qiskit/advanced/aer/2_device_noise_simulation.ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qiskit_header.png](attachment:qiskit_header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Qiskit Aer: Device noise model simulations\n",
    "\n",
    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This notebook shows how to use the Qiskit Aer `noise.device` module to automatically generate a noise model for an IBMQ hardware device, and use this model to do noisy simulations of `QuantumCircuits` to study the effects of errors which occur on real devices.\n",
    "\n",
    "Note, that these automatic models are only a highly simplified approximation of the real errors that occur on actual devices. The study of quantum errors on real devices is an active area of research and we discuss the Qiskit Aer tools for configuring more detailed noise models in another notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T16:57:28.062507Z",
     "start_time": "2019-08-19T16:57:24.202560Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from qiskit import Aer, IBMQ, execute\n",
    "from qiskit.providers.aer import noise\n",
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "from qiskit.tools.monitor import job_monitor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Device noise module\n",
    "\n",
    "The *Qiskit Aer* device noise module contains two functions to automatically generate a simplified noise model for a real device. This model is generated using the calibration information reported in the `BackendProperties` of a device.\n",
    "\n",
    "### IBMQ Provider\n",
    "\n",
    "We will use a real hardware device in the `IBMQ` provider as an example. First we must load our account credentials, and then select a backend from the provider."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T16:57:31.724630Z",
     "start_time": "2019-08-19T16:57:28.076074Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmqx2') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_16_melbourne') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_vigo') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_ourense') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_london') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_burlington') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_essex') from IBMQ(hub='ibm-q', group='open', project='main')>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load IBMQ account\n",
    "provider = IBMQ.load_account()\n",
    "provider.backends()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the `ibmq_essex` device for this tutorial. We may get the properties of the backend using the `properties` method, the information in the returned `BackendProperties` object will be used to automatically generate a noise model for the device that can be used by the Qiskit Aer `QasmSimulator`. We will also want to get the `coupling_map` for the device from its `configuration` to use when compiling circuits for simulation to most closely mimic the gates that will be executed on a real device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T16:57:32.162207Z",
     "start_time": "2019-08-19T16:57:32.159466Z"
    }
   },
   "outputs": [],
   "source": [
    "device = provider.get_backend('ibmq_essex')\n",
    "properties = device.properties()\n",
    "coupling_map = device.configuration().coupling_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test circuit for device and simulation comparison\n",
    "\n",
    "Now we construct a test circuit to compare the output of the real device with the noisy output simulated on the Qiskit Aer `QasmSimulator`. We will prepare a 3-qubit GHZ state $\\frac{1}{2}(|0,0,0\\rangle + |1,1,1\\rangle)$ on qubits 0, 1 and 2. Before running with noise or on the device we show the ideal expected output with no noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T16:57:32.330124Z",
     "start_time": "2019-08-19T16:57:32.175615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct quantum circuit\n",
    "circ = QuantumCircuit(3, 3)\n",
    "circ.h(0)\n",
    "circ.cx(0, 1)\n",
    "circ.cx(1, 2)\n",
    "circ.measure([0,1,2], range(3))  # Can use list or generator like `range`.\n",
    "\n",
    "# Select the QasmSimulator from the Aer provider\n",
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "# Execute and get counts\n",
    "result = execute(circ, simulator).result()\n",
    "counts = result.get_counts(circ)\n",
    "plot_histogram(counts, title='Ideal counts for 3-qubit GHZ state')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating a device noise model\n",
    "\n",
    "Noise models in Qiskit Aer are presented using the `NoiseModel` object from the `aer.noise` module. The function we will use is the `basic_device_noise_model` function from `aer.noise.device` module which will return a basic approximate `NoiseModel` object configured from a device `BackendProperties`.\n",
    "\n",
    "### Basic device noise model\n",
    "\n",
    "The `basic_device_noise_model` constructs an approximate noise model consisting of:\n",
    "\n",
