1_getting_started_with_qiskit.ipynb

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   "source": [
    "<img src=\"../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
   ]
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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.  The fundamental package of Qiskit is Terra that provides the basic building blocks necessary to program quantum computers. The fundamental unit of Qiskit is the [**quantum circuit**](https://en.wikipedia.org/wiki/Quantum_circuit). A basic 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** that allows you to run them on different backends.  After the jobs have been run, the data is collected and postprocessed depending on the desired output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "\n",
    "1) [Circuit Basics](#basics)\n",
    "\n",
    "\n",
    "2) [Visualize Circuits](#visualize)\n",
    "\n",
    "\n",
    "3) [Simulating Circuits With Aer](#simulation)\n",
    "\n",
    "\n",
    "4) [Running on IBM Q Devices](#ibmq)\n"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circuit Basics <a name='basics'></a>\n",
    "\n",
    "\n",
    "### Building the circuit\n",
    "\n",
    "The basic element needed for your first program is the QuantumCircuit.  We begin by creating a `QuantumCircuit` comprised of three qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Create a Quantum Circuit acting on a quantum register of three qubits\n",
    "circ = QuantumCircuit(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you create the circuit with its registers, you can add gates (\"operations\") to manipulate the registers. As you proceed through the tutorials you will find more gates and circuits; 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 three-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 the superposition state $\\left(|0\\rangle+|1\\rangle\\right)/\\sqrt{2}$.\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."
   ]
  },
  {
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   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1a25ab1ac8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a H gate on qubit 0, putting this qubit in superposition.\n",
    "circ.h(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(0, 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(0, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Circuit <a name='visualize'></a>\n",
    "\n",
    "You can visualize your circuit using Qiskit `QuantumCircuit.draw()`, which plots the circuit in the form found in many textbooks."
   ]
  },
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    {
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      "text/plain": [
       "<Figure size 257.798x156.52 with 1 Axes>"
      ]
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     "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 that are applied earlier in the circuit show up further to the left).\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> If you don't have matplotlib set up as your default in '~/.qiskit/settings.conf' it will use a text-based drawer over matplotlib. To set the default to matplotlib, use the following in the settings.conf\n",
    "    \n",
    "    [default]\n",
    "    circuit_drawer = mpl\n",
    "    \n",
    "For those that want the full LaTeX experience, you can also set the circuit_drawer = latex.\n",
    "\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating circuits using Qiskit Aer <a name='simulation'></a>\n",
    "\n",
    "Qiskit Aer is our package for simulating quantum circuits. It provides many different backends for doing a simulation. There is also a basic, Python only, implementation called `BasicAer` in Terra that can be used as a drop-in replacement for `Aer` in the examples below.\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 used in most physics textbooks. Suppose there are $n$ qubits, and qubit $j$ is labeled as $Q_{j}$. Qiskit uses an ordering in which the $n^{\\mathrm{th}}$ qubit is on the <em><strong>left</strong></em> side of the tensor 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 many 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": {
    "ExecuteTime": {
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     "start_time": "2019-08-10T11:37:44.765280Z"
    }
   },
   "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 that 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": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.799134Z",
     "start_time": "2019-08-10T11:37:44.770995Z"
    }
   },
   "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": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.804647Z",
     "start_time": "2019-08-10T11:37:44.801681Z"
    }
   },
   "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": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.814182Z",
     "start_time": "2019-08-10T11:37:44.808905Z"
    }
   },
   "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 density matrix \\rho."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.605645Z",
     "start_time": "2019-08-10T11:37:44.817291Z"
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   },
   "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.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": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.626148Z",
     "start_time": "2019-08-10T11:37:45.607840Z"
    }
   },
   "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 = Aer.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. \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> 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",
    "</div>\n",
    "\n",
    "Recall the probability of obtaining outcome $xyz$ is given by\n",
    "$$\\mathrm{Pr}(xyz) = |\\langle xyz | \\psi \\rangle |^{2}$$ and as such for the GHZ state probability of obtaining 000 or 111 are both 1/2.\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": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.840681Z",
     "start_time": "2019-08-10T11:37:45.627937Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 441.939x202.555 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Quantum Circuit\n",
    "meas = QuantumCircuit(3, 3)\n",
    "meas.barrier(range(3))\n",
    "# map the quantum measurement to the classical bits\n",
    "meas.measure(range(3),range(3))\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": "2019-08-10T11:37:45.868074Z",
     "start_time": "2019-08-10T11:37:45.842666Z"
    }
   },
   "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": "2019-08-10T11:37:45.873600Z",
     "start_time": "2019-08-10T11:37:45.869929Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'000': 469, '111': 555}\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": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.991815Z",
     "start_time": "2019-08-10T11:37:45.875518Z"
    }
   },
   "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.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 from the IBM Q account <a name='ibmq'></a>\n",
    "\n",
    "To faciliate access to real quantum computing hardware, we have provided a simple API interface.\n",
