qiskit/terra/reduced_backends.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T19:15:01.604612Z",
     "start_time": "2019-05-02T19:15:01.599898Z"
    }
   },
   "source": [
    "<img src=\"../../images/qiskit-heading.gif\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" width=\"500 px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with reduced backends and noise models.\n",
    "\n",
    "In this tutorial we will see how to target a sub-graph of a quantum device by reducing the coupling map from a device backend and performing noise simulations on this sub-graph by reducing the noise model for the backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:05:24.621074Z",
     "start_time": "2019-05-03T00:05:24.617712Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "from qiskit.compiler import transpile\n",
    "from qiskit.transpiler.coupling import CouplingMap\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load IBMQ accounts and grab target device backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:05:28.918109Z",
     "start_time": "2019-05-03T00:05:25.196368Z"
    }
   },
   "outputs": [],
   "source": [
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:05:28.925372Z",
     "start_time": "2019-05-03T00:05:28.920588Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device has: 14 qubits\n"
     ]
    }
   ],
   "source": [
    "backend = provider.get_backend('ibmq_16_melbourne')\n",
    "print('Device has:', backend.configuration().n_qubits, 'qubits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem setup\n",
    "\n",
    "Suppose we have a quantum circuit that we want to run on a given quantum device that is smaller than the full size of the device, and which we want to map to a selected subset of qubits in the device layout.  As an example, lets consider the standard Bell state circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:05:39.554974Z",
     "start_time": "2019-05-03T00:05:39.550262Z"
    }
   },
   "outputs": [],
   "source": [
    "q = QuantumRegister(2)\n",
    "c = ClassicalRegister(2)\n",
    "qc = QuantumCircuit(q, c)\n",
    "\n",
    "qc.h(q[0])\n",
    "qc.cx(q[0], q[1])\n",
    "qc.measure(q, c);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "that we wish to run on qubits 11 and 12 of the target device.  We will call these qubits the `target_qubits`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:05:40.968275Z",
     "start_time": "2019-05-03T00:05:40.965738Z"
    }
   },
   "outputs": [],
   "source": [
    "target_qubits = [11, 12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standard transpilation\n",
    "\n",
    "Lets transpile the circuit for the given backend, making sure to map the circuit to the `target_qubits`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:05:47.724431Z",
     "start_time": "2019-05-03T00:05:46.836003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;line-height: 15px;\">                                       \n",
       " q_0: |0>──────────────────────────────\n",
       "                                       \n",
       " q_1: |0>──────────────────────────────\n",
       "                                       \n",
       " q_2: |0>──────────────────────────────\n",
       "                                       \n",
       " q_3: |0>──────────────────────────────\n",
       "                                       \n",
       " q_4: |0>──────────────────────────────\n",
       "                                       \n",
       " q_5: |0>──────────────────────────────\n",
       "                                       \n",
       " q_6: |0>──────────────────────────────\n",
       "                                       \n",
       " q_7: |0>──────────────────────────────\n",
       "                                       \n",
       " q_8: |0>──────────────────────────────\n",
       "                                       \n",
       " q_9: |0>──────────────────────────────\n",
       "                                       \n",
       "q_10: |0>──────────────────────────────\n",
       "         ┌──────────────┐      ░ ┌─┐   \n",
       "q_11: |0>┤ U2(0,3.1416) ├──■───░─┤M├───\n",
       "         └──────────────┘┌─┴─┐ ░ └╥┘┌─┐\n",
       "q_12: |0>────────────────┤ X ├─░──╫─┤M├\n",
       "                         └───┘ ░  ║ └╥┘\n",
       "q_13: |0>─────────────────────────╫──╫─\n",
       "                                  ║  ║ \n",
       " c1_0: 0 ═════════════════════════╩══╬═\n",
       "                                     ║ \n",
       " c1_1: 0 ════════════════════════════╩═\n",
       "                                       </pre>"
      ],
      "text/plain": [
       "<qiskit.visualization.text.TextDrawing at 0x1a17552978>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_qc = transpile(qc, backend, initial_layout=target_qubits)\n",