    "* **Single-qubit gate errors** consisting of a single qubit depolarizing error followed by a single qubit thermal relaxation error.\n",
    "* **Two-qubit gate errors** consisting of a two-qubit depolarizing error followed by single-qubit thermal relaxation errors on both qubits in the gate.\n",
    "* **Single-qubit readout errors** on the classical bit value obtained from measurements on individual qubits.\n",
    "\n",
    "For the gate errors the error parameter of the thermal relaxation errors is derived using the `thermal_relaxation_error` function from `aer.noise.errors` module, along with the individual qubit $T_1$ and $T_2$ parameters, and the `gate_time` parameter from the device backend properties. The probability of the depolarizing error is then set so that the combined average gate infidelity from the depolarizing error followed by the thermal relaxation is equal to the `gate_error` value from the backend properties.\n",
    "\n",
    "For the readout errors the probability that the recorded classical bit value will be flipped from the true outcome after a measurement is given by the qubit `readout_errors`.\n",
    "\n",
    "Let us construct the device noise model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T16:57:34.232936Z",
     "start_time": "2019-08-19T16:57:34.125212Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NoiseModel:\n",
      "  Basis gates: ['cx', 'id', 'u2', 'u3']\n",
      "  Instructions with noise: ['measure', 'cx', 'u2', 'id', 'u3']\n",
      "  Qubits with noise: [0, 1, 2, 3, 4]\n",
      "  Specific qubit errors: [('id', [0]), ('id', [1]), ('id', [2]), ('id', [3]), ('id', [4]), ('u2', [0]), ('u2', [1]), ('u2', [2]), ('u2', [3]), ('u2', [4]), ('u3', [0]), ('u3', [1]), ('u3', [2]), ('u3', [3]), ('u3', [4]), ('cx', [0, 1]), ('cx', [1, 0]), ('cx', [1, 2]), ('cx', [1, 3]), ('cx', [2, 1]), ('cx', [3, 1]), ('cx', [3, 4]), ('cx', [4, 3]), ('measure', [0]), ('measure', [1]), ('measure', [2]), ('measure', [3]), ('measure', [4])]\n"
     ]
    }
   ],
   "source": [
    "# Construct the noise model from backend properties\n",
    "noise_model = noise.device.basic_device_noise_model(properties)\n",
    "print(noise_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating a quantum circuit with noise\n",
    "\n",
    "To use this noise model we must make use of several keyword arguments in the `execute` function. These are:\n",
    "\n",
    "* `noise_model`: This passes the noise model to the `QasmSimulator.run` method for noisy simulation.\n",
    "* `basis_gates`: A noise model is defined with respect to specific gates, we must pass these basis gates to the Qiskit compiler so that it compiles the circuit to the correct gates for the noise model. The basis gates of a noise model may be obtained from the `NoiseModel.basis_gates` property.\n",
    "* `coupling_map`: We also must make sure we provide the `coupling_map` for the real device so that the compiler will produce a `Qobj` for the simulator that will match the compiled experiment that can be executed on the real device.\n",
    "\n",
    "### Local Simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T16:57:36.864987Z",
     "start_time": "2019-08-19T16:57:36.672055Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the basis gates for the noise model\n",
    "basis_gates = noise_model.basis_gates\n",
    "\n",
    "# Select the QasmSimulator from the Aer provider\n",
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "# Execute noisy simulation and get counts\n",
    "result_noise = execute(circ, simulator, \n",
    "                       noise_model=noise_model,\n",
    "                       coupling_map=coupling_map,\n",
    "                       basis_gates=basis_gates).result()\n",
    "counts_noise = result_noise.get_counts(circ)\n",
    "plot_histogram(counts_noise, title=\"Counts for 3-qubit GHZ state with depolarizing noise model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cloud Simulator\n",
    "\n",
    "It is also possible to run noisy simulations on the `ibmq_qasm_simulator`, which is hosted online, and is part of the `IBMQ` provider."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T16:57:46.160483Z",
     "start_time": "2019-08-19T16:57:38.913520Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the ibmq_qasm_simulator from the IBMQ provider\n",
    "simulator = provider.get_backend('ibmq_qasm_simulator')\n",
    "\n",
    "# Execute noisy simulation and get counts\n",
    "result_noise = execute(circ, simulator, \n",
    "                       noise_model=noise_model,\n",
    "                       coupling_map=coupling_map,\n",
    "                       basis_gates=basis_gates).result()\n",
    "counts_noise = result_noise.get_counts(circ)\n",
    "plot_histogram(counts_noise, title=\"3-qubit GHZ state with depolarizing noise model on IBMQ qasm simulator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison to the real device\n",
    "\n",
    "Now we will execute the circuit on the real device to see the effect of the actual noise processes on the output counts. Note that this execution may take some time to return the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:12:16.614580Z",
