{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the IBMQ and Qiskit "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the circuit (Quantum Random Number Generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The access to IMB Quantum experience is done via *Qiskit* package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit\n",
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic definition of a circuit is given by the number of qubits and the number of classical bits. Let's start with the simplest circuit having one qubit and one classical bit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = qiskit.QuantumCircuit(1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having defined the basic structure of the circuit, you can define a sequence of operations performed on it. For example we might want to apply Hadamard on the qubit and then measure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x25e45f88748>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.h(0)\n",
    "circuit.measure(0,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At any point one can draw the circuit using the draw object. Default setting is text output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        ┌───┐┌─┐\n",
       "q_0: |0>┤ H ├┤M├\n",
       "        └───┘└╥┘\n",
       " c_0: 0 ══════╩═\n",
       "                </pre>"
      ],
      "text/plain": [
       "<qiskit.visualization.text.TextDrawing at 0x25e45f88cf8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, one can obtain a more comprehensible output using the Matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 233.576x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating the circuit "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the circuit, define first the backend for your circuit. You can use start by using a built-in (local) simulator. A standard one is provided in Aer framework called *QASM*. A *QASM* simulator is available also as an IBM cloud service. Finally, you can use also one of the available quantum computers. For now, let us start with the built-in simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator = qiskit.Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then you can run the job using the <code>execute</code> command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = qiskit.execute(circuit, simulator, shots=1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': 517, '1': 507}\n"
     ]
    }
   ],
   "source": [
