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 backend 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` module to automatically generate a basic 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 an *approximation* of the real errors that occur on actual devices, due to the fact that they must be build from a limited set of input parameters related to *average error rates* on gates. 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 import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "from qiskit.providers.aer.noise import NoiseModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Device Backend Noise Model\n",
    "\n",
    "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",
    "\n",
    "* The *gate_error* probability of each basis gate on each qubit.\n",
    "* The *gate_length* of each basis gate on each qubit.\n",
    "* The $T_1$, $T_2$ relaxation time constants of each qubit.\n",
    "* The readout error probabilies of each qubit.\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')>,\n",
       " <IBMQBackend('ibmq_armonk') 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_vigo` device for this tutorial. 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": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T16:57:32.162207Z",
     "start_time": "2019-08-19T16:57:32.159466Z"
    }
   },
   "outputs": [],
   "source": [
    "device_backend = provider.get_backend('ibmq_vigo')\n",
    "\n",
    "# The device coupling map is needed for transpiling to correct\n",
    "# CNOT gates before simulation\n",
    "coupling_map = device_backend.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": 4,
   "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": 4,
     "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], [0, 1, 2])\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 backend noise model\n",
    "\n",
    "Noise models in Qiskit Aer are presented using the `NoiseModel` object from the `noise` module. The `NoiseModel` class has a `from_backend` method which will return a basic approximate `NoiseModel` object configured from a devices `BackendProperties`.\n",
    "\n",
    "### Backend noise model\n",
    "\n",
    "The `NoiseModel.from_backend` method 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": 5,
   "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: ['cx', 'u2', 'u3', 'measure', 'id']\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 = NoiseModel.from_backend(device_backend)\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": 6,
   "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": 6,
     "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": 7,
   "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": 7,
     "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": 8,
   "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_backend)\n",
    "job_monitor(job_device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "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_backend.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": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'errors': [{'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9987991024031746, 0.0009125766557770015, 0.0002883209410483589], 'gate_qubits': [[0]]}, {'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9994618453913152, 5.24484565160637e-05, 0.0004857061521686967], 'gate_qubits': [[1]]}, {'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.0059288 +0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.00592985+0.j]]), array([[0.        +0.j, 0.01882053+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[-0.99998242-0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.99980529+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[2]]}, {'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9996532331285638, 5.002636029838658e-05, 0.0002967405111378163], 'gate_qubits': [[3]]}, {'type': 'qerror', 'operations': ['id'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9994731408677977, 0.00020454222013641828, 0.00032231691206585644], 'gate_qubits': [[4]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9987991024031746, 0.0009125766557770015, 0.0002883209410483589], 'gate_qubits': [[0]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9994618453913152, 5.24484565160637e-05, 0.0004857061521686967], 'gate_qubits': [[1]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.0059288 +0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.00592985+0.j]]), array([[0.        +0.j, 0.01882053+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[-0.99998242-0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.99980529+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[2]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9996532331285638, 5.002636029838658e-05, 0.0002967405111378163], 'gate_qubits': [[3]]}, {'type': 'qerror', 'operations': ['u2'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9994731408677977, 0.00020454222013641828, 0.00032231691206585644], 'gate_qubits': [[4]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9976004797575401, 0.0018229614893283455, 0.0005765587531315619], 'gate_qubits': [[0]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9989239831438541, 0.00010484046227492216, 0.0009711763938710094], 'gate_qubits': [[1]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.00838295+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.00838592+0.j]]), array([[0.        +0.j, 0.02661389+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[0.99996486+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99961061+0.j]])]}]], 'probabilities': [1.0], 'gate_qubits': [[2]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9993065890070274, 0.00010001802562791711, 0.0005933929673447302], 'gate_qubits': [[3]]}, {'type': 'qerror', 'operations': ['u3'], 'instructions': [[{'name': 'id', 'qubits': [0]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}]], 'probabilities': [0.9989466011536605, 0.0004088689103996175, 0.0006445299359398859], 'gate_qubits': [[4]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'id', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}], [{'name': 'z', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}]], 'probabilities': [0.9831288504526653, 0.008802706927623886, 0.002807116116304525, 0.0005055952337943374, 4.526982058400407e-06, 1.4436200590160304e-06, 0.004694325122139923, 4.2031894653639317e-05, 1.3403650700988807e-05], 'gate_qubits': [[0, 1]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'id', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}], [{'name': 'z', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}]], 'probabilities': [0.9814278287301046, 0.0005562225225295958, 0.005165913543621727, 0.009684102127743538, 5.488448112274838e-06, 5.097393091477534e-05, 0.0030914462680049714, 1.752071819360755e-06, 1.627235714920568e-05], 'gate_qubits': [[1, 0]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [1], 'params': [array([[ 0.01498303+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.01500003+0.j]]), array([[0.        +0.j, 0.04758983+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[0.99988775+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99875433+0.j]])]}], [{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[-1.49830282e-02-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.22196656e-14+0.j,  1.49830282e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  1.50000315e-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",
      "        -1.50000315e-02+0.j]]), array([[ 0.        +0.j,  0.        +0.j, -0.04758983+0.j,\n",
      "         0.        +0.j],\n",
      "       [ 0.        +0.j,  0.        +0.j,  0.        +0.j,\n",
      "         0.04758983+0.j],\n",
      "       [ 0.        +0.j,  0.        +0.j,  0.        +0.j,\n",
      "         0.        +0.j],\n",
      "       [ 0.        +0.j,  0.        +0.j,  0.        +0.j,\n",
      "         0.        +0.j]]), array([[-9.99887748e-01-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 4.43787762e-16+0.j,  9.99887748e-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, -9.98754328e-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",
      "         9.98754328e-01+0.j]])], 'param': [array([[-4.68248981e-04-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.50862788e-15+0.j, -4.39311733e-03+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.78665407e-16+0.j,  0.00000000e+00+0.j,  4.68780365e-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",
      "         4.39810279e-03+0.j]]), array([[ 0.00000000e+00+0.j,  1.44467185e-31+0.j,  1.48727533e-03+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.43661310e-20+0.j,  4.33150975e-33+0.j, -1.94742452e-19+0.j,\n",
      "         1.39536343e-02+0.j],\n",
      "       [-2.15131801e-19+0.j, -3.40477166e-20+0.j, -2.60124718e-36+0.j,\n",
      "        -1.44629266e-31+0.j],\n",
      "       [-2.14205737e-19+0.j, -1.62230234e-19+0.j, -1.55793961e-18+0.j,\n",
      "        -4.38637835e-33+0.j]]), array([[ 0.00000000e+00+0.j, -1.42352995e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.01092717e-17+0.j, -2.06796904e-30+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.20615953e-16+0.j,  1.61484000e-15+0.j,  1.40674427e-33+0.j,\n",
      "         1.42514542e-02+0.j],\n",
      "       [-6.80536551e-17+0.j, -2.23634222e-18+0.j,  0.00000000e+00+0.j,\n",
      "         2.08878682e-30+0.j]]), array([[ 1.49757096e-02+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-5.48908685e-16+0.j, -1.59621522e-03+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.01441637e-16+0.j,  0.00000000e+00+0.j, -1.49927045e-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",
      "         1.59802666e-03+0.j]]), array([[ 0.00000000e+00+0.j,  2.03289082e-33+0.j, -1.56543828e-29+0.j,\n",
      "        -4.52148551e-02+0.j],\n",
      "       [-2.83579451e-22+0.j, -8.58313755e-35+0.j, -2.24002958e-18+0.j,\n",
      "        -1.47966740e-28+0.j],\n",
      "       [ 4.24658234e-21+0.j, -4.89506248e-21+0.j, -2.15826986e-38+0.j,\n",
      "        -2.03533897e-33+0.j],\n",
      "       [ 4.22830234e-21+0.j, -1.51127753e-17+0.j, -8.62458056e-18+0.j,\n",
      "         8.60914801e-35+0.j]]), array([[ 0.00000000e+00+0.j,  5.23755997e-30+0.j,  4.75665819e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 2.24737460e-19+0.j,  6.79923682e-32+0.j,  0.00000000e+00+0.j,\n",