    "new_qc.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the circuit gets transformed so that the number of qubits in the circuit is equal to the number of qubits in the device, and the gates did indeed get mapped to the `target_qubits`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above example also highlights one of the downsides of transpiling circuits that are much smaller than the full size of the device; The transpiled circuit is padded with qubits that play no role in computation.  As such, it may be of interest to extract the sub-graph of the device defined by the `target_qubits` and use that in the transpilation process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reducing the device coupling map\n",
    "\n",
    "A reduced coupling map is a map that takes the sub-graph of a device defined by `target_qubits` and returns the same sub-graph coupling map, but with qubits labeled from 0->(N-1) where N is the number of qubits in `target_qubits`.  First we need to get the device coupling map and create a `CouplingMap` object from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:15:29.052339Z",
     "start_time": "2019-05-03T00:15:29.048499Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 0], [1, 2], [2, 3], [4, 3], [4, 10], [5, 4], [5, 6], [5, 9], [6, 8], [9, 8], [9, 10], [7, 8], [11, 3], [11, 10], [11, 12], [12, 2], [13, 1], [13, 12]]\n"
     ]
    }
   ],
   "source": [
    "cmap = backend.configuration().coupling_map\n",
    "CMAP = CouplingMap(cmap)\n",
    "print(CMAP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To reduce the coupling map defined by `target_qubits` we make use of the `CouplingMap.reduce()` method, and pass `target_qubits` to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:15:29.961285Z",
     "start_time": "2019-05-03T00:15:29.949402Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 1]]\n"
     ]
    }
   ],
   "source": [
    "red_cmap = CMAP.reduce(target_qubits)\n",
    "print(red_cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are returned a `CouplingMap` instance that shows the controlled-x gate connectivity between the `target_qubits` with the indices of the qubits reduced.  This can now be passed to the transpile function to generate a new circuit that has the same number of qubits as the original circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:18:03.992182Z",
     "start_time": "2019-05-03T00:18:03.978469Z"
    }
   },
   "outputs": [],
   "source": [
    "red_qc = transpile(qc, None, coupling_map=red_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:18:04.324546Z",
     "start_time": "2019-05-03T00:18:04.319574Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;line-height: 15px;\">        ┌───┐      ░ ┌─┐   \n",
       "q_0: |0>┤ H ├──■───░─┤M├───\n",
       "        └───┘┌─┴─┐ ░ └╥┘┌─┐\n",
       "q_1: |0>─────┤ X ├─░──╫─┤M├\n",
       "             └───┘ ░  ║ └╥┘\n",
       "c1_0: 0 ══════════════╩══╬═\n",
       "                         ║ \n",
       "c1_1: 0 ═════════════════╩═\n",
       "                           </pre>"
      ],
      "text/plain": [
       "<qiskit.visualization.text.TextDrawing at 0x104a41128>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_qc.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Executing a circuit transpiled with a reduced coupling map on a device\n",
    "\n",
    "To `execute` a circuit compiled with a reduced layout on the actual device, one simply passes the `target_qubits` list as the `initial_layout`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:37:49.754345Z",
     "start_time": "2019-05-03T00:37:18.376876Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "job_device = execute(red_qc, backend, initial_layout=target_qubits)\n",
    "job_monitor(job_device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:37:50.248852Z",
     "start_time": "2019-05-03T00:37:49.756706Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(job_device.result().get_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Noise modelling using reduced noise models\n",
    "\n",
    "We have just seen how to take the `coupling_map` of a device, reduce a given sub-graph of that device, and use it in the transpiling process and then execution on the device.  We will now show how to do a noisy simulation using the reduced circuit and a noise model that is also reduced in size.  The combination of reduced coupling and noise maps allows us to easily simulate sub-graphs of large, e.g. >50 qubit, devices in the presence of noise.\n",
    "\n",
    "First we need to import the noise library from the Aer provider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:33:27.345073Z",
