aer.primitives.Estimator.html) primitive from Qiskit Aer to compute the exact expectation value of the observable $ZZ \\cdots Z$.\n",
        "\n",
        "<Admonition type=\"note\">\n",
        "Setting `approximation=True` when initializing the Estimator tells Qiskit Aer to approximate the effect of sampling error rather than actually perform sampling. This makes the simulation much more efficient, and also allows us to calculate the exact expectation value, free of sampling error. After Qiskit Aer 0.14, this will be the default behavior, so we won't need to specify this argument.\n",
        "</Admonition>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_64852/3684797947.py:8: DeprecationWarning: ``qiskit_aer.primitives.estimator.Estimator.__init__()``'s argument ``approximation`` is deprecated as of qiskit-aer 0.13. It will be removed no earlier than 3 months after the release date. approximation=True will be default in the future.\n",
            "  exact_estimator = Estimator(approximation=True)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.8870140234256602"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from qiskit.quantum_info import SparsePauliOp\n",
        "from qiskit_aer.primitives import Estimator\n",
        "\n",
        "observable = SparsePauliOp(\"Z\" * n_qubits)\n",
        "params = [0.1] * circuit.num_parameters\n",
        "\n",
        "exact_estimator = Estimator(approximation=True)\n",
        "job = exact_estimator.run(circuit, observable, params)\n",
        "exact_value = job.result().values[0]\n",
        "exact_value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now, let's initialize a noise model that includes depolarizing error of 2% on every CX gate. In practice, the error arising from the two-qubit gates, which are CX gates here, are the dominant source of error when running a circuit. See [Building noise models](./building_noise_models) for an overview of constructing noise models in Qiskit Aer.\n",
        "\n",
        "In the next cell, we construct an Estimator that incorporates this noise model and use it to compute the expectation value of the observable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_64852/2637453528.py:9: DeprecationWarning: ``qiskit_aer.primitives.estimator.Estimator.__init__()``'s argument ``approximation`` is deprecated as of qiskit-aer 0.13. It will be removed no earlier than 3 months after the release date. approximation=True will be default in the future.\n",
            "  noisy_estimator = Estimator(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.7247404214143529"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from qiskit_aer.noise import NoiseModel, depolarizing_error\n",
        "\n",
        "noise_model = NoiseModel()\n",
        "cx_depolarizing_prob = 0.02\n",
        "noise_model.add_all_qubit_quantum_error(\n",
        "    depolarizing_error(cx_depolarizing_prob, 2), [\"cx\"]\n",
        ")\n",
        "\n",
        "noisy_estimator = Estimator(\n",
        "    backend_options={\"noise_model\": noise_model}, approximation=True\n",
        ")\n",
        "job = noisy_estimator.run(circuit, observable, params)\n",
        "noisy_value = job.result().values[0]\n",
        "noisy_value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As you can see, the expectation value in the presence of the noise is quite far from the correct value. In practice, you can employ a variety of error mitigation techniques to counter the effects of the noise, but a discussion of these techniques is outside the scope of this article.\n",
        "\n",
        "To get a very rough sense of how the noise affects the final result, consider our noise model, which adds a depolarizing error of 2% to each CX gate. Depolarizing error with probability $p$ is defined as a quantum channel $E$ that has the following action on a density matrix $\\rho$:\n",
        "\n",
        "$$\n",
        "E(\\rho) = (1 - p) \\rho + p\\frac{I}{2^n}\n",
        "$$\n",
        "\n",
        "where $n$ is the number of qubits, in this case, 2. That is, with probability $p$, the state is replaced with the completely mixed state, and the state is preserved with probability $1 - p$. After $m$ applications of the depolarizing channel, the probability of the state being preserved would be $(1 - p)^m$. Therefore, we expect the probability of retaining the correct state at the end of the simulation to go down exponentially with the number of CX gates in our circuit.\n",
        "\n",
        "Let's count the number of CX gates in our circuit and compute $(1 - p)^m$. Because our circuit uses the EfficientSU2 class, we'll need to call `decompose` once to decompose it into CX gates. We call `count_ops` to get a dictionary that maps gate names to counts, and retrieve the entry for the CX gate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6542558123199923"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cx_count = circuit.decompose().count_ops()[\"cx\"]\n",
        "(1 - cx_depolarizing_prob) ** cx_count"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This value, 65%, gives a rough estimate of the probability that our final state is correct. It is a conservative estimate because it does not take into account the initial state of the simulation. To get a more concrete estimate of how much our final state deviates from the correct state, let's use the [Sampler](https://qiskit.org/ecosystem/aer/stubs/qiskit_aer.primitives.Sampler.html) primitive to estimate the final measurement probability distributions with and without noise, and then compute the fidelity between these distributions. When running the Sampler, we pass `shots=None` to request a final distribution that does not include random sampling error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8917750028756636"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import math\n",
        "from qiskit.result import ProbDistribution\n",
        "from qiskit_aer.primitives import Sampler\n",
        "\n",
        "\n",
        "measured_circuit = circuit.copy()\n",
        "measured_circuit.measure_all()\n",
        "\n",
        "# Get exact probability distribution\n",
        "exact_sampler = Sampler()\n",
        "job = exact_sampler.run(measured_circuit, params, shots=None)\n",
        "exact_quasis = job.result().quasi_dists[0]\n",
        "exact_probs = exact_quasis.nearest_probability_distribution()\n",
        "\n",
        "# Get noisy probability distribution\n",
        "noisy_sampler = Sampler(backend_options={\"noise_model\": noise_model})\n",
        "job = noisy_sampler.run(measured_circuit, params, shots=None)\n",
        "noisy_quasis = job.result().quasi_dists[0]\n",
        "noisy_probs = noisy_quasis.nearest_probability_distribution()\n",
        "\n",
        "\n",
        "# Compute fidelity\n",
        "def fidelity(dist1: ProbDistribution, dist2: ProbDistribution) -> float:\n",
        "    result = 0\n",
        "    for bitstring in dist1 | dist2:\n",
        "        prob1 = dist1.get(bitstring, 0)\n",
        "        prob2 = dist2.get(bitstring, 0)\n",
        "        result += math.sqrt(prob1 * prob2)\n",
        "    return result**2\n",
        "\n",
        "\n",
        "fidelity(exact_probs, noisy_probs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Next steps\n",
        "\n",
        "<Admonition type=\"tip\" title=\"Recommendations\">\n",
        "  - To simulate small, simple circuits, see [Exact simulation with Qiskit primitives](simulate-with-qiskit-primitives).\n",
        "  - Review the [Qiskit Aer](https://qiskit.org/ecosystem/aer/) documentation.\n",
        "  - Learn how to run on a physical system in the [Run](../run) section.\n",
        "</Admonition>"
      ]
    }
  ],
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