qiskit/basics/getting_started_with_qiskit.ipynb

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    "<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\">"
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   "cell_type": "markdown",
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   "source": [
    "# Getting Started with Qiskit\n",
    "\n",
    "Here, we provide an overview of working with Qiskit. Qiskit provides the basic building blocks necessary to program quantum computers. The basic concept of Qiskit is an array of quantum circuits. A workflow using Qiskit consists of two stages: **Build** and **Execute**. **Build** allows you to make different quantum circuits that represent the problem you are solving, and **Execute** allows you to run them on different backends.  After the jobs have been run, the data is collected. There are methods for putting this data together, depending on the program. This either gives you the answer you wanted, or allows you to make a better program for the next instance.\n",
    "\n",
    "\n",
    "**Contents**\n",
    "\n",
    "[Circuit basics](#circuit_basics)\n",
    "\n",
    "[Simulating circuits with Qiskit Aer](#aer_simulation)\n",
    "\n",
    "[Running circuits using the IBMQ provider](#ibmq_provider)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    "ExecuteTime": {
     "end_time": "2018-09-29T00:04:16.313210Z",
     "start_time": "2018-09-29T00:04:14.460647Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister\n",
    "from qiskit import execute"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circuit Basics <a id='circuit_basics'></a>\n",
    "\n",
    "\n",
    "### Building the circuit\n",
    "\n",
    "The basic elements needed for your first program are the QuantumCircuit, and QuantumRegister."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
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   "outputs": [],
   "source": [
    "# Create a Quantum Register with 3 qubits.\n",
    "q = QuantumRegister(3, 'q')\n",
    "\n",
    "# Create a Quantum Circuit acting on the q register\n",
    "circ = QuantumCircuit(q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> Naming the QuantumRegister is optional and not required.\n",
    "</div>\n",
    "\n",
    "After you create the circuit with its registers, you can add gates (\"operations\") to manipulate the registers. As you proceed through the documentation you will find more gates and circuits; the below is an example of a quantum circuit that makes a three-qubit GHZ state\n",
    "\n",
    "$$|\\psi\\rangle = \\left(|000\\rangle+|111\\rangle\\right)/\\sqrt{2}.$$\n",
    "\n",
    "To create such a state, we start with a 3-qubit quantum register. By default, each qubit in the register is initialized to $|0\\rangle$. To make the GHZ state, we apply the following gates:\n",
    "* A Hadamard gate $H$ on qubit 0, which puts it into a superposition state.\n",
    "* A controlled-Not operation ($C_{X}$) between qubit 0 and qubit 1.\n",
    "* A controlled-Not operation between qubit 0 and qubit 2.\n",
    "\n",
    "On an ideal quantum computer, the state produced by running this circuit would be the GHZ state above.\n",
    "\n",
    "In Qiskit, operations can be added to the circuit one-by-one, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:04:20.807281Z",
     "start_time": "2018-09-29T00:04:20.796420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.extensions.standard.cx.CnotGate at 0x11c0f2be0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a H gate on qubit 0, putting this qubit in superposition.\n",
    "circ.h(q[0])\n",
    "# Add a CX (CNOT) gate on control qubit 0 and target qubit 1, putting\n",
    "# the qubits in a Bell state.\n",
    "circ.cx(q[0], q[1])\n",