    "counts = job.result().get_counts()\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <code>result()</code> object is not just results, it contains an extensive information about performed job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Result(backend_name='qasm_simulator', backend_version='0.3.0', date=datetime.datetime(2019, 10, 3, 13, 27, 7, 780494), header=Obj(backend_name='qasm_simulator', backend_version='0.3.0'), job_id='873936f3-b0ce-4325-a8a1-7eb3b5dadf36', metadata={'max_memory_mb': 8096, 'omp_enabled': True, 'parallel_experiments': 1, 'time_taken': 0.002237686}, qobj_id='431f7fe7-4916-4e2a-a42c-aa08b33b74e2', results=[ExperimentResult(data=ExperimentResultData(counts=Obj(0x0=517, 0x1=507)), header=Obj(clbit_labels=[['c', 0]], creg_sizes=[['c', 1]], memory_slots=1, n_qubits=1, name='circuit0', qreg_sizes=[['q', 1]], qubit_labels=[['q', 0]]), meas_level=2, metadata={'measure_sampling': True, 'method': 'stabilizer', 'parallel_shots': 8, 'parallel_state_update': 1}, seed_simulator=363670589, shots=1024, status='DONE', success=True, time_taken=0.002129734)], status='COMPLETED', success=True, time_taken=0.0029561519622802734)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can use also visualize these results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in the ideal case, the quantum system provides a physical way of obtaining randomness. Hence, we have created a quantum RNG."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Quantum Experience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use IBM quantum computer, import IBMQ object from Qiskit package. This object manages account credentials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import IBMQ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are first-time user, you need to get an API token. Follow the instructions on <a href=\"https://qiskit.org/documentation/install.html#access-ibm-q-systems\">this page</a> to create your own token. After you obtain it, run the following code, where you replace <code>MY_API_TOKEN</code> with the token you obtained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IBMQ.save_account('MY_API_TOKEN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have already used the account and the token is stored on your computer, you can load it using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can chec the active account using code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "IBMQ.active_account()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, to run the job, first select the device that will be used as a backend instead of simulator. First, let us look at available devices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmqx2') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_16_melbourne') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_vigo') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_ourense') from IBMQ(hub='ibm-q', group='open', project='main')>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBMQ.get_provider().backends()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are all the backends you can use. The first one is the *QASM* cloud simulator, and the rest are available quantum computers. Some are more busy than others and we can use the least busy one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = qiskit.providers.ibmq.least_busy(IBMQ.get_provider().backends(operational=True, simulator=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can run the job in the same way as when we used the simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = qiskit.execute(circuit, device, shots=1024)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can monitor the progress of your job, as it can take several minutes to go through the queue and run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "from qiskit.tools.monitor import job_monitor\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you can look at the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = job.result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantum computer v. QASM simulator "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the basic difference between the simulator and the quantum computer, it is enough to just prepare an \"empty\" circuit, in which we just measure the initialized state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 173.376x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit = qiskit.QuantumCircuit(1, 1)\n",
    "circuit.measure(0, 0)\n",
    "circuit.draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classical QASM simulation will use the predefined <code>simulator</code> backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cjob = qiskit.execute(circuit, simulator, shots=1024)\n",
    "ccounts = cjob.result().get_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For quantum computation we will use already defined quantum computer in <code>device</code>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "qjob = qiskit.execute(circuit, device, shots=1024)\n",
    "job_monitor(qjob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "qcounts = qjob.result().get_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the results of both runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAE1CAYAAABHgoIcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzt3Xl4VdW9//H3FzIISEISAiRgQATTEDCGKBWcwMJFtHotYtULrUOROrV6BRXs9QrVWo0TtlYrDnX8aVFoq164ZQgWBQeIEAVCBCMgJGBMCEGGhIT1++OccA8h04FknyR8Xs9znpy99tr7fDfxwMe9117bnHOIiIiIeKVdqAsQERGR44vCh4iIiHhK4UNEREQ8pfAhIiIinlL4EBEREU8pfIiIiIinFD5ERETEUwofIiIi4imFDxEREfFUWKgLCKW4uDiXlJQU6jJERKQFWb169XfOufjq5ezs7G5hYWHPAwPR/7Q3xkFgTWVl5cSMjIxva+twXIePpKQksrKyQl2GiIi0ILGxsZsDl8PCwp7v0aNHSnx8/M527drpmSQNOHjwoBUVFQ3Yvn3788CltfVRghMREanfwPj4+DIFj8Zp166di4+P34XvTFHtfTysR0REpDVqp+ARHP+fV50ZQ+FDREREPKXwISIiIp46rgecioiIHI0bZpLRnPt/7naym3P/oaYzHyIiIq3cH/7wh7hTTz11QIcOHdK7du2aNmHChKTi4uL2NfuYWcbzzz8fU3