qiskit/basics/getting_started_with_qiskit_terra.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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   "source": [
    "# Getting Started with Qiskit Terra\n",
    "\n",
    "Here, we provide an overview of working with Qiskit Terra. Qiskit Terra provides the basic building blocks necessary to program quantum computers. The basic concept of Qiskit Terra is an array of quantum circuits. A workflow using Terra 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 (e.g., when running the variational quantum eigensolver (VQE) algorithm).\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**"
   ]
  },
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     "end_time": "2018-09-29T00:04:16.313210Z",
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister\n",
    "from qiskit import execute "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='circuit_basics'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Circuit Basics\n",
    "================\n",
    "\n",
    "## Building the circuit <a id='sectionB'></a>\n",
    "\n",
    "The basic elements needed for your first program are the QuantumCircuit, and QuantumRegister."
   ]
  },
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   "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. (See [Summary of Quantum Operations](../terra/summary_of_quantum_operations.ipynb) for a thorough discussion of quantum gates.) As you proceed through the documentation you will find more gates and circuits but 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-X operation ($C_{X}$) between qubit 0 and qubit 1.\n",
    "* A controlled-X 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 Terra, 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 0x1129a9828>"
      ]
     },
     "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 Terra `circuit_drawer`, which plots circuit in the form found in many textbooks."
   ]
  },
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   "cell_type": "code",
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   "outputs": [
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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=305x132 at 0x115999C50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import circuit_drawer\n",
    "\n",
    "circuit_drawer(circ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this circuit, the qubits are put in order with qubit zero at the top and qubit three 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": [