    "# Add a CX (CNOT) gate on control qubit 0 and target qubit 2, putting\n",
    "# the qubits in a GHZ state.\n",
    "circ.cx(q[0], q[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Circuit\n",
    "\n",
    "You can visualize your circuit using Qiskit `QuantumCircuit.draw()`, which plots circuit in the form found in many textbooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:04:26.904758Z",
     "start_time": "2018-09-29T00:04:25.509141Z"
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    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;line-height: 15px;\">        ┌───┐          \n",
       "q_0: |0>┤ H ├──■────■──\n",
       "        └───┘┌─┴─┐  │  \n",
       "q_1: |0>─────┤ X ├──┼──\n",
       "             └───┘┌─┴─┐\n",
       "q_2: |0>──────────┤ X ├\n",
       "                  └───┘</pre>"
      ],
      "text/plain": [
       "<qiskit.tools.visualization._text.TextDrawing at 0x10629f978>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this circuit, the qubits are put in order with qubit zero at the top and qubit two at the bottom. The circuit is read left-to-right (meaning that gates which are applied earlier in the circuit show up further to the left)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating circuits using Qiskit Aer <a id='aer_simulation'></a>\n",
    "\n",
    "Qiskit Aer is our package for simulating quantum circuits. It provides many different backends for doing a simulation. Here we use the basic python version.\n",
    "\n",
    "### Statevector backend\n",
    "\n",
    "The most common backend in Qiskit Aer is the `statevector_simulator`. This simulator returns the quantum \n",
    "state which is a complex vector of dimensions $2^n$ where $n$ is the number of qubits \n",
    "(so be careful using this as it will quickly get too large to run on your machine)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "\n",
    "When representing the state of a multi-qubit system, the tensor order used in qiskit is different than that use in most physics textbooks. Suppose there are $n$ qubits, and qubit $j$ is labeled as $Q_{j}$. In most textbooks (such as Nielsen and Chuang's \"Quantum Computation and Information\"), the basis vectors for the $n$-qubit state space would be labeled as $Q_{0}\\otimes Q_{1} \\otimes \\cdots \\otimes Q_{n}$. **This is not the ordering used by qiskit!** Instead, qiskit uses an ordering in which the $n^{\\mathrm{th}}$ qubit is on the _left_ side of the tesnsor product, so that the basis vectors are labeled as  $Q_n\\otimes \\cdots  \\otimes  Q_1\\otimes Q_0$.\n",
    "\n",
    "For example, if qubit zero is in state 0, qubit 1 is in state 0, and qubit 2 is in state 1, qiskit would represent this state as $|100\\rangle$, whereas most physics textbooks would represent it as $|001\\rangle$.\n",
    "\n",
    "This difference in labeling affects the way multi-qubit operations are represented as matrices. For example, qiskit represents a controlled-X ($C_{X}$) operation with qubit 0 being the control and qubit 1 being the target as\n",
    "\n",
    "$$C_X = \\begin{pmatrix} 1 & 0 & 0 & 0 \\\\  0 & 0 & 0 & 1 \\\\ 0 & 0 & 1 & 0 \\\\ 0 & 1 & 0 & 0 \\\\\\end{pmatrix}.$$\n",
    "\n",
    "</div>\n",
    "\n",
    "To run the above circuit using the statevector simulator, first you need to import Aer and then set the backend to `statevector_simulator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Aer\n",
    "from qiskit import BasicAer\n",
    "\n",
    "# Run the quantum circuit on a statevector simulator backend\n",
    "backend = BasicAer.get_backend('statevector_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have chosen the backend it's time to compile and run the quantum circuit. In Qiskit we provide the `execute` function for this. ``execute`` returns a ``job`` object that encapsulates information about the job submitted to the backend.\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> You can obtain the above parameters in Jupyter. Simply place the text cursor on a function and press Shift+Tab.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Quantum Program for execution \n",