     "start_time": "2019-08-19T16:57:46.194353Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "# Submit job to real device and wait for results\n",
    "job_device = execute(circ, device)\n",
    "job_monitor(job_device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:12:16.619630Z",
     "start_time": "2019-08-19T16:57:41.471Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get results from completed execution\n",
    "result_device = job_device.result()\n",
    "counts_device = result_device.get_counts(0)\n",
    "plot_histogram(counts_device, title='Counts for 3-qubit GHZ state on IBMQ device: {}'.format(device.name()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might notice that the counts returned in the above histogram don't look exactly like our simulation. This is because the real errors that happen on a device can be very complicated, and characterizing them to create accurate models is an active area of quantum computing research. The *basic device noise model* we used for our simulation is a simplified error model that only takes into account *local* errors occurring on the qubits participating in each individual gate, and treats the errors as a relaxation process combined with an error operation which acts to randomize the state of the qubit with some probability *p* (the depolarizing probability). While this model is an approximation, due to its simplicity it is a useful starting point for studying the effects of noise on quantum computations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For those who'd like to know what these noise model look like, here is one printed as a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'errors': [{'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.00829792+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.00829978+0.j]]), array([[0.        +0.j, 0.02119276+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[-0.99996557-0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.99974096+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[0]]}, {'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.0093747 +0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.00937931+0.j]]), array([[0.        +0.j, 0.03132168+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[-0.99995606-0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.99946535+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[1]]}, {'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[0.        +0.j, 0.02920292+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[1.       +0.j, 0.       +0.j],\n",
      "       [0.       +0.j, 0.9995735+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[2]]}, {'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.00960233+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.00960487+0.j]]), array([[0.        +0.j, 0.02296866+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[-0.9999539 -0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.99969005+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[3]]}, {'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.00797361+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.00797606+0.j]]), array([[0.        +0.j, 0.02479484+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[0.99996821+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99966074+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[4]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.01037088+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.01037452+0.j]]), array([[0.        +0.j, 0.02648928+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[0.99994622+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99959526+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[0]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.01171485+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.01172384+0.j]]), array([[0.        +0.j, 0.03914669+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[-0.99993138-0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.9991647 +0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[1]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[0.        +0.j, 0.03649927+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[-1.        -0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.99933368+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[2]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.01200082+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.01200577+0.j]]), array([[0.       +0.j, 0.0287087+0.j],\n",
      "       [0.       +0.j, 0.       +0.j]]), array([[0.99992799+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99951572+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[3]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.0099651 +0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.00996989+0.j]]), array([[0.        +0.j, 0.03099088+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[0.99995035+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99946994+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[4]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.0146607+0.j,  0.       +0.j],\n",