    "<a id='aer_simulation'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating circuits using Qiskit Aer\n",
    "\n",
    "Qiskit Aer is our package for simulating quantum circuits. It provides many different backends for doing a simulation.\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-warning\">\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 Aer\n",
    "\n",
    "# Run the quantum circuit on a statevector simulator backend\n",
    "backend = Aer.get_backend('statevector_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have chosen the backend its time to compile and run the quantum circuit. In Qiskit Terra 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 returns the status of the job a result object respectiviely.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> Jobs run asynchronous 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 Terra provides the method \n",
    "`result.get_statevector(circ)` to return the statevector for the quantum circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation:  COMPLETED\n",
      "[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)\n",
    "print(\"simulation: \", result )\n",
    "print(np.around(outputstate,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qiskit Terra also provides a visualization toolbox to allow you to view these results. See [Qiskit visualizations](qiskit_visualizations.ipynb) for more information.\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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import plot_state\n",
    "plot_state(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": [
      "simulation:  COMPLETED\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",
      " [ 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 = Aer.get_backend('unitary_simulator')\n",
    "job = execute(circ, backend)\n",
    "result = job.result()\n",
    "\n",
    "# Show the results\n",
    "print(\"simulation: \", result )\n",
    "print(np.around(result.get_unitary(circ), 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": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=540x287 at 0x112FE6048>"
      ]
     },
     "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",
    "circuit_drawer(qc)"
   ]
  },
  {
   "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 = Aer.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": [
      "{'111': 524, '000': 500}\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 Terra 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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": [