    "job = execute(circ, backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you run a program, a job object is made that has the following two useful methods: \n",
    "`job.status()` and `job.result()` which return the status of the job and a result object respectively.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> Jobs run asynchronously but when the result method is called it switches to synchronous and waits for it to finish before moving on to another task.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results object contains the data and Qiskit provides the method \n",
    "`result.get_statevector(circ)` to return the state vector for the quantum circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.707+0.j 0.   +0.j 0.   +0.j 0.   +0.j 0.   +0.j 0.   +0.j 0.   +0.j\n",
      " 0.707+0.j]\n"
     ]
    }
   ],
   "source": [
    "outputstate = result.get_statevector(circ, decimals=3)\n",
    "print(outputstate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qiskit also provides a visualization toolbox to allow you to view these results.\n",
    "\n",
    "Below, we use the visualization function to plot the real and imaginary components of the state vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import plot_state_city\n",
    "plot_state_city(outputstate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unitary backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qiskit Aer also includes a `unitary_simulator` that works _provided all the elements in the circuit are unitary operations_. This backend calculates the $2^n \\times 2^n$ matrix representing the gates in the quantum circuit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.707+0.j  0.707+0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.707+0.j -0.707+0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.707+0.j  0.707+0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.707+0.j -0.707+0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.707+0.j  0.707+0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.707+0.j -0.707+0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.707+0.j  0.707+0.j]\n",
      " [ 0.707+0.j -0.707+0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]]\n"
     ]
    }
   ],
   "source": [
    "# Run the quantum circuit on a unitary simulator backend\n",
    "backend = BasicAer.get_backend('unitary_simulator')\n",
    "job = execute(circ, backend)\n",
    "result = job.result()\n",
    "\n",
    "# Show the results\n",
    "print(result.get_unitary(circ, decimals=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OpenQASM backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulators above are useful because they provide information about the state output by the ideal circuit and the matrix representation of the circuit. However, a real experiment terminates by _measuring_ each qubit (usually in the computational $|0\\rangle, |1\\rangle$ basis). Without measurement, we cannot gain information about the state. Measurements cause the quantum system to collapse into classical bits. \n",
    "\n",
    "For example, suppose we make independent measurements on each qubit of the three-qubit GHZ state\n",
    "$$|\\psi\\rangle = |000\\rangle +|111\\rangle)/\\sqrt{2},$$\n",
    "and let $xyz$ denote the bitstring that results. Recall that, under the qubit labeling used by Qiskit, $x$ would correspond to the outcome on qubit 2, $y$ to the outcome on qubit 1, and $z$ to the outcome on qubit 0. This representation of the bitstring puts the most significant bit (MSB) on the left, and the least significant bit (LSB) on the right. This is the standard ordering of binary bitstrings. We order the qubits in the same way, which is why Qiskit uses a non-standard tensor product order.\n",