      "       [ 0.       +0.j, -0.014671 +0.j]]), array([[0.        +0.j, 0.03745492+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[0.99989253+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99919062+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[0]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.01655345+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.01657887+0.j]]), array([[0.        +0.j, 0.05534057+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[0.99986298+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99832989+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[1]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[0.        +0.j, 0.05160057+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[1.       +0.j, 0.       +0.j],\n",
      "       [0.       +0.j, 0.9986678+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[2]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.0169635+0.j,  0.       +0.j],\n",
      "       [ 0.       +0.j, -0.0169775+0.j]]), array([[0.        +0.j, 0.04059187+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[0.99985611+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99903156+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[3]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.01408531+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.01409886+0.j]]), array([[0.        +0.j, 0.04381719+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[-0.9999008 -0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.99894008+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[4]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[ 4.47751897e-04+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-3.78582647e-18+0.j, -4.48333825e-04+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j, -4.49024607e-04+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         4.49608188e-04+0.j]]), array([[ 0.00000000e+00+0.j, -1.14435579e-03+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 2.86087015e-20+0.j, -1.41934455e-32+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-4.62297763e-17+0.j, -5.30658489e-16+0.j,  6.03995949e-33+0.j,\n",
      "         1.14760856e-03+0.j],\n",
      "       [-9.39316574e-17+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         8.19789382e-33+0.j]]), array([[ 0.00000000e+00+0.j,  3.31124042e-31+0.j, -1.49805540e-03+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 6.17037275e-22+0.j, -2.30265727e-33+0.j,  0.00000000e+00+0.j,\n",
      "         1.50000237e-03+0.j],\n",
      "       [-2.88341801e-17+0.j,  5.64596000e-18+0.j, -3.84014979e-33+0.j,\n",
      "        -3.34425288e-31+0.j],\n",
      "       [-2.58105561e-17+0.j, -1.81200646e-16+0.j,  0.00000000e+00+0.j,\n",
      "         6.15985422e-33+0.j]]), array([[ 0.00000000e+00+0.j, -4.81362378e-33+0.j, -6.26714940e-32+0.j,\n",
      "         3.82870154e-03+0.j],\n",
      "       [-1.01022244e-23+0.j,  3.76657720e-35+0.j,  0.00000000e+00+0.j,\n",
      "         6.29286440e-32+0.j],\n",
      "       [ 4.72077409e-19+0.j, -9.44648456e-20+0.j,  3.03724148e-36+0.j,\n",
      "         4.84126697e-33+0.j],\n",
      "       [ 4.22574195e-19+0.j,  8.98633549e-19+0.j,  1.36152962e-18+0.j,\n",
      "        -4.08542537e-35+0.j]]), array([[-1.99159769e-02-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-3.51298025e-15+0.j,  1.99418610e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 6.62826462e-18+0.j,  0.00000000e+00+0.j, -1.98595273e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         1.98853380e-02+0.j]]), array([[-2.24719056e-02-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 3.78516407e-15+0.j, -2.24427376e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-4.99362014e-18+0.j,  0.00000000e+00+0.j,  2.25357807e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         2.25065298e-02+0.j]]), array([[ 0.00000000e+00+0.j,  5.09008755e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.20197950e-18+0.j,  1.00712848e-31+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 4.83807809e-18+0.j, -7.98059002e-18+0.j, -1.04872801e-31+0.j,\n",
      "         5.07566026e-02+0.j],\n",
      "       [-8.82056788e-18+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         3.85620350e-33+0.j]]), array([[ 0.00000000e+00+0.j,  2.06430852e-30+0.j, -7.51848508e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.32844888e-23+0.j, -4.35930378e-35+0.j,  0.00000000e+00+0.j,\n",
      "        -7.50872625e-02+0.j],\n",
      "       [-6.20784771e-19+0.j, -8.77871280e-18+0.j, -4.30656911e-35+0.j,\n",
      "        -2.07098927e-30+0.j],\n",
      "       [-5.55687732e-19+0.j, -6.02789364e-18+0.j,  0.00000000e+00+0.j,\n",
      "         8.69151287e-35+0.j]]), array([[-0.99954898-0.j,  0.        +0.j,  0.        +0.j,\n",
      "         0.        +0.j],\n",
      "       [ 0.        +0.j, -0.99825159+0.j,  0.        +0.j,\n",
      "         0.        +0.j],\n",
      "       [ 0.        +0.j,  0.        +0.j, -0.99671587+0.j,\n",