     "start_time": "2019-05-03T00:33:27.342005Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit.providers.aer import noise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we build a simplified noise model of the full device using data from the backend parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:33:28.672600Z",
     "start_time": "2019-05-03T00:33:27.717308Z"
    }
   },
   "outputs": [],
   "source": [
    "properties = backend.properties()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:36:45.073188Z",
     "start_time": "2019-05-03T00:36:45.062620Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoiseModel:\n",
       "  Basis gates: ['cx', 'id', 'u2', 'u3']\n",
       "  Instructions with noise: ['u2', 'u3', 'measure', 'cx']\n",
       "  Qubits with noise: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n",
       "  Specific qubit errors: [('u2', [0]), ('u2', [1]), ('u2', [2]), ('u2', [3]), ('u2', [4]), ('u2', [5]), ('u2', [6]), ('u2', [7]), ('u2', [8]), ('u2', [9]), ('u2', [10]), ('u2', [11]), ('u2', [12]), ('u2', [13]), ('u3', [0]), ('u3', [1]), ('u3', [2]), ('u3', [3]), ('u3', [4]), ('u3', [5]), ('u3', [6]), ('u3', [7]), ('u3', [8]), ('u3', [9]), ('u3', [10]), ('u3', [11]), ('u3', [12]), ('u3', [13]), ('cx', [1, 0]), ('cx', [1, 2]), ('cx', [2, 3]), ('cx', [4, 3]), ('cx', [4, 10]), ('cx', [5, 4]), ('cx', [5, 6]), ('cx', [5, 9]), ('cx', [6, 8]), ('cx', [7, 8]), ('cx', [9, 8]), ('cx', [9, 10]), ('cx', [11, 3]), ('cx', [11, 10]), ('cx', [11, 12]), ('cx', [12, 2]), ('cx', [13, 1]), ('cx', [13, 12]), ('measure', [0]), ('measure', [1]), ('measure', [2]), ('measure', [3]), ('measure', [4]), ('measure', [5]), ('measure', [6]), ('measure', [7]), ('measure', [8]), ('measure', [9]), ('measure', [10]), ('measure', [11]), ('measure', [12]), ('measure', [13])]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_model = noise.device.basic_device_noise_model(properties)\n",
    "basis_gates = noise_model.basis_gates\n",
    "noise_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we reduce the full noise model down using `remapping=target_qubits`, and `discard_qubits=True`.  This latter keyword argument trims the noise model down to just those noise terms that correspond to the reduced target qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:33:28.689908Z",
     "start_time": "2019-05-03T00:33:28.683091Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoiseModel:\n",
       "  Basis gates: ['cx', 'id', 'u2', 'u3']\n",
       "  Instructions with noise: ['u2', 'u3', 'measure', 'cx']\n",
       "  Qubits with noise: [0, 1]\n",
       "  Specific qubit errors: [('u2', [0]), ('u2', [1]), ('u3', [0]), ('u3', [1]), ('cx', [0, 1]), ('measure', [0]), ('measure', [1])]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_noise_model = noise.utils.remap_noise_model(noise_model,\n",
    "                                                remapping=target_qubits,\n",
    "                                                discard_qubits=True)\n",
    "red_noise_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify reduced circuit and noise model give same results as full example\n",
    "\n",
    "To show that the reduced and full noise models give the same answer we fix the random number generator in the simulator using the `seed_simulator` keyword argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:36:18.478741Z",
     "start_time": "2019-05-03T00:36:18.476025Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_backend = Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reduced model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:36:19.141451Z",
     "start_time": "2019-05-03T00:36:19.095690Z"
    }
   },
   "outputs": [],
   "source": [
    "job = execute(red_qc, sim_backend,\n",
    "              basis_gates = basis_gates,\n",
    "              noise_model=red_noise_model,\n",
    "              seed_simulator=123456)\n",
    "res = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:36:19.703105Z",
     "start_time": "2019-05-03T00:36:19.608809Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(res.get_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Full model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:36:31.839480Z",
     "start_time": "2019-05-03T00:36:31.778333Z"
    }
   },
   "outputs": [],
   "source": [
    "job_full = execute(new_qc, sim_backend,\n",
    "              basis_gates = basis_gates,\n",
    "              noise_model=noise_model,\n",
    "              seed_simulator=123456)\n",
    "res_full = job_full.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-03T00:36:32.510079Z",
     "start_time": "2019-05-03T00:36:32.419099Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(res_full.get_counts())"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}