    "\n",
    "The probability of obtaining outcome $xyz$ is given by\n",
    "$$\\mathrm{Pr}(xyz) = |\\langle xyz | \\psi \\rangle |^{2}.$$\n",
    "By explicit computation, we see there are only two bitstrings that will occur: $000$ and $111$. If the bitstring $000$ is obtained, the state of the qubits is $|000\\rangle$, and if the bitstring is $111$, the qubits are left in the state $|111\\rangle$. The probability of obtaining 000 or 111 is the same; namely, 1/2:\n",
    "$$\\begin{align}\n",
    "\\mathrm{Pr}(000) &= |\\langle 000 | \\psi \\rangle |^{2} = \\frac{1}{2}\\\\\n",
    "\\mathrm{Pr}(111) &= |\\langle 111 | \\psi \\rangle |^{2} = \\frac{1}{2}.\n",
    "\\end{align}$$\n",
    "\n",
    "To simulate a circuit that includes measurement, we need to add measurements to the original circuit above, and use a different Aer backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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",
       "q_2: |0>──────────┤ X ├─░──╫──╫─┤M├\n",
       "                  └───┘ ░  ║  ║ └╥┘\n",
       " c_0: 0 ═══════════════════╩══╬══╬═\n",
       "                              ║  ║ \n",
       " c_1: 0 ══════════════════════╩══╬═\n",
       "                                 ║ \n",
       " c_2: 0 ═════════════════════════╩═\n",
       "                                   </pre>"
      ],
      "text/plain": [
       "<qiskit.tools.visualization._text.TextDrawing at 0x119f7abe0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Classical Register with 3 bits.\n",
    "c = ClassicalRegister(3, 'c')\n",
    "# Create a Quantum Circuit\n",
    "meas = QuantumCircuit(q, c)\n",
    "meas.barrier(q)\n",
    "# map the quantum measurement to the classical bits\n",
    "meas.measure(q,c)\n",
    "\n",
    "# The Qiskit circuit object supports composition using\n",
    "# the addition operator.\n",
    "qc = circ+meas\n",
    "\n",
    "#drawing the circuit\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This circuit adds a classical register, and three measurements that are used to map the outcome of qubits to the classical bits. \n",
    "\n",
    "To simulate this circuit, we use the ``qasm_simulator`` in Qiskit Aer. Each run of this circuit will yield either the bitstring 000 or 111. To build up statistics about the distribution of the bitstrings (to, e.g., estimate $\\mathrm{Pr}(000)$), we need to repeat the circuit many times. The number of times the circuit is repeated can be specified in the ``execute`` function, via the ``shots`` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:04:28.595093Z",
     "start_time": "2018-09-29T00:04:28.589162Z"
    }
   },
   "outputs": [],
   "source": [
    "# Use Aer's qasm_simulator\n",
    "backend_sim = BasicAer.get_backend('qasm_simulator')\n",
    "\n",
    "# Execute the circuit on the qasm simulator.\n",
    "# We've set the number of repeats of the circuit\n",
    "# to be 1024, which is the default.\n",
    "job_sim = execute(qc, backend_sim, shots=1024)\n",
    "\n",
    "# Grab the results from the job.\n",
    "result_sim = job_sim.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have a result object, you can access the counts via the function `get_counts(circuit)`. This gives you the _aggregated_ binary outcomes of the circuit you submitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:04:30.559484Z",
     "start_time": "2018-09-29T00:04:30.513862Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'000': 526, '111': 498}\n"
     ]
    }
   ],
   "source": [