    "To access IBM Q devices, you'll need an API token. You can generate, or view, your API token [here](https://quantum-computing.ibm.com/account) (create an account if you don't already have one).\n",
    "\n",
    "Your IBM Q account lets you run your circuit on real devices or on our cloud simulator. Basic account usage can be seen in the examples below. For more detailed instructions on using the IBM Q account, see [Part 3: The IBMQ Account](3_the_ibmq_account.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.996553Z",
     "start_time": "2019-08-10T11:37:45.993701Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import IBMQ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After generating your API token, call: `IBMQ.save_account('MY_TOKEN')`.\n",
    "\n",
    "This will store your IBM Q 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": "2019-08-10T11:37:48.612022Z",
     "start_time": "2019-08-10T11:37:45.998566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once your account has been loaded, you have one or more providers available to you"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:48.618826Z",
     "start_time": "2019-08-10T11:37:48.614519Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-perf', group='performance', project='default-params')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-perf', group='performance', project='condor')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-perf', group='qcaas', project='default')>,\n",
       " <AccountProvider for IBMQ(hub='perf-on-client-d', group='normal', project='default')>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBMQ.providers()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each provider gives access to a selection of services (e.g. Backends) that is authorized by your account.  To see the backends available to a given provider, first select the provider by hub, group, project, or a combination thereof:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:48.626125Z",
     "start_time": "2019-08-10T11:37:48.621318Z"
    }
   },
   "outputs": [],
   "source": [
    "provider = IBMQ.get_provider(group='open')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the ask the provider to list its backends:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:49.023564Z",
     "start_time": "2019-08-10T11:37:48.628510Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmqx4') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmqx2') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_16_melbourne') from IBMQ(hub='ibm-q', group='open', project='main')>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provider.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": [
    "Let us now grab a backend from the provider on which to run our quantum circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:49.028903Z",
     "start_time": "2019-08-10T11:37:49.025461Z"
    }
   },
   "outputs": [],
   "source": [
    "backend = provider.get_backend('ibmqx2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the circuit on the given device we use `execute`.  Sometimes the devices are busy with jobs from other users.  Your job is added to the list of pending jobs called the queue, and executed in this queue order.  To monitor the status of our job through the process, we can use the `job_monitor`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <b>Info:</b> The <code>execute</code> functions does much more than just send your circuit(s) to a backend.  This functionality can be explored in <a href=5_using_the_transpiler.ipynb>Part 5: Using the transpiler.</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:10.495874Z",
     "start_time": "2019-08-10T11:37:49.031047Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "job_exp = execute(qc, backend=backend)\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": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:10.930480Z",
     "start_time": "2019-08-10T11:38:10.498070Z"
    }
   },
   "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": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:11.106254Z",
     "start_time": "2019-08-10T11:38:10.936297Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_exp = result_exp.get_counts(qc)\n",
    "plot_histogram([counts_exp,counts], legend=['Device', 'Simulator'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulating circuits using the IBM Q cloud simulator\n",
    "\n",
    "The IBM Q 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": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:11.111059Z",
     "start_time": "2019-08-10T11:38:11.108664Z"
    }
   },
   "outputs": [],
   "source": [
    "simulator_backend = provider.get_backend('ibmq_qasm_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:11.139628Z",
     "start_time": "2019-08-10T11:38:11.113246Z"
    }
   },
   "outputs": [],
   "source": [
    "job_cloud = execute(qc, backend=simulator_backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:17.557589Z",
     "start_time": "2019-08-10T11:38:11.143017Z"
    }
   },
   "outputs": [],
   "source": [
    "result_cloud = job_cloud.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:17.649171Z",
     "start_time": "2019-08-10T11:38:17.559620Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_cloud = result_cloud.get_counts(qc)\n",
    "plot_histogram(counts_cloud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieving a previously run 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, the IBM Q backends allow you to do that.\n",
    "First you would need to note your job's ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:17.654536Z",
     "start_time": "2019-08-10T11:38:17.651074Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JOB ID: 5d4eac8e2c3a2d001131ddd1\n"
     ]
    }
   ],
   "source": [
    "job_id = job_exp.job_id()\n",
    "\n",
    "print('JOB ID: {}'.format(job_id))"
   ]
  },
  {
   "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": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:18.097079Z",
     "start_time": "2019-08-10T11:38:17.656360Z"
    }
   },
   "outputs": [],
   "source": [
    "retrieved_job = backend.retrieve_job(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then the results can be obtained from the new job object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:18.222163Z",
     "start_time": "2019-08-10T11:38:18.099147Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'111': 415,\n",
       " '101': 32,\n",
       " '001': 17,\n",
       " '011': 25,\n",
       " '000': 483,\n",
       " '110': 21,\n",
       " '100': 12,\n",
       " '010': 19}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retrieved_job.result().get_counts(qc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:18.277518Z",
     "start_time": "2019-08-10T11:38:18.224481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.3 (default, Mar 27 2019, 16:54:48) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Sat Aug 10 07:38:18 2019 EDT</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
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  "varInspector": {
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 1
}