      "         0.        +0.j],\n",
      "       [ 0.        +0.j,  0.        +0.j,  0.        +0.j,\n",
      "        -0.99542216+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[0, 1]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[ 8.17822463e-04+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 2.58844949e-16+0.j, -8.22093398e-04+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j, -8.19773668e-04+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         8.24054793e-04+0.j]]), array([[ 0.00000000e+00+0.j, -1.10061834e-29+0.j, -2.09166025e-03+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 3.18407838e-19+0.j, -2.32417918e-33+0.j,  4.25250458e-16+0.j,\n",
      "         2.10258358e-03+0.j],\n",
      "       [-9.78761684e-17+0.j,  8.94208069e-17+0.j,  2.57219385e-32+0.j,\n",
      "         1.10222734e-29+0.j],\n",
      "       [ 4.60366414e-16+0.j,  2.53445105e-16+0.j,  0.00000000e+00+0.j,\n",
      "        -2.35409700e-32+0.j]]), array([[ 0.00000000e+00+0.j,  2.74006581e-03+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-5.40459073e-20+0.j, -7.47468581e-34+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 3.69696036e-16+0.j, -9.39635724e-16+0.j,  1.46967820e-32+0.j,\n",
      "        -2.74660321e-03+0.j],\n",
      "       [ 3.91602709e-16+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -1.40539512e-32+0.j]]), array([[ 0.00000000e+00+0.j, -3.89756332e-32+0.j,  4.07022899e-31+0.j,\n",
      "        -7.00798400e-03+0.j],\n",
      "       [ 1.14228426e-21+0.j, -1.32960704e-34+0.j,  1.33095139e-18+0.j,\n",
      "        -4.09055354e-31+0.j],\n",
      "       [-3.51129569e-19+0.j,  3.23239024e-19+0.j,  1.96104808e-34+0.j,\n",
      "         3.91017955e-32+0.j],\n",
      "       [ 1.65155894e-18+0.j,  1.28610784e-18+0.j,  1.37513316e-17+0.j,\n",
      "        -6.38150911e-35+0.j]]), array([[-2.69285127e-02-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.57125232e-14+0.j, -2.67886138e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 8.95640690e-18+0.j,  0.00000000e+00+0.j,  2.69927602e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         2.68525275e-02+0.j]]), array([[ 3.03451166e-02+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 8.24888148e-15+0.j, -3.05035886e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 6.74755131e-18+0.j,  0.00000000e+00+0.j,  3.02728899e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -3.04309847e-02+0.j]]), array([[ 0.00000000e+00+0.j, -3.45562797e-31+0.j, -6.88722823e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.47869380e-20+0.j, -2.96709026e-33+0.j, -2.15710647e-17+0.j,\n",
      "        -6.85144775e-02+0.j],\n",
      "       [ 7.61931782e-18+0.j, -1.39898509e-17+0.j, -7.62745286e-35+0.j,\n",
      "         3.49194397e-31+0.j],\n",
      "       [-3.58379173e-17+0.j,  1.81469376e-17+0.j,  0.00000000e+00+0.j,\n",
      "         3.04915359e-33+0.j]]), array([[ 0.00000000e+00+0.j, -1.01669519e-01+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 4.93383690e-18+0.j,  1.45627676e-30+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 6.16456074e-17+0.j, -3.18881367e-17+0.j, -6.72208195e-31+0.j,\n",
      "        -1.01427528e-01+0.j],\n",
      "       [ 3.19100085e-17+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -7.83188046e-31+0.j]]), array([[-9.99176341e-01-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.21022966e-16+0.j, -9.93985426e-01+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j, -9.96798126e-01+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -9.91619566e-01+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[1, 0]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[ 0.00000000e+00+0.j, -6.12699230e-20+0.j,  2.32666995e-03+0.j,\n",
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      "         9.91814203e-01+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[1, 2]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[ 7.25770905e-04+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
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      "        -2.66366941e-02+0.j]]), array([[ 2.72774787e-02+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
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      "        -2.72272523e-02+0.j]]), array([[ 0.00000000e+00+0.j,  6.56922854e-31+0.j, -6.53303478e-02+0.j,\n",
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      "        -8.36743445e-34+0.j]]), array([[ 0.00000000e+00+0.j,  8.90156094e-02+0.j,  0.00000000e+00+0.j,\n",
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      "         0.00000000e+00+0.j],\n",
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      "         2.11205013e-30+0.j]]), array([[-9.99273991e-01-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.21205539e-16+0.j, -9.95298484e-01+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j, -9.97134524e-01+0.j,\n",
      "         0.00000000e+00+0.j],\n",
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      "        -9.93167528e-01+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[1, 3]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[ 0.00000000e+00+0.j,  2.62929350e-03+0.j,  0.00000000e+00+0.j,\n",