    "<a id='ibmq_provider'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running circuits using the IBMQ provider\n",
    "=======================\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('ibmqx5') from IBMQ()>,\n",
       " <IBMQBackend('ibmqx2') from IBMQ()>,\n",
       " <IBMQBackend('ibmq_16_melbourne') from IBMQ()>,\n",
       " <IBMQBackend('ibmq_qasm_simulator') from IBMQ()>]"
      ]
     },
     "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.backends.ibmq import least_busy\n",
    "\n",
    "large_enough_devices = IBMQ.backends(filters=lambda x: x.configuration()['n_qubits'] > 3 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": [],
   "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_exp = execute(qc, backend=backend, shots=shots, max_credits=max_credits)"
   ]
  },
  {
   "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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kZkMI+iwaAw+5+6Qqt18PXA2UAVuAq9z9bw31/CIikphE+iAwszPM7BEzezH2/3dreb/GBCOgzgZ6AZeYWa8qzd4BCty9L/A0cFci2UREpGHVukCY2dXAk8DfgWeAj4AZZvbjWtz9JGCdu6939z1AMXBufAN3f8Xdv4pdXUQwz0JERCKSyEzqccD34yfFmdlMYBY1n4o0i6Bju1wJMPAA7UcBLySQTUREGlgiBaINQcd0vDXAMQ0XB8zsn4EC4PRqbh9NcB4KOnfu3JBPLSIicRLpg3gDmGxmhwOYWUvg18CbtbhvKcGw2HLZsW2VmNlZwC+BQnffHfZA7v6Auxe4e0G7du0SiC8iIolIpED8BOgLfG5mHwPbgX8C/qUW910CdDOz482sGTACmBvfwMz6AfcTFIdPEsglIiJJUKtDTGZmwGHAmcC3iK3m6u4ltbm/u5eZ2ViCeRONgUfcfZWZTQSWuvtcgr2RVsBTwdOxyd2rztwWEZEUqVWBcHc3s/eAI2JFoVaFocpjzCNYriN+24S4y2cl+pgiIpI8iRxieodgwT4RETkEJDKK6VVgvpk9SjBktXyNJtxdS36LiBxkEikQpwAb+ObwU0fnhBAROejUWCBiw1pvAr4ElgP/Vd0QVBEROXjUpg9iKsF5H94HLgB+k9REIiKSFmpTIIYAP3D3cQSL7f0wuZFERCQd1KZAtHT3jwDcfTPQOrmRREQkHdSmk7pJbFlvq+Y67v5yMsKJiEh0alMgPqHyKKVtVa470KUhQ4mISPRqLBDunpOCHCIikmYSOqOciIgcOlQgREQklAqEiIiEUoEQEZFQKhAiIhJKBUJEREKpQIiISCgVCBERCaUCISIioVQgREQklAqEiIiEUoEQEZFQKhAiIhJKBUJEREKpQIiISCgVCBERCaUCISIioVQgREQklAqEiIiEUoEQEZFQKhAiIhJKBUJEREKpQIiISKiUFQgzG2Jma8xsnZndGHL7aWa23MzKzGx4qnKJiEi4lBQIM2sMTAXOBnoBl5hZryrNNgFXAE+kIpOI1Gz+/Pn06NGD3NxcJk2a9I3bd+/ezcUXX0xubi4DBw5k48aNAOzdu5fLL7+cPn360LNnT+64446K+1x11VW0b9+e3r17p+plSB2lag/iJGCdu6939z1AMXBufAN33+ju7wJfpyiTiBzAvn37GDNmDC+88AKrV69mxowZrF69ulKbhx9+mKOPPpp169Zx3XXXMX78eACeeuopdu/ezXvvvceyZcu4//77K4rHFVdcwfz581P9cqQOUlUgsoDNcddLYttEJE0tXryY3NxcunTpQrNmzRgxYgRz5syp1GbOnDlcfvnlAAwfPpyFCxfi7pgZO3fupKysjF27dtGsWTOOPPJIAE477TSOOeaYlL8eSVzGdVKb2WgzW2pmS7ds2RJ1HJGDVmlpKZ06daq4np2dTWlpabVtmjRpQuvWrdm2bRvDhw+nZcuWdOjQgc6dO3PDDTeoKGSgVBWIUqBT3PXs2LaEufsD7l7g7gXt2rVrkHAi0rAWL15M48aN+