    "counts = result_sim.get_counts(qc)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approximately 50 percent of the time the output bitstring is 000. Qiskit also provides a function `plot_histogram` which allows you to view the outcomes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import plot_histogram\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimated outcome probabilities $\\mathrm{Pr}(000)$ and  $\\mathrm{Pr}(111)$ are computed by taking the aggregate counts and dividing by the number of shots (times the circuit was repeated). Try changing the ``shots`` keyword in the ``execute`` function and see how the estimated probabilities change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running circuits using the IBMQ provider <a id='ibmq_provider'></a>\n",
    "\n",
    "To faciliate access to real quantum computing hardware, we have provided a simple API interface.\n",
    "To access IBMQ devices, you'll need an API token. For the public IBM Q devices, you can generate an API token [here](https://quantumexperience.ng.bluemix.net/qx/account/advanced) (create an account if you don't already have one). For Q Network devices, login to the q-console, click your hub, group, and project, and expand \"Get Access\" to generate your API token and access url.\n",
    "\n",
    "Our IBMQ provider lets you run your circuit on real devices or on our HPC simulator. Currently, this provider exists within Qiskit, and can be imported as shown below. For details on the provider, see [The IBMQ Provider](the_ibmq_provider.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:04:37.950277Z",
     "start_time": "2018-09-29T00:04:37.947819Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import IBMQ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After generating your API token, call, `IBMQ.save_account('MY_TOKEN')`. For Q Network users, you'll also need to include your access url: `IBMQ.save_account('MY_TOKEN', 'URL')`\n",
    "\n",
    "This will store your IBMQ credentials in a local file.  Unless your registration information has changed, you only need to do this once.  You may now load your accounts by calling,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:04:41.166242Z",
     "start_time": "2018-09-29T00:04:40.072340Z"
    }
   },
   "outputs": [],
   "source": [
    "IBMQ.load_accounts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once your account has been loaded, you can view the list of backends available to you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available backends:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<IBMQBackend('ibmqx4') from IBMQ()>,\n",
       " <IBMQBackend('ibmq_16_melbourne') from IBMQ()>,\n",
       " <IBMQBackend('ibmq_qasm_simulator') from IBMQ()>,\n",
       " <IBMQBackend('ibmq_20_tokyo') from IBMQ(ibm-q-internal, research, yorktown)>,\n",
       " <IBMQBackend('ibmq_qasm_simulator') from IBMQ(ibm-q-internal, research, yorktown)>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Available backends:\")\n",
    "IBMQ.backends()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running circuits on real devices\n",
    "\n",
    "Today's quantum information processors are small and noisy, but are advancing at a fast pace. They provide a great opportunity to explore what [noisy, intermediate-scale quantum (NISQ)](https://arxiv.org/abs/1801.00862) computers can do."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IBMQ provider uses a queue to allocate the devices to users. We now choose a device with the least busy queue which can support our program (has at least 3 qubits)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:04:50.410670Z",
     "start_time": "2018-09-29T00:04:41.860767Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best backend is ibmqx4\n"
     ]
    }
   ],
   "source": [
    "from qiskit.providers.ibmq import least_busy\n",