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      "        -7.79164571e-22+0.j]]), array([[-2.90119304e-02-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
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      "         2.90301689e-02+0.j]]), array([[ 0.00000000e+00+0.j,  9.05898608e-02+0.j,  0.00000000e+00+0.j,\n",
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      "        -1.78348772e-18+0.j]]), array([[ 0.00000000e+00+0.j,  3.90060870e-30+0.j,  9.71763493e-02+0.j,\n",
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      "        -0.99074625+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[2, 1]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[ 7.25770905e-04+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
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      "        -8.95439822e-33+0.j],\n",
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      "         3.85414644e-35+0.j]]), array([[-2.65876475e-02-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
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      "         2.66366941e-02+0.j]]), array([[ 2.72774787e-02+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
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      "        -2.72272523e-02+0.j]]), array([[ 0.00000000e+00+0.j, -6.53303478e-02+0.j,  0.00000000e+00+0.j,\n",
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      "       [ 1.01679479e-17+0.j, -6.48588190e-30+0.j,  0.00000000e+00+0.j,\n",
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      "        -6.68409467e-31+0.j]]), array([[ 0.00000000e+00+0.j,  8.21509481e-31+0.j, -8.90156094e-02+0.j,\n",
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      "        -8.88250251e-02+0.j],\n",
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      "        -8.27207872e-31+0.j],\n",
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      "        -4.07004830e-33+0.j]]), array([[-9.99273991e-01-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.21205539e-16+0.j, -9.97134524e-01+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j, -9.95298484e-01+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -9.93167528e-01+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[3, 1]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[ 5.50737933e-04+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 5.56951077e-17+0.j, -5.51789810e-04+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.22541491e-19+0.j,  0.00000000e+00+0.j, -5.51963463e-04+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         5.53017681e-04+0.j]]), array([[ 0.00000000e+00+0.j,  1.31882332e-03+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 2.87282637e-19+0.j, -4.64776291e-32+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-4.20021303e-17+0.j,  2.89387968e-16+0.j,  5.58146889e-32+0.j,\n",
      "        -1.32175803e-03+0.j],\n",
      "       [ 5.21772110e-17+0.j,  2.07297971e-19+0.j,  0.00000000e+00+0.j,\n",
      "        -9.38293324e-33+0.j]]), array([[ 0.00000000e+00+0.j,  1.93388636e-28+0.j,  1.71456788e-03+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.22698907e-21+0.j, -2.10683012e-34+0.j, -1.42948643e-15+0.j,\n",
      "        -1.71784261e-03+0.j],\n",
      "       [-8.95932244e-19+0.j,  5.60645903e-16+0.j, -3.56747283e-35+0.j,\n",
      "        -1.93732831e-28+0.j],\n",
      "       [ 8.15668564e-19+0.j,  2.16132746e-16+0.j,  0.00000000e+00+0.j,\n",
      "         2.47339014e-34+0.j]]), array([[ 0.00000000e+00+0.j, -1.23169874e-31+0.j,  5.72221488e-31+0.j,\n",
      "        -4.10578601e-03+0.j],\n",
      "       [-9.04391819e-25+0.j, -1.14748157e-37+0.j,  1.60758873e-18+0.j,\n",
      "        -5.73151541e-31+0.j],\n",
      "       [-6.60375722e-22+0.j,  1.12486101e-18+0.j, -9.93791128e-38+0.j,\n",
      "         1.23250002e-31+0.j],\n",
      "       [ 6.01214791e-22+0.j, -1.39022615e-18+0.j,  4.62253765e-18+0.j,\n",
      "         2.14651550e-37+0.j]]), array([[ 2.13760640e-02+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 4.67124153e-15+0.j,  2.13353148e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.30547909e-17+0.j,  0.00000000e+00+0.j, -2.14236311e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -2.13827912e-02+0.j]]), array([[ 2.57497991e-02+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-8.21778034e-15+0.j, -2.57989797e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.14334335e-17+0.j,  0.00000000e+00+0.j,  2.56926266e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -2.57416980e-02+0.j]]), array([[ 0.00000000e+00+0.j,  6.16616969e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 3.98933700e-18+0.j, -4.34436445e-31+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 3.91206141e-18+0.j, -1.93414292e-17+0.j,  6.36377103e-31+0.j,\n",
      "         6.15247888e-02+0.j],\n",
      "       [ 9.81436443e-18+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -2.02346203e-31+0.j]]), array([[ 0.00000000e+00+0.j,  6.24656441e-27+0.j,  6.65483717e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.90225072e-20+0.j, -1.79059571e-33+0.j,  5.21937761e-17+0.j,\n",