fDDD9mwYQO//e1vWb9+fdSxJEGpKhBLgG5mdryZNQNGAHNT9NwiUgdZWVls3rz/yHBJSQlZWVnVtikrK+Pzzz+nTZs2PPHEEwwZMoSmTZvSvn17TjnlFJYuXZrS/FJ/KSkQ7l4GjAVeBN4HnnT3VWY20cwKAcxsgJmVABcC95vZqlRkE5FwAwYMYO3atWzYsIE9e/ZQXFxMYWFhpTaFhYVMmzYNgKeffprvfe97mBmdO3fm5ZdfBmDnzp0sWrSIE044IeWvQeonZX0Q7j7P3bu7e1d3vz22bYK7z41dXuLu2e7e0t3buHteqrKJyDc1adKEoqIiBg8eTM+ePbnooovIy8tjwoQJzJ0bHAAYNWoU27ZtIzc3l8mTJ1cMhR0zZgxffvkleXl5DBgwgCuvvJK+ffsCcMkllzBo0CDWrFlDdnY2Dz/8cGSvUQ7M3D3qDHVWUFDgdd1tzbnx+To/78ZJw+p8XxGRclF9DpnZMncvqKldxo1iEhGR1FCBEBGRUE2iDiAimUWHZw8d2oMQEZFQKhAiIhJKBUJEMlpdV5wtt2nTJlq1asVvfvObim1Tpkyhd+/e5OXlcffddyf7JaQtFQgRyVj1WXG23PXXX8/ZZ59dcX3lypU8+OCDLF68mL/85S8899xzrFu3LiWvJ92oQIhIxqrPirMAs2fP5vjjjycvb/+83Pfff5+BAwdy+OGH06RJE04//XSeeeaZ1L2oNKICISIZqz4rzn755Zfceeed3HLLLZXa9+7dm9dff51t27bx1VdfMW/evEprUh1KNMxVRA5Jt956K9dddx2tWrWqtL1nz56MHz+eH/zgB7Rs2ZL8/HwaN24cUcpoqUCISMZKZMXZ7OzsSivOvv322zz99NOMGzeO7du306hRI1q0aMHYsWMZNWoUo0aNAuAXv/gF2dnZKX1d6UIFQkQyVvyKs1lZWRQXF/PEE5VPa1++4uygQYMqrTj7+uuvV7S59dZbadWqFWPHjgXgk08+oX379mzatIlnnnmGRYsWpfR1pQsVCBHJWPErzu7bt4+rrrqqYsXZgoICCgsLGTVqFCNHjiQ3N5djjjmG4uLiGh/3ggsuYNu2bTRt2pSpU6dy1FFHpeDVpB8VCBHJaEOHDmXo0KGVtk2cOLHicosWLXjqqacO+Bi33nprpevxexeHMo1ikoTUdVLSggUL6N+/P3369KF///4VJ5MBmDlzJn379iUvL+8bY9RFJDoqEFJr9ZmU1LZtW5599lnee+89pk2bxsiRIwHYtm0bP/vZz1i4cCGrVq3i73//OwsXLqxXzmQUsT179jB69Gi6d+/OCSecwKxZs+qVUSQT6BCT1Fr8pCSgYlJSr169KtrMmTOnYnd9+PDhjB07FnenX79+FW3y8vLYtWsXu3fvZv369XTr1o127doBcNZZZzFr1izOPPPMOmUsL2ILFiwgOzubAQMGUFhYWCljfBErLi5m/PjxzJw5s6KIdezYkZUrVzJ48OCKMfW333477du354MPPuDrr7/m008/rVM+SZ26rjqrFWf30x6E1Fp9JiXFmzVrFieeeCLNmzcnNzeXNWvWsHHjRsrKypg9e3a9JiXVZ2Ztv3796NixI1C5iAE88sgj/PznPwegUaNGtG3bts4ZRTKFCoSk1KpVqxg/fjz3338/AEcffTT33nsvF198Md/5znfIycmp16SkZBSx7du3A3DzzTdz4okncuGFF/Lxxx/XOWO5ZBwK++Uvf0mnTp2+MflLpC5UIKTWEpmUBFSalFTe/rzzzmP69Ol07dq14j7nnHMOb7/9Nm+99RY9evSge/fuKXg11ataxMrKyigpKeHb3/42y5cvZ9CgQdxwww31eo5k9OdA8F4uXry4XtlEyqlASK3FT0ras2cPxcXFFBYWVmpTPikJqDQpafv27QwbNoxJkyZxyimnVLrPJ598AsBnn33GPffcw9VXX13njMkoYm3atOHwww/n/PPPB+DCCy9k+fLldc4IyTsUdvLJJ9OhQ4d6ZRMppwIhtRY/Kalnz55cdNFFFZOS5s6dC8CoUaPYtm0bubm5TJ48ueLQSVFREevWrWPixInk5+eTn59fURiuvfZaevXqxSmnnMKNN95Yrz2IZBQxM+Occ87h1VdfBWDhwoWVOr3rIhmHwkQamkYxSULqOinppptu4qabbgp9zBkzZjRYvvrMrI0vYuWv6aWXXqJ9+/bceeedjBw5kp/+9Ke0a9eOP/zhDw2Wua7KD4W99NJLUUeRg5QKRJqYP38+1157Lfv27ePqq6/mxhtvrHT77t27ueyyy1i2bBlt2rRh5syZ5OTksG3bNoYPH86SJUu44oorKCoqqrjPsmXLuOKKK9i1axdDhw5lypQpmFmqX1rKJaOIHXfccbz22msNlrE+i8yVtw/rzxFpSCoQaaA+Y/dbtGjBbbfdxsqVK1m5cmWlx73mmmt48MEHGThwIEOHDmX+/PmVzpzVUOo63hwO3THn9Vlk7kD9OSINSX0QaaA+HZYtW7bk1FNPpUWLFpXaf/TRR+zYsYOTTz4ZM+Oyyy5j9uzZKXtNcmDJ6s8ZN24c2dnZfPXVV2RnZ39jjSGRRGgPIg2EdVi+/fbb1baJ77CsbsJWaWlppTXswzpBDzXpNrM2GYfC7rrrLu66666GDSqHLO1BiIhIKO1BpIH6dlhW95glJSUHfExJP+m2lyOHNu1BpIH6jN2vTocOHTjyyCNZtGgR7s706dM599xzk/o6ROTgoj2INFDfs2Ll5OSwY8cO9uzZw+zZs3nppZfo1asX99xzT8Uw17PPPjspI5hE5OClApEm6nNWrPJF3KoqKCj4xtBXEZHa0iEmEREJlbI9CDMbAkwBGgMPufukKrc3B6YD/YFtwMXuvjFV+dKVOi1FJCop2YMws8bAVOBsoBdwiZlVXe1sFPCZu+cCvwPuTEU2EREJl6pDTCcB69x9vbvvAYqBqkNqzgWmxS4/DZxph8LCQSIiaSpVBSILiD+PZElsW2gbdy8DPgeqH+gvIiJJZe6e/CcxGw4McferY9dHAgPdfWxcm5WxNiWx63+Ntdla5bFGA6NjV3sAa5IUuy2wtcZW0VLGhpMJOTMhI2RGzkM943Hu3q6mRqnqpC4FOsVdz45tC2tTYmZNgNYEndWVuPsDwANJylnBzJa6e0Gyn6c+lLHhZELOTMgImZFTGWsnVYeYlgDdzOx4M2sGjADmVmkzF7g8dnk48LKnYvdGRERCpWQPwt3LzGws8CLBMNdH3H2VmU0Elrr7XOBh4DEzWwd8SlBEREQkIimbB+Hu84B5VbZNiLv8D+DCVOWphaQfxmoAythwMiFnJmSEzMipjLWQkk5qERHJPFpqQ0REQqlAiIhIKBUIEREJpQJRAy330TAsoN83SQv6u64d/cGGMLOjzawtQPlcjHT6hcqUD1sza2ZmJ5hZtge+jm3Xe1kPZtYo0zKng/jfu7i/a72XB6BRTHHM7FzgBoJ5GMT+nwfMc/edkQWLY2Yt47OU/9Kn26TC2JIoPyaYDd8G+BKYCTzh7juizFbOzFq7++dx1xsBlBeydGJm7YGuwAfuvi1uu6XLzz6d379ysYx5QBdgkbt/HH9bumSP5TR33xdpjjT53YqcmfUHZgN3ALuAw4FuBOs9bQL+090/jC4hmFku8N/A68BrwOLY6rjxbTq4+0dR5IvLUEAwM/7fgS8IJkcOAM4EvgJucfe3o0sIZnYCwc/7fwm+BLwYm4sT3ybX3ddFkS8uQxbwc2AIwbpjnQl+H/8A/DHqDxAIvrQQfEf5Km5b2hULM8sBJgG9gY+BAoL38vfAH2KLhEbKzI4AcPcv4rY1Djal/r1UgYgxs98Brd39qrht7Ql+iUYDLYELovz2a2b/DfwImA+0Az4DlgP/6+7vxvYmngZGuPveCHPeAXzL3a+M29acoOCOBk4EznP3LRFFxMzuBr5P8F7mExxuXQLMdfc3Ym0WAadVLcIpzvkYYAQrDewi2Bv7LvBtYCXwi6oLWqaamf0KOI/gvXzO3V+pcvvhBL8P66PIF5fjf4C9wG+B7QTrvQ0nmKD7d+A6d38vuoRgZlOAfwGeBR5399lVbj8CyElVThWImNhSIGcDl8YfdojddizwOHCXu78URb5YjieARcBCgg+1fwKOA5oD/wf0A45295OiyghgZpcC1wKj3H1lldsOA2YBj7n7jCjyxXI8QPCePQnkELyfAwgOPfydYPl5c/dBUWUEMLPVwGXuvjRuWyuCn/V/AUvc/fqo8sXybCJYRudr4ATgHwR7uc+5+wozuxro7+7XRBgTM1sCXO/ur1fZ3hn4NcEpBq4Bvo7qsJ2ZfUDw99GCYI+7BcHf+/+4+5/N7Brg++5+fkoCubv+Bb8LbYE/Ay8AA0JuX0vwrTeqfM0Jvu2cGbftMIIPtX8lOAPf18AP0+C9bE5wUqiFwDlx2xvF/l8DDI8wX1PgdGBQlW05wFDg32Lv5bCI38dmBGdifAJoFXJ7HvAmwdLNUWXsCjwDnEZQVM8CxhH0N71B8MWqjGCvNsr3sjFwO0Hh6hZyewfgHaCw8i2EAAAG60lEQVRnhBk7x37WZ8b+hnKBS2Pv4VqCL4dfE5yOOSWZtAcBmFkTDxYUPJ7gW9l3CdZhnwesIPiBfcfdu0cYEwgO1bj77qqdk2Z2GvCqu0c6IqO8o8/M2gE3AVcCewj6JNYA3wGy3L1fhDGB4Niuu+8LeS8HAm9F/V7GsvQDHgQ2AL9298Vxtx0H/J+7HxZhvmMIvqSsdvfNsW0tCIptZ4LDN+e5e9uoMpYzs2yC/gYIPnQXALsJPnQ7Aysjfi8PB3oCH3qsHzF22Lg1wRfYq4GrU/leqkDEmNmRHutfMLPeBN8wLyD4wTwJzPe43fwomNkRHtd5FdtWXtyuItiNHxNRvFCxX/qzgYsIdpdfIChk/xdhptCRP+XbzewCgpNVjYsgXliePIJiex7BqLBnCL5h5gOvufsNEcYEqh8BZGYzgebu/qMIYsXnKP/i0gX4D2Akwci6F4BvAccCz7v7LRHGPKDYIebW7j4sZc95qBcIM+sJ/JBgefGjCDqHngde9yqjWqJSJeORBL/UzwF/9tiQ19i3oy/dfXtkQeOk4yiW2ort/XzlEQ9tjn07x90/jV1vT3AI50cE/SR/JFgu/4tqHyT5GdsQfAPfCzR1989i28uL293AdHdfHlXG6pjZMIKjA5sIDoetcvdd0aaqnpndCLzg7n9J2XOqQNirBEPeZgJHEHzT7RfbNtHd/1h+KCKNMg4H+hN8SExw9+eiyhYv9mGwlaBDbWPc9qYe4aiqeAfImDZj4AHMbBRQSFAQ1hMMa34DeNrd96ZD3ioZ1xIc338LWOix+QVm1sbj5m1EIVZovyj/HUyH966qqhmradPc3XenMNah3UkNtCf41l11+5HAROAD4FRlrFXOYwm+Sa4D9hF8UIwiGFVV3uYJou3or23G8yN+LzsSTNL8fwSdp5cQzHtYTjCQ4nuxdo3TLOMjwDKCuSWnRf07GcvZDngb+CVwEsFRgvLBEuX/t0zXjHFtIskY+Q8w4h9MZ+AVqhn5Q9Bh/SjBrrMyHjjnJQRDHRsRnHP8DuBDgrH7fyT4pvk1ISNIlPEbOa8DFoRsPxaYAqwimFegjDXn/BlBv82rwGaCw7PXEIwAO4xg9NoG4JgMyNgm1dkiH6URsc0E38huMbN/NbO82Dj9cquAXh7t4ZFMyAjBJLPpQFt3L3H3n7t7R4IRYR8TzFr+s7uvVcYavQ20MrP8+I3u/rG7X0vwMx8ZSbL9MiEjBLOm73b3MwgGniwjGBb+LMGIpnsJvq1/Wu0jJF9tM6b8UJ36IIJp7LcA3YFPgI8IvlG2JhgT/7i7/776R0i+TMgIFR3TzTykc9/MlhIsZzA19ckq5ciEjIcDDxFM2nsG+BPBMNJ/xG5/k2BNqyJlPDAz60ow83hhle2nEYwKK5/Q+Yco8sWypG3GQ7ZAmFl3gmUfWhMccuhKMHSwhKATMw+4B3jKI+qgzoSMsZw9CMZot4vl3EKw7MKf3N1jI122AEe6+5fKWLPYXuIY4GSCiWZfEuRuQjDvoL/HrX0UhUzIGM9CFsAzs33AEemSM90yHsoF4n2CURc7CNY0OhroRDBx5gGPrccTpUzICJVybidYriCH4JvlRmCyu68ysxZh39qV8cDMbAAwiGCwwlEEH8L3edwIrKhlQkao+PB1j33omdn5BN/MUzavoCbplvGQLBBmNhiY6u65setNCJYJ6A8MI/gQvsIjXL01EzLGclXN2ZhgVMuJwPkEma909xJlPLDYB+1PgZeBN9x9Tdxt5TPoUz/UMcMyxrLE5/xfj1uVN36iZJRD2DMh46HaSd0S+NjMOgG4e5m7/83dnwFuBhwYHGVAMiMjfDPnvlgH8FzgFwSjgr4fZUAyIyMEy3oXEHSa32FmvzOzi80sK/bB+y3gd9FGzIiMUDnnXWY22cyGW7AcvptZezO7P8pDs5mQ8VDdgyhfFrsxweqO66vcfg/BGPN/iSJfLEPaZ4zlSPucGZKxMcHwxpkEa1b1J1iXpz3B8f1FBDOot3qqVvLMwIy1zPkWQedvOr+XkWeEoDPpkBOrzr8gWBf+HTN7l+AD5GWCYWaFBLOVI5MJGSEzcmZCRoKVW6cB6939LeCN2LfxfgSHwk4ATiXo/I1KJmSEmnP2JPqcmZDx0NyDiGdmJwLnEhyL7kDwoTHf3R+JNFicTMgImZEz3TPa/kXlqq4wOxq4w93bRBivPEvaZ4TMyJnuGQ/5AhEvNmyvmVc5YVA6yYSMkBk5MyRj+aJ3txFMlvpl1JmqyoSMkBk50y2jCoRIBjCztsBOT+/VRtM+I2RGznTJqAIhIiKhDtVhriIiUgMVCBERCaUCISIioVQgREQklAqEiIiEUoEQEZFQ/x9X4/SEMJ1OcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts_exp = result_exp.get_counts(qc)\n",
    "plot_histogram(counts_exp)"
   ]
  },
  {
   "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')"
   ]
  },
  {
   "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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: 5be8ae5e17436b0052751909\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": [
       "{'00000': 367,\n",
       " '00001': 10,\n",
       " '00010': 30,\n",
       " '00011': 27,\n",
       " '00100': 22,\n",
       " '00101': 83,\n",
       " '00110': 50,\n",
       " '00111': 435}"
      ]
     },
     "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
}