P7qVOn9ujZs+egjh07pnfv3v20iy++uG/1uiFDhiSbWcZHH33UIXCbkSNHnmJmGe+9917nYOtV+BAREWnF7rvvvu4zZszo9eCDD24tLS1dvWzZstxvvvkmYsSIEf3Ly8utut/rr78eFx0dXfXaa6/FBW7/xz/+MW727NlxCxYs+HLv3r2rVqxYsW7EiBFlgX169+5d/uKLLx7abvv27e1Xr159YkxMTOXR1KzwISIi0kqVlJS0e+SRRxIffvjhLePGjSuLjIx0ycnJFe+8807+1q1bI2fNmhUL8OWXX0asWLGi88yZMzd9+OGH0d98882hYRcrVqzoNHz48LLU1NRygKSkpMopU6Z8F/g548aNK37nnXdiKyt9WePFF1+MHT169M7w8PCjugtI4UNERKSVysrKOrGioqLdNddcszOwPTo6+uDw4cN3LVy4MArgueeei0tNTd177bXXlvbt23ffCy+8cOgsxllnnfX9nDlz4u69997uS5cu7VgdMAIlJiYe6Nev3765c+dGAbzxxhtdr7vuuuKjrVvhQ0REpJX69ttvw7p06VIZHh5+xLoePXocKC4uDgOYPXt23BVXXFEMcPnll5e88cYbh8LHzTffXPLQQw9tWbx4cfTo0aOTu3btevo999zTo+b+xo8fX/zqq6/G5eTkRO7evbv9yJEj9xxt3QofIiIirVS3bt0qS0tLww4cOHDEuu3bt4fHxcVVLliwoNO2bdsir7322hKA6667rmTDhg0dli9ffmgA6U033VSyfPnyL0tLS1c//vjjmx955JHEOXPmRAXu72c/+1np8uXLox577LHu1UHmaCl8iIiItFIjRozYEx4efvDll18+7A6WsrKydu+//370eeedt/svf/lLV+ccgwcPTu3atWvasGHDUgACB5BWi4yMdNdff/3O5OTkfZ9//vlhd7d07tz54PDhw3e9/vrr8RMnTjym8KF5PkRERFqpuLi4qsmTJxfcfffdSV26dKm65JJLdm/atCl80qRJSTExMZWTJk0q6dmzZ69HH3108+WXX15avd3rr78e8+ijjyY+88wzW5955pm4bt26VY4ePXp3VFTUwTlz5kRt3Lixw7Bhw464rPLYY49tmzRp0nfJyckVx1K3woeIiEiQWtIkYA888MCOuLi4qmnTpp00fvz4yIqKCjvzzDO/X7x48Zdvvvlm9AknnHDw5ptvLo6MjDx0Z8ptt932XWZmZuLbb78dHR0dXZWZmZkwadKkkw8ePGiJiYnlmZmZm0ePHv19zc/q06fPgT59+hx5jSdI5tzx+6yc9PR0l5WVFeoyRESkBYmNjc12zp1RvZyTk7MpLS3tu/q2aUlmzpwZ99BDD/X84IMP1vfv3/+YzlAci5ycnK5paWl9alunMx8iIiJtyO23314cHh7ulixZ0imU4aM+Ch8iIiJtzC233FIS6hrqo7tdRERExFMKHyIiIuIphQ8RERHxlMKHiIiIeErhQ0RERDyl8CEiIiKe0q22Ldytt97KggUL6Nq1K8uXLz9ivXOOadOmsXDhQjp06MCf/vQn0tLSAHjjjTd47LHHAJg8eTJXX301AKtXr+aWW25h//79jBo1it///veYmXcHJSLSyn2VPD2jOfd/St70FjODanPQmY8W7j/+4z9466236ly/aNEivvrqK1auXMkTTzzB5MmTAdi5cyeZmZksXLiQRYsWkZmZSWmpb1r/KVOm8MQTT7By5Uq++uorFi1a5MmxiIhI8/jDH/4Qd+qppw7o0KFDeteuXdMmTJiQVFxc3L56/eeffx45ZsyYvjExMWmdO3c+/dRTTx0wffr07pWVlSGpV+GjhRs2bBgxMTF1rp83bx5XXXUVZsaZZ55JWVkZ27dvJysri+HDhxMTE0OXLl0YPnw4ixcvZvv27ezevZshQ4ZgZlx11VXMmzfPwyNqOosWLWLIkCFkZGQwc+bMI9Z/8803XHbZZZxzzjlccsklbNu27dC66dOnM2zYMIYNG8bcuXMPtU+aNIkhQ4YwbNgwbr31Vmp7TLWISEty3333dZ8xY0avBx98cGtpaenqZcuW5X7zzTcRI0aM6F9eXm5r166NPOecc1J69uxZsXr16nW7d+9e/dZbb32VnZ3dsbS0tH3Dn9D0FD5aucLCQnr27HloOTExkcLCQgoKCo5oLygooLCwkMTExCP6tzZVVVXcddddzJ49m48++og5c+awfv36w/rce++9XHnllXz44Yfceeed3H///QAsWLCAnJwcli5dysKFC3nqqacoKysD4IorruCTTz5h2bJl7N+/n1dffdXzYxMRaaySkpJ2jzzySOLDDz+8Zdy4cWWRkZEuOTm54p133snfunVr5KxZs2LvueeexMGDB3///PPPb+3du/cBgLS0tPJ33333665du1aFom6Fj1autgcDmlnQ7a1NdnY2J598Mn369CEiIoKxY8cyf/78w/rk5eVx3nnnAXDuueceOsOzfv16zj77bMLCwujUqROpqaksXrwYgFGjRmFmmBmDBw+moKDA2wMTEQlCVlbWiRUVFe2uueaanYHt0dHRB4cPH75r4cKFUcuWLYv6yU9+srOufYSCwkcrl5iYeNjlhIKCAnr06EHPnj2PaE9ISDh0BqRm/9amrjM+gQYOHMi7774LwHvvvcf3339PSUkJAwcOZNGiRezdu5fi4mI+/PDDw/6sAA4cOMDs2bP50Y9+1PwHIyJylL799tuwLl26VIaHhx+xrkePHgeKi4vDSktL2/fs2bNFXUNW+GjlxowZw5tvvolzjhUrVhAVFUWPHj244IILWLJkCaWlpZSWlrJkyRIuuOACevTowYknnsiKFStwzvHmm29y0UUXhfowgtaYMzi//e1vWb58Oeeffz7Lli0jISGBsLAwLrjgAkaNGsWFF17IxIkTOfPMMwkLO/zGrylTpjB06FCGDh3arMchInIsunXrVllaWhpW2/i07du3h8fFxVV26dKlatu2bUemkxDSrbYt3MSJE1m2bBnFxcWkpqYydepUqkcnX3fddYwaNYqFCxeSkZFBhw4deOqppwCIiYlhypQph/7P/c477zw0cPXRRx89dKvtyJEjGTlyZGgO7hjUdcYnUEJCAq+88goA33//Pe+++y5RUVGA79bj6juDbrjhBk455ZRD2z388MMUFxfzxBNPNPdhiIgckxEjRuwJDw8/+PLLL8dMnDjx0KWVsrKydu+//370Pffcs83M+Pvf/x5z2223FYey1kAKHy3c888/X+96M+ORRx6pdd2ECROYMGHCEe3p6em1zhnSmgwePJj8/Hw2b95MQkICc+fOZdasWYf1KS4uJiYmhnbt2jFz5kzGjx8P+Aar7tq1i9jYWNauXcvatWsZMWIEAK+88gpZWVn8/e9/p107nRgUkZYtLi6uavLkyQV33313UpcuXaouueSS3Zs2bQqfNGlSUkxMTOWkSZNKxowZs3vYsGEpv/zlL3v95je/2Z6UlFS5Zs2ayN/85jeJL7zwwpZQDDpV+JBWKSwsjMzMTMaNG0dVVRXjx48nJSWFBx98kPT0dMaMGcOHH37I/fffj5kxdOjQQyHtwIEDhy41de7cmWefffbQZZfJkydz0kknMXr0aAB+/OMfc9ddd4XmIEWkxWpJk4A98MADO+Li4qqmTZt20vjx4yMrKirszDPP/H7x4sVfRkVFHUxNTS1funRp7tSpU3uedtppA6uqqkhMTKwYP378d126dAnJ3S5W27Xz40V6errLysoKdRkiItKCxMbGZjvnzqhezsnJ2ZSWlvZdKGsKxsyZM+Meeuihnh988MH6/v37V4SqjpycnK5paWl9alunMx8iIiJtyO23314cHh7ulixZ0imU4aM+Ch8iIiJtzC233FIS6hrqoxF1IiIi4imFDxERkfodPHjwYOubCjqE/H9eB+tar/AhIiJSvzVFRUXRCiCNc/DgQSsqKooG1tTVx9MxH2Z2HjAFyAASgeuccy81sM0g4ClgCFACPAvc7wJu0zGzy4H7gVOAr4DfOOf+1hzHICIix5fKysqJ27dvf3779u0D0f+0N8ZBYE1lZeXEujp4PeD0RHxJ6BX/q15mFgUsBJYCZwLJwEvAHuAxf5+hwF+B+4C5wFjgLTM72zn3SdMfgoiIHE8yMjK+BS4NdR1tiafhwzk3D5gHYGYvNWKT8UBH4Brn3D5gjZmlAHeY2eP+sx+3A0ucc7/zb/M7Mxvhb7+6qY9BREREjk1LP300FPjAHzyq/RPfJZs+AX0W1Njun8CwZq9OREREgtbS5/noAWyt0bYjYN3X/p87aulT63PizWwSMAl8Dx777LPPAN+Dyjp27MjGjRsBiI6Opm/fvqxatQqA9u3bk5aWRl5eHnv27AEgJSWFkpISnlw08JgOsi27Y3QuBQUFgO/POyoqiry8PMA3tXn//v1ZtWoVzjnMjPT0dDZs2MDu3bsBSE5OpqysjMLCQuDYfk87dvj+M+nVqxcRERHk5+cDvofwJSUlkZOTA0B4eDiDBg0iNzeXfft8uXfAgAEUFRVRVFQEQO/evTEzNm3aBEBcXBwJCQmsWeMbXxUZGUlqaipr166lvLwcgIEDB1JYWEhxse/ZTn369ME5x+bNmwGIj48nPj6edevWAdChQwdSUlL44osvqH5iZVpaGlu2bGHnTt/zo/r27UtFRQVbt/q+Jt27dyc2Npbc3FwAOnXqRHJyMjk5OVRV+WZRTk9PJz8/n127dgHQr18/9u7dq9+Tfk/6Pfl/T9L8Qja9upl9D9xa34BTM1sAfOOc+0VAW29gEzDUOfexmVUAv3DOvRrQ5xrgWefcCfXV0FTTq9/1cswx76OtyrxmZ8OdRERakJrTq0vTa+lnPrZz5BmMbv6fOxroU/NsiIRAyVlPhrqEFiv249tCXYKISEi09DEfHwHnmlngGYxRQAG+sx/VfUbV2G4U0LqfGS8iItJGeRo+zOxEMzvdzE73f3aSfznJv/73ZrY4YJP/B+wFXjKzgWY2FpgKPB4wz8eTwAVmNs3MfmBm04ARwEzPDkxEREQazeszH2cAq/yvDsAM//vf+tcn4JsoDADn3C58ZzESgZXAn/DN7/F4QJ/lwFXANcDnwM+BKzXHh4iISMvk9Twf7wN1Tk/rnLu2lrYvgPMa2O/bwNvHWJ6IiIh4oKWP+RAREZE2RuFDREREPKXwISIiIp5S+BARERFPKXyIiIiIpxQ+RERExFMKHyIiIuIphQ8RERHxlMKHiIiIeErhQ0RERDyl8CEiIiKeUvgQERERTyl8iIiIiKcUPkRERMRTCh8iIiLiKYUPERER8ZTCh4iIiHhK4UNEREQ8pfAhIiIinlL4EBEREU8pfIiIiIinFD5ERETEUwofIiIi4imFDxEREfGUwoeIiIh4SuFDREREPKXwISIiIp5S+BARERFPKXyIiIiIpxQ+RERExFMKHyIiIuIphQ8RERHxlMKHiIiIeErhQ0RERDyl8CEiIiKeUvgQERERTyl8iIiIiKcUPkRERMRTCh8iIiLiKYUPERER8ZTCh4iIiHhK4UNEREQ8pfAhIiIinlL4EBEREU8pfIiIiIinggofZvZTM/u3gOX/NrOtZvZPM0to+