      "         6.64215105e-02+0.j],\n",
      "       [-1.38899995e-17+0.j, -1.11068982e-17+0.j, -2.36653183e-33+0.j,\n",
      "        -6.26046658e-27+0.j],\n",
      "       [ 1.26456393e-17+0.j, -5.64289494e-18+0.j,  0.00000000e+00+0.j,\n",
      "         4.16736130e-33+0.j]]), array([[-9.99439697e-01-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.21462776e-16+0.j, -9.97534465e-01+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j, -9.97220631e-01+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -9.95319629e-01+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[3, 4]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[ 8.70965245e-04+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 4.22207275e-16+0.j, -8.74042405e-04+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.94029079e-19+0.j,  0.00000000e+00+0.j, -8.73606780e-04+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         8.76693273e-04+0.j]]), array([[ 0.00000000e+00+0.j, -3.81159286e-30+0.j,  2.08723732e-03+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 2.82763778e-20+0.j,  6.49363486e-33+0.j,  0.00000000e+00+0.j,\n",
      "        -2.09461163e-03+0.j],\n",
      "       [ 5.53934539e-17+0.j, -7.42447546e-17+0.j, -4.67439410e-33+0.j,\n",
      "         3.82129077e-30+0.j],\n",
      "       [-1.20081934e-17+0.j,  2.68358652e-16+0.j,  5.17721192e-16+0.j,\n",
      "        -1.81165613e-33+0.j]]), array([[ 0.00000000e+00+0.j,  2.71365119e-03+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-3.74080359e-19+0.j,  3.55525453e-32+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 2.67165452e-16+0.j, -8.12891931e-17+0.j, -1.94156168e-31+0.j,\n",
      "        -2.72188138e-03+0.j],\n",
      "       [ 5.24772524e-18+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         1.59129122e-31+0.j]]), array([[ 0.00000000e+00+0.j,  1.85093852e-29+0.j,  3.53883578e-19+0.j,\n",
      "        -6.50316886e-03+0.j],\n",
      "       [-1.37053800e-19+0.j,  6.20410765e-33+0.j,  0.00000000e+00+0.j,\n",
      "        -3.55649964e-19+0.j],\n",
      "       [-2.68488539e-16+0.j,  3.63918526e-16+0.j,  2.32426917e-32+0.j,\n",
      "        -1.85812051e-29+0.j],\n",
      "       [ 5.82029477e-17+0.j,  5.49289052e-17+0.j,  2.06671238e-17+0.j,\n",
      "        -2.96697980e-32+0.j]]), array([[-2.68747684e-02-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.85961616e-14+0.j,  2.69697181e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.34300637e-17+0.j,  0.00000000e+00+0.j, -2.67935068e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         2.68881695e-02+0.j]]), array([[ 3.23795709e-02+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 6.78543042e-15+0.j,  3.22655752e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-7.18795595e-18+0.j,  0.00000000e+00+0.j, -3.24777744e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -3.23634329e-02+0.j]]), array([[ 0.00000000e+00+0.j, -1.12745584e-31+0.j,  7.75964935e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 2.34827624e-21+0.j, -5.27374924e-35+0.j,  0.00000000e+00+0.j,\n",
      "         7.73233067e-02+0.j],\n",
      "       [ 4.60027563e-18+0.j,  7.66758965e-18+0.j, -4.12737296e-34+0.j,\n",
      "         1.12023751e-31+0.j],\n",
      "       [-9.97247789e-19+0.j, -3.47343091e-17+0.j, -1.21619394e-17+0.j,\n",
      "         4.69256984e-34+0.j]]), array([[ 0.00000000e+00+0.j,  8.37332462e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.08644317e-17+0.j,  1.13677021e-30+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-7.62928554e-18+0.j,  0.00000000e+00+0.j, -3.75488913e-30+0.j,\n",
      "         8.34800609e-02+0.j],\n",
      "       [-2.97216391e-17+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         2.62502211e-30+0.j]]), array([[-9.99113883e-01-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.21123114e-16+0.j, -9.95596395e-01+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j, -9.96092850e-01+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -9.92585998e-01+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[4, 3]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.99, 0.01], [0.06999999999999995, 0.93]], 'gate_qubits': [[0]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.71, 0.29000000000000004], [0.07, 0.9299999999999999]], 'gate_qubits': [[1]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.98, 0.020000000000000018], [0.05, 0.95]], 'gate_qubits': [[2]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.98, 0.02], [0.053333333333333344, 0.9466666666666667]], 'gate_qubits': [[3]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.8733333333333333, 0.12666666666666668], [0.05666666666666664, 0.9433333333333334]], 'gate_qubits': [[4]]}], 'x90_gates': []}\n"
     ]
    }
   ],
   "source": [
    "print(noise_model.to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>7.9987335205078125</td></tr><tr><td colspan='2'>Tue Dec 10 16:36:38 2019 Eastern Standard Time</td></tr></table>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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