      "        -5.06997692e-03+0.j],\n",
      "       [-3.36542767e-18+0.j, -4.91367353e-18+0.j,  5.40006421e-35+0.j,\n",
      "        -5.24266584e-30+0.j],\n",
      "       [-3.35094072e-18+0.j,  8.16199741e-18+0.j,  0.00000000e+00+0.j,\n",
      "        -6.79268746e-32+0.j]]), array([[3.12484507e-02+0.j, 0.00000000e+00+0.j, 0.00000000e+00+0.j,\n",
      "        0.00000000e+00+0.j],\n",
      "       [1.09880392e-16+0.j, 2.93173324e-01+0.j, 0.00000000e+00+0.j,\n",
      "        0.00000000e+00+0.j],\n",
      "       [4.62654282e-17+0.j, 0.00000000e+00+0.j, 3.12130291e-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",
      "        2.92840998e-01+0.j]]), array([[ 0.00000000e+00+0.j, -9.49988303e-01+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.07372232e-16+0.j, -1.83317977e-29+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-3.28932918e-18+0.j,  2.98144866e-16+0.j,  6.15208233e-33+0.j,\n",
      "        -9.48911446e-01+0.j],\n",
      "       [ 1.27209236e-17+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         1.83125899e-29+0.j]]), array([[ 0.99939934+0.j,  0.        +0.j,  0.        +0.j,\n",
      "         0.        +0.j],\n",
      "       [ 0.        +0.j, -0.10652293+0.j,  0.        +0.j,\n",
      "         0.        +0.j],\n",
      "       [ 0.        +0.j,  0.        +0.j,  0.99826647+0.j,\n",
      "         0.        +0.j],\n",
      "       [ 0.        +0.j,  0.        +0.j,  0.        +0.j,\n",
      "        -0.10640218+0.j]])]}]], 'probabilities': [0.9965608584457192, 0.003439141554280862], 'gate_qubits': [[1, 2]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'id', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}], [{'name': 'z', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}]], 'probabilities': [0.9876851637854267, 0.0007256262423920402, 0.006745627903576234, 0.0006919839586162474, 5.083823652486917e-07, 4.726067041625613e-06, 0.004115234346504566, 3.023354146550188e-06, 2.8105959930736895e-05], 'gate_qubits': [[1, 3]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 0.01610797+0.j,  0.        +0.j],\n",
      "       [ 0.        +0.j, -0.01612911+0.j]]), array([[0.        +0.j, 0.05116837+0.j],\n",
      "       [0.        +0.j, 0.        +0.j]]), array([[0.99987026+0.j, 0.        +0.j],\n",
      "       [0.        +0.j, 0.99855979+0.j]])]}], [{'name': 'kraus', 'qubits': [0, 1], 'params': [array([[-1.61079686e-02-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.12648379e-14+0.j,  1.61291081e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j,  1.61079686e-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",
      "        -1.61291081e-02+0.j]]), array([[ 0.00000000e+00+0.j,  5.11683669e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.66302722e-18+0.j, -8.01986294e-18+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 9.31181498e-19+0.j,  0.00000000e+00+0.j,  3.87896309e-31+0.j,\n",
      "        -5.11683669e-02+0.j],\n",
      "       [ 2.20196519e-18+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -8.01986294e-18+0.j]]), array([[-9.99870258e-01-0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 4.43740705e-16+0.j, -9.98559788e-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,  9.99870258e-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",
      "         9.98559788e-01+0.j]])], 'param': [array([[ 5.03203348e-04+0.j, -3.67936951e-18+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-5.41659074e-15+0.j, -5.03863733e-04+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-1.12295743e-16+0.j,  0.00000000e+00+0.j,  4.72242363e-03+0.j,\n",
      "         3.72480011e-18+0.j],\n",
      "       [-5.78533724e-39+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -4.72862116e-03+0.j]]), array([[ 0.00000000e+00+0.j, -1.59846932e-03+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 7.00154704e-16+0.j,  5.91754867e-22+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.15946805e-14+0.j,  0.00000000e+00+0.j,  5.28812452e-18+0.j,\n",
      "        -1.50011905e-02+0.j],\n",
      "       [-1.64869500e-15+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -5.29173511e-18+0.j]]), array([[ 0.00000000e+00+0.j, -7.99484259e-35+0.j, -1.53043197e-02+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 2.94808186e-20+0.j,  5.86116272e-36+0.j,  0.00000000e+00+0.j,\n",
      "         1.53244045e-02+0.j],\n",
      "       [ 5.54350585e-19+0.j,  3.71870473e-18+0.j,  2.27794526e-32+0.j,\n",
      "        -3.65570596e-34+0.j],\n",
      "       [ 3.90987455e-18+0.j,  1.99449056e-15+0.j,  4.31667317e-15+0.j,\n",
      "        -2.28167903e-32+0.j]]), array([[ 1.61001068e-02+0.j,  1.33730837e-18+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.96893584e-15+0.j, -1.61212359e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 4.10194967e-17+0.j,  0.00000000e+00+0.j, -1.71556562e-03+0.j,\n",
      "        -1.35386213e-18+0.j],\n",
      "       [-1.42517999e-37+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "         1.71781706e-03+0.j]]), array([[ 0.00000000e+00+0.j,  1.00045641e-32+0.j,  7.67903690e-18+0.j,\n",
      "         4.86155061e-02+0.j],\n",