vJERESkrQn2zMf06jdmNhi4B/gDEA481pgdmNnNZva1me03s2wzO7eevi+ZmavltSegz/A6+vwgyGMTERERD4QF2b83kOd//xPg7865TDNbAPyzoY3N7ErgSeBm4EP/z/lmNsA5t6WWTW4DptZoWwYsraVvKlASsFzUUD0iIiLivWDPfOwHOvvf/whY5H+/K6C9PncALznnnnPO5TrnfgUUAjfV1tk5t8s5t736BZwC9AWeq6X7t4F9nXNVQRyXiIiIeCTY8PEB8JiZ3QucAczzt58KfFPfhmYWAWQAC2qsWgAMa+Tn3wCsdc4tr2XdSjMrNLPFZjaikfsTERERjwV72eVW4BlgHHCjc67A3z6Ghi+7dAXaAztqtO8ARjb0wWYWDVyBb5xJoOozJyuACOBnwGIzG+6cO+LyjJlNAiYBJCQk8NlnnwGQmJhIx44d2bhxIwDR0dH07duXVatWAdC+fXvS0tLIy8tjzx7fkJOUlBRKSkqAmIbKFzlCSUkJzjk2b94MQHx8PPHx8axbtw6ADh06kJKSwhdffMGBAwcASEtLY8uWLezcuROAvn37UlFRwdatWwHo3r07sbGx5ObmAtCpUyeSk5PJycmhqsp3MjA9PZ38/Hx27doFQL9+/di7dy8FBb6vc0JCAlFRUeTl+a6wdu7cmf79+7Nq1Sqcc5gZ6enpbNiwgd27dwOQnJxMWVkZhYWFwLF9n3bs8P0V0atXLyIiIsjPzwcgJiaGpKQkcnJyAAgPD2fQoEHk5uayb98+AAYMGEBRURFFRb6rrr1798bM2LRpEwBxcXEkJCSwZs0aACIjI0lNTWXt2rWUl5cDMHDgQAoLCykuLgagT58++j0dZ78naX7mnPPmg8wSgW3Aec65DwLa7wOuds7VO0DUzG7BN6g10TlX0kDfeUClc+7S+vqlp6e7rKysxh5Cne56WeGjLlOfmR7qElqs2I9vC3UJIlKL2NjYbOfcGaGuoy0Lep4PMzvBzMaZ2d1m1sXfdoqZxTaw6XdAFdCjRns3jjwbUpsbgDkNBQ+/T4D+jegnIiIiHgt2no9+wHrgz8DvgOrAcROQWd+2zrkKIBsYVWPVKKC2MRyBn/tDII3aB5rW5nR8l2NERESkhQl2zMdMfANEbwJKA9rfAf7SiO0fB141s0/x3TJ7I5CIL8xgZq8AOOd+XmO7G4ANwL9q7tDMbgc2AWvxjfmYAFwGXN7IYxIREREPBRs+hgFnOeeqzCywfQu+EFEv59xfzSwO+C8gAVgDXOSc2+zvklRzGzPrDFwF/NbVPkAlAngU6AnswxdCLnbOzaulr4iIiIRYsOEDfLOZ1pSEb66PBjnnngaermPd8FradgMn1rO/TBq45CMiIiItR7ADThfgmyismjOzKGAG8D9NVpWIiIi0WcGe+bgDWGJmecAJwF+BfvjuVvlpE9cmIiIibVBQ4cM5V2BmpwNXA4PxnTmZBbzunNvXDPWJiIhIGxP0mA9/yHjR/xIREREJSoPhw8zGAu865w7439fJOTe3ySoTERGRNqkxZz7exjcr6bf+93Vx+J7dIiIiIlKnBsOHc65dbe9FREREjkaw06ufZ2ZHBBYza29m5zVdWSIiItJWBXsmYwn/9zyXQF3860RERETqFWz4MHxjO2qKA/YcezkiIiLS1jXqVlsze8f/1gGvmVl5wOr2wEAaeDKtiIiICDR+no9i/08DduJ7gFu1CuBDGv+4exERETmONSp8OOeuAzCzTcCjzjldYhEREZGjEuz06jOaqxARERE5PjRmhtPPgfOdczvN7AtqH3AKgHPutKYsTkRERNqexpz5mANUDzCtb4ZTERERkQY1ZobTGbW9FxERETkami5dREREPNWYMR/1jvMIpDEfIiIi0pDGPtVWREREpEkENeZDRERE5FhpzIeIiIh4SvN8iIiIiKc0z4eIiIh4SvN8iIiIiKeCerZLNTM7BUjxL+Y6575qupJERESkLQsqfJhZHPACcClw8P+a7T3geudccRPXJyIiIm1MsHe7PA/0A84FTvC/zgNOBp5r2tJERESkLQr2ssto4EfOuY8C2paZ2S+BRU1XloiIiLRVwZ75KAL21NK+F9AlFxEREWlQsOHjt8BMM+tZ3eB//5h/nYiIiEi9jubBcicDm8xsm3+5J7Af6IZvTIiIiIhInfRgOREREfGUHiwnIiIintKD5URERMRTQYUPM4swsxlm9qWZ7TezqsBXcxUpIiIibUewZz7uB67Bd3fLQeBO4E/4brO9uWlLExERkbYo2PDxU+BG59yzQBXwD+fcr4H7gFFNXZyIiIi0PcGGj+7AOv/774Eu/vf/C/xbUxUlIiIibVew4WMLkOh/vxHfdOsAQ4F9TVWUiIiItF3Bho+/AT/yv38SmGFmXwMvoQnGREREpBGCerCcc25awPu3zWwrMAz40jn3XlMXJyIiIm1PsE+1PYxz7mPg4yaqRURERI4DQU8yZmaDzewVM1vpf71qZoObozgRERFpe4KdZGw8sAJIAOb5X92BT81sQtOXJyIiIm1NsJddfgfc65x7MLDRzKYBDwCvNVVhIiIi0jYFe9klHphdS/tbQLfG7MDMbjazr/3Ts2eb2bn19B1uZq6W1w9q9LvczNaZWbn/50+COioRERHxTLDhYwkwvJb24cC/GtrYzK7Ed4vug0A6sByYb2ZJDWyaiu9ST/VrQ8A+hwJ/BV4HTvf/fMvMfthQPSIiIuK9Bi+7mNnYgMX5wO/N7Az+7y6Xs4CxwPRGfN4dwEvOuef8y78yswuBm4BpdW/Gt8657+pYdzuwxDn