    "\n",
    "large_enough_devices = IBMQ.backends(filters=lambda x: x.configuration().n_qubits > 4 and\n",
    "                                                       not x.configuration().simulator)\n",
    "backend = least_busy(large_enough_devices)\n",
    "print(\"The best backend is \" + backend.name())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the circuit on the backend, we need to specify the number of shots and the number of credits we are willing to spend to run the circuit. Then, we execute the circuit on the backend using the ``execute`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:04:52.336262Z",
     "start_time": "2018-09-29T00:04:51.755588Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd73a122e3a24bb78be372442cfca7e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value=\"<p style='font-size:16px;'>Job Status: job is being initialized </p>\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit.tools.monitor import job_monitor\n",
    "shots = 1024           # Number of shots to run the program (experiment); maximum is 8192 shots.\n",
    "max_credits = 3        # Maximum number of credits to spend on executions. \n",
    "\n",
    "job_exp = execute(qc, backend=backend, shots=shots, max_credits=max_credits)\n",
    "job_monitor(job_exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``job_exp`` has a ``.result()`` method that lets us get the results from running our circuit.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> When the .result() method is called, the code block will wait until the job has finished before releasing the cell.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:05:42.718830Z",
     "start_time": "2018-09-29T00:05:42.296069Z"
    }
   },
   "outputs": [],
   "source": [
    "result_exp = job_exp.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like before, the counts from the execution can be obtained using ```get_counts(qc)``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:05:44.604801Z",
     "start_time": "2018-09-29T00:05:44.599850Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_exp = result_exp.get_counts(qc)\n",
    "plot_histogram([counts_exp,counts])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulating circuits using a HPC simulator\n",
    "\n",
    "The IBMQ provider also comes with a remote optimized simulator called ``ibmq_qasm_simulator``. This remote simulator is capable of simulating up to 32 qubits. It can be used the \n",
    "same way as the remote real backends. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = IBMQ.get_backend('ibmq_qasm_simulator', hub=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots = 1024           # Number of shots to run the program (experiment); maximum is 8192 shots.\n",
    "max_credits = 3        # Maximum number of credits to spend on executions. \n",
    "\n",
    "job_hpc = execute(qc, backend=backend, shots=shots, max_credits=max_credits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_hpc = job_hpc.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dA/0EsIVScJ6TmSvL7e/HQ7iSpH1Ih/ZAM3M5cGIr7Z/qtIokSdoLdPg+0IjYLyJOiYjPR8QB5baDI6Kq88uTJKln6tAeaESMpXTh0GuBA4C7gOeAj5c/n9XZBUqS1BN1dA/0WuB+YASwqVn7z4F3d1ZRkiT1dB29CvedwDsyc3tENG9fBtR2WlWSJPVwRcbC7ddK20hK94JKkrRP6GiA3g9c2OxzRsQQ4MvALzutKkmSeriOHsK9EHggIhqB/YA7gbHAKuBDnVybJEk9VkfvA10ZEW8FPgwcQmkP9kbgB5m5aZczS5LUi3R0D5RyUN5cfkmStE/abYBGxMnAfZm5tfy+TZl5T6dVJklSD9aePdC7gQOBZ8rv25JAn84oSpKknm63AZqZr2ntvSRJ+7IOBWJEHBkRrwrdiOgTEUd2XlmSJPVsHd2jfABobdD4A8rTJEnaJ3Q0QIPSuc6WhgMv7nk5kiTtHdp1G0tE/Lz8NoHvR8TmZpP7AJOBhzu5NkmSeqz23ge6pvxvAOt45ZNYtgAPAjd1Yl2SJPVo7QrQzPwHgIhYAlydmR6ulSTt0zo6lN+Xu6oQSZL2Ju0Ziei/gaMyc11EPEHrFxEBkJlv6cziJEnqqdqzB/oTYMdFQ7saiUiSpH1Ge0Yi+nJr7yVJ2pc5NJ8kSQW05xzoLs97Nuc5UEnSvqK9T2ORJEnNdOgcqCRJKvEcqCRJBXgfqCRJBXgfqCRJBVT8PtCIOBf4HFADPAl8KjP/s42+RwFfBcYDA4GlwL9l5tUt+n0Q+GfgYOB/gP+dmT/d01olSWpLoXOgEXFwRPx1+XVwB+Y7FbgOmA7UUXoE2oyIGNnGLC8A3wSOBCYCVwJfLofwjmUeBtwJ/AB4a/nfuyLi7R1fM0mS2qdDARoRwyPiXmAhcG/59aeI+FlEDG/HIi4EbsnMmzKzITPPB5qAj7fWOTPnZuaPMvPJzHwqM78P/Bo4olm3TwEPZOZXysv8CvAf5XZJkrpEh57GAvwbMJZSgP2h3PZ24F8pPQ/05LZmjIj+wKHA1S0m3Q+8sz1fHhF15b6XN2s+DPhWi66/Bj7RxjLOBs4GqKmp4bHHHgOgtraWgQMHsmjRIgCGDh3KmDFjqK+vB6BPnz5MnTqVxsZGXnyx9DS3CRMmsHbtWmBYe8rXXqahoYFNm0qPvp04cSKrV69m9erVAIwaNYqIYMmSJQAMHz6cmpoa5s+fD8CAAQOYNGkSTz75JJs3ly4hmDx5Mk1NTaxZU3q87ujRo8lMli5dCkB1dTXV1dUsWLAAgP33358JEybwxBNPsHXrVgCmTp3KsmXLWLduHQBjxoxhy5YtLF++HIARI0ZQVVVFQ0MDAIMGDWL8+PHMmzeP7du3A1BXV8fixYtZv349AGPHjmXjxo2sXLkSKG0XQ4YMobGxEYDBgwczbtw46uvryUwigrq6OhYuXMiGDRsAGD9+PM8//zxNTU3Anm1Pq1atAuCggw6if//+LF68GIBhw4YxcuRI5s2bB0C/fv2YMmVKob+Teqc1a9Z0yvbUXpHZrkGGSp0jNgLvycw5LdoPA2Zm5qBdzFsLrKB0Re/sZu1fAk7LzPG7mHc5UE0p8L+cmVc0m7YFOCszb2vWdgZwU2YO2NX61NXV5axZs3bVpV0uutUA7Y2uOnNdd5egLuI22zt11jZbVVU1NzOn7a5fR/dAVwOtPUx7I7Cmg8vqiCOA1wLvAL4WEU9l5u1d+H2SJO1SRwP0CuDaiDg9M1cARMTrgWvK03blWWA7MKJF+wjg6V3NmJlPld8+EREjKB3C3RGgTxdZpiRJe6LIYPJvBJZExIry59cDLwF/Qekcaasyc0tEzAWOBe5qNulYSveattdrgOaHZueUl/H1Fst8uAPLlCSpQyo9mPw3gNsj4hHgIeAcoBa4HiAibgPIzDPKn88HngIay/MfCXwW+E6zZV4HzI6IiyldFXwS8G7g8E6sW5KkV6joYPKZeWf5dpdLKQ2kMB84ITOXlru0vB+0D/A1YDSwjdIgCRdTDtzyMh+OiL+jdI/oFeU+p2bmH5AkqYt09BzoHsvM7/DKPcjm045u8fla4Np2LPNuHGZQklRBHR1IoX9EfDki/hQRL0XE9uavripSkqSepqND+f0zcCalq25fpjSm7bcp3cJy7i7mkySpV+logH4IOCczb6B0S8rPMvMC4DJKV75KkrRP6GiAjgAWlN+/ABxQfv/vwHGdVZQkST1dRwN0GaXbTgAWAe8rvz8M2NRZRUmS1NN1NEB/Cryn/P46So8Wewq4hV0MoiBJUm/TodtYMvMLzd7fXR7k/Z3AnzLzF51dnCRJPdUe3Qeamb8Hft9JtUiStNfo6CFcIuKQiLgtIh4tv26PiEO6ojhJknqqjg6kcBrwX5SG4ftV+TUCeCQiPtL55UmS1DN19BDuV4AvZub05o0R8QVKY9F+v7MKkySpJ+voIdxq4MettN9F6XFmkiTtEzoaoA8AR7fSfjTwuz0tRpKkvUV7Hqh9crOPM4CvRsQ0/nz17TuAk4HLO706SZJ6qKIP1D67/GruW7TxmDJJknqb9jxQu8O3ukiS1NsZjpIkFVBkIIW/iojZEfFsRKyOiN9FxAldUZwkST1VRwdSOIvSgPL/A3weuBh4CvhpRHys88uTJKln6uhACp8HLszM/9us7bsRMZdSmN7caZVJktSDdfQQ7khKD89uaQYwas/LkSRp71DkgdrHttJ+HLB0z8uRJGnv0NFDuFcD3yo/feXhctu7gNOB8zuzMEmSerKOPlD7hoh4BvgMpdGHABqAD2Xmzzq7OEmSeqp2B2hE9KV0qHZ2Zv6060qSJKnna/c50MzcBtwDDO66ciRJ2jt09CKiecDYrihEkqS9SUcD9HLgmoj4QES8ISKqmr+6oD5Jknqkjl6F+8vyv/cA2aw9yp/7dEZRkiT1dB0N0Hd3SRWSJO1l2hWgETEQ+DrwAaAfMBO4IDOf7cLaJEnqsdp7DvTLwEcpHcK9g9JoRP/aRTVJktTjtfcQ7snAP2bmjwAi4gfAQxHRJzO3d1l1kiT1UO3dA30D8J87PmTmI8A2oLYripIkqadrb4D2Aba0aNtGxy9CkiSpV2hvAAbw/YjY3KxtP+CmiNi4oyEz/6Yzi5Mkqadqb4De2krb9zuzEEmS9ibtCtDM/IeuLkSSpL1JR4fykyRJGKCSJBVigEqSVIABKklSAQaoJEkFGKCSJBVggEqSVIABKklSAQaoJEkFGKCSJBVggEqSVIABKklSAQaoJEkFGKCSJBVggEqSVIABKklSAQaoJEkFGKCSJBVggEqSVEDFAzQizo2IpyLipYiYGxFH7KJvTUT8MCL+GBHbI+KWVvp8NCKyldd+XboikqR9WkUDNCJOBa4DpgN1wMPAjIgY2cYsA4Bngf8D/GEXi94I1DR/ZeZLnVW3JEktVXoP9ELglsy8KTMbMvN8oAn4eGudM3NJZl6QmbcAa3ex3MzMp5u/Or90SZL+rG+lvigi+gOHAle3mHQ/8M49XPz+EbEU6AM8DnwxM+vbqONs4GyAmpoaHnvsMQBqa2sZOHAgixYtAmDo0KGMGTOG+vrSYvr06cPUqVNpbGzkxRdfBGDChAmsXbsWGLaH5asnamhoYNOmTQBMnDiR1atXs3r1agBGjRpFRLBkyRIAhg8fTk1NDfPnzwdgwIABTJo0iSeffJLNmzcDMHnyZJqamlizZg0Ao0ePJjNZunQpANXV1VRXV7NgwQIA9t9/fyZMmMATTzzB1q1bAZg6dSrLli1j3bp1AIwZM4YtW7awfPlyAEaMGEFVVRUNDQ0ADBo0iPHjxzNv3jy2b98OQF1dHYsXL2b9+vUAjB07lo0bN7Jy5UqgtF0MGTKExsZGAAYPHsy4ceOor68nM4kI6urqWLhwIRs2bABg/PjxPP/88zQ1NQF7tj2tWrUKgIMOOoj+/fuzePFiAIYNG8bIkSOZN28eAP369WPKlCmF/k7qndasWdMp21N7RWZ28iq08UURtcAK4KjMnN2s/UvAaZk5fjfz/wJ4NjM/2qL9MOBNwDxgMPBJ4ARgamYu3NUy6+rqctasWQXW5pUuutUA7Y2uOnNdd5egLuI22zt11jZbVVU1NzOn7a5fxfZAu0pmzgHm7PgcEQ9T2gs9H7igu+qSJPVulTwH+iywHRjRon0E0GnnLDNzO/AoMK6zlilJUksVC9DM3ALMBY5tMelYSlfjdoqICOAtlC5OkiSpS1T6EO43gNsj4hHgIeAcoBa4HiAibgPIzDN2zBARby2/HQK8XP68JTMXlKdfBvweWFjucwGlAG31yl5JkjpDRQM0M++MiOHApZTu15wPnJCZS8tdWrsftOXVtCcCS4HR5c8HADcCBwLry/2PzMxHOrd6SZL+rOIXEWXmd4DvtDHt6FbaYjfL+zTw6U4pTpKkdnIsXEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAIqHqARcW5EPBURL0XE3Ig4Yjf9jyr3eykiFkfEOXu6TEmS9lRFAzQiTgWuA6YDdcDDwIyIGNlG/zcCvyr3qwO+CnwrIj5YdJmSJHWGSu+BXgjckpk3ZWZDZp4PNAEfb6P/OcDKzDy/3P8m4Fbgs3uwTEmS9ljFAjQi+gOHAve3mHQ/8M42Zjuslf6/BqZFRL+Cy5QkaY/1reB3vQ7oA6xq0b4KeG8b8xwIzGylf9/y8qKjy4yIs4Gzyx9fqKqqamxP8drpdcCz3V1EJfzbp7u7AqlTuM123Kj2dKpkgPYImXkjcGN317G3iohHM3Nad9chqX3cZrtOJQP0WWA7MKJF+wjg6TbmebqN/tvKy4sCy5QkaY9V7BxoZm4B5gLHtph0LKUrZ1szp43+j2bm1oLLlCRpj1X6EO43gNsj4hHgIUpX2dYC1wNExG0AmXlGuf/1wCci4lrgBuBdwEeBD7d3mep0Hv6W9i5us10kMrOyXxhxLnARUAPMBz6dmbPL0/4DIDOPbtb/KOBfgEnASuBrmXl9e5cpSVJXqHiASpLUGzgWriRJBRigkiQVYICqQyIiursGSeoJPAcqSVIB+9xIROq4iBgAvAU4CVgPPAksAv5fZr4YEZH+EpO0j3EPVLsVEd8ETqb0lJthwGhKtxTdC1ybmYu7rzpJ6h6eA9UuRcRESoNXfAw4PjPHUrrf9jvAicCTEXG250Yl7WvcA9UuRcQllILzyPLnvpm5rdn06ZT2To/JzJXdVKakZiJiBKUhTX+VmWt30a9fZm6tXGW9i3ug2p0GoCYixgJk5raI6BsR+5Wn3wRsBE7prgIlvcqlwG3Aooi4KyJOKF/LsFNEjAQ+2bJd7WeAandmU3r6zS8i4kMRMSAzt2XmSwCZ+RSlZ7Ju7s4iJb3CNOBrwGcoXbfwU+CpiPhWRBxS7vNPwDmZ6bZbkIdwtVsRUUtpPOIpwHLgEWBW+f15wN8DozPzxW4rUhKwc3u9DvhNZt4YEX2BscD/ovQgjinAAmAMcElmXtdtxe7lDFC1S0QMB/6a0nmVMcB4Sr9sfwfckJk/6sbyJJVFxCDgGOCZzPxDi2kDgcnAZylduzA4MzdVvsrewQBVmyLiIEq/XAFepPSrdROlAH0tpXOfz+7qIgVJ3au1+7Qj4hbg4Mw8onuq6h0cSEGtioiPU7p1ZSqloFxM6ZDtA8DdmbmoG8uT1IaIeA2QO0KzlfDcn9Izk7/RDeX1Ku6B6lXKh2sXAdcA/wpUA+8FjgYmUhpE4YLMXOAoRNLeJSKzUx4iAAADSElEQVT6AdMyc05317K3M0D1KhFxPvCRzHx7K9MOB74KvB54W2Y+W+n6JL1aORjfCCz1ytrK8DYWtWYLMDgiJkNpLNyI6A+QmQ8CpwEvAcd1X4mSWjgPqAeuj4gTI+LAiOjTvENEDImIv9qxPWvPGKBqzd3Ay8CnImJwZm7OzC3lcytk5jLgOeCg7ixS0iucSukWs7GUxqmeA3w9Ig6PiKHlPn8PfDEzt3RTjb2KAapXKI9pu5bSSCbHAisj4rsRcWh5+siI+Aile8l+3H2VStohIqqBrcBN5StrRwHfpXTr2WxgVkR8HvgU8Ic2F6QO8RyoWhURBwAjgXdSeozZu8qTngYCuD0zL++e6iQ1FxE1wN8BCzLz1y2m1QFnlacPA96QmSsqX2XvY4Bqp4j4C+B0SsN/PUvpns/ngAeB3wP9KB0e+vfM/FN31Snp1cq3p2RmvtT86Ug7rpKPiK8AJ2RmXXfV2NsYoNqpfHP1JOA+Sodxqygdqn0T8AxwacuRTST1HG3dVlYegegx4HuZ+bXKV9Y7GaACdp773EDpF+rsZm0jgbdTOgQ0BvhQZj7WbYVKeoWIGAJs2NX92OWnJ50K3OEFRJ3Hi4i0w0TgKUq3sADloUwyl2bmjyk9PPs54G+7qT5Jrfs68LGImFIO09bsl5m3Gp6dywDVDospHab9l4gYt+OWlR3KN2bfCry/O4qT9GoR8WFKjyW7BvgZpdtWToqIg8vnRHecG711x33d6jwewtVOEfEO4HpKFw/dAPwWWJeZL5TPodwGvJSZH+nGMiWVRcRNwHbgKkpPVzkTOBhoBH5FaRseD1yXmQ6e0MkMUL1C+VfqF4G/ofQEljnAakpj4TYBZ2XmE91XoSSA8nM+LwKGZObFzdonUdorPQXYDzgAuDUz/7FbCu3FDFC1qnxLy18BH6A0bN984K7M/GO3FiZpp4gYBozIzD+Wh+fb2vxioog4FbgDOCQzH++uOnsrA1S7FRGvycyXu7sOSbtXvn4hMnN7RPwTpcO3A7u7rt7I54FqtwxPae/RYnsdDFzWXbX0du6BSlIvVX7E2XZ/BHcNA1SSpAK8D1SSpAIMUEmSCjBAJUkqwACVJKkAA1SSpAL+P38dehn43S1kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_hpc = result_hpc.get_counts(qc)\n",
    "plot_histogram(counts_hpc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieving a previously ran job\n",
    "\n",
    "If your experiment takes longer to run then you have time to wait around, or if you simply want to retrieve old jobs back, the IBMQ backends allow you to do that.\n",
    "First you would need to note your job's ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-29T00:05:57.842131Z",
     "start_time": "2018-09-29T00:05:57.839227Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JOB ID: 5c56667159faae0051bceb52\n"
     ]
    }
   ],
   "source": [
    "jobID = job_exp.job_id()\n",
    "\n",
    "print('JOB ID: {}'.format(jobID))        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a job ID, that job object can be later reconstructed from the backend using retrieve_job:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_get=backend.retrieve_job(jobID)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then the results can be obtained from the new job object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'100': 33,\n",
       " '110': 47,\n",
       " '010': 21,\n",
       " '111': 346,\n",
       " '001': 21,\n",
       " '101': 112,\n",
       " '011': 32,\n",
       " '000': 412}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_get.result().get_counts(qc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}