      "       [ 2.41975315e-21+0.j,  5.66587611e-37+0.j,  0.00000000e+00+0.j,\n",
      "        -7.69311201e-18+0.j],\n",
      "       [ 4.13919189e-20+0.j, -1.77544746e-15+0.j,  1.86161182e-33+0.j,\n",
      "         9.15705780e-32+0.j],\n",
      "       [ 7.32939786e-20+0.j,  1.14026428e-17+0.j, -1.33196885e-17+0.j,\n",
      "        -1.86601622e-33+0.j]]), array([[ 0.00000000e+00+0.j,  5.11433932e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-2.09183947e-17+0.j,  2.32221949e-21+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-9.50930652e-18+0.j,  0.00000000e+00+0.j,  2.20501754e-17+0.j,\n",
      "        -5.44964381e-03+0.j],\n",
      "       [ 1.60566538e-18+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -2.20667236e-17+0.j]]), array([[-3.12353516e-02-0.j,  6.45484998e-20+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [-9.82921929e-17+0.j, -3.11944132e-02+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  0.00000000e+00+0.j, -2.93135097e-01+0.j,\n",
      "        -7.30810524e-20+0.j],\n",
      "       [-2.69592271e-35+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -2.92750902e-01+0.j]]), array([[ 0.00000000e+00+0.j, -2.37973392e-34+0.j, -9.49985345e-01+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 1.05515560e-18+0.j,  4.82300375e-34+0.j,  0.00000000e+00+0.j,\n",
      "        -9.48740256e-01+0.j],\n",
      "       [ 1.98405953e-17+0.j, -8.08519994e-19+0.j,  8.26674353e-31+0.j,\n",
      "         6.54093736e-33+0.j],\n",
      "       [ 1.39920658e-16+0.j, -2.71103024e-16+0.j,  0.00000000e+00+0.j,\n",
      "        -8.24588475e-31+0.j]]), array([[ 9.99382252e-01+0.j, -1.76740596e-20+0.j,  0.00000000e+00+0.j,\n",
      "         0.00000000e+00+0.j],\n",
      "       [ 0.00000000e+00+0.j,  9.98072422e-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, -1.06490339e-01+0.j,\n",
      "         1.76511847e-20+0.j],\n",
      "       [-8.40302563e-37+0.j,  0.00000000e+00+0.j,  0.00000000e+00+0.j,\n",
      "        -1.06350769e-01+0.j]])]}]], 'probabilities': [0.9960245721811906, 0.003975427818809491], 'gate_qubits': [[2, 1]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'id', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}], [{'name': 'z', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}]], 'probabilities': [0.9885597095129962, 0.0006431254991130516, 0.003823913469007349, 0.0006743924256405235, 4.3873825846269863e-07, 2.6086621323802115e-06, 0.006267490566345749, 4.077429982103131e-06, 2.4243696524242103e-05], 'gate_qubits': [[3, 1]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'id', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}], [{'name': 'z', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}]], 'probabilities': [0.9933820391526242, 0.0003778151832083881, 0.0022440173300229344, 0.0015450459126611891, 5.876307216661602e-07, 3.4902078626772105e-06, 0.00244056320615534, 9.282247901842337e-07, 5.513151953402167e-06], 'gate_qubits': [[3, 4]]}, {'type': 'qerror', 'operations': ['cx'], 'instructions': [[{'name': 'id', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}], [{'name': 'reset', 'qubits': [0]}], [{'name': 'z', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], [{'name': 'reset', 'qubits': [1]}], [{'name': 'z', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}], [{'name': 'reset', 'qubits': [0]}, {'name': 'reset', 'qubits': [1]}]], 'probabilities': [0.9925147110681822, 0.0017468145445662928, 0.0027602875761804585, 0.00042715442441728556, 7.517869035360819e-07, 1.1879612842822139e-06, 0.0025375692989712995, 4.4660929554559005e-06, 7.057246539055333e-06], 'gate_qubits': [[4, 3]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.972, 0.028], [0.04600000000000004, 0.954]], 'gate_qubits': [[0]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.994, 0.006], [0.02200000000000002, 0.978]], 'gate_qubits': [[1]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.996, 0.004], [0.02400000000000002, 0.976]], 'gate_qubits': [[2]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.986, 0.014], [0.016000000000000014, 0.984]], 'gate_qubits': [[3]]}, {'type': 'roerror', 'operations': ['measure'], 'probabilities': [[0.982, 0.018], [0.03600000000000003, 0.964]], 'gate_qubits': [[4]]}], 'x90_gates': []}\n"
     ]
    }
   ],
   "source": [
    "print(noise_model.to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.12.0.dev0+62adde9</td></tr><tr><td>Aer</td><td>0.4.0</td></tr><tr><td>Ignis</td><td>0.3.0.dev0+829207f</td></tr><tr><td>Aqua</td><td>0.7.0.dev0+2776c5d</td></tr><tr><td>IBM Q Provider</td><td>0.4.6rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 14:38:56) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Thu Feb 06 16:19:40 2020 PST</td></tr></table>"
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       "<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, 2020.</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>"
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       "<IPython.core.display.HTML object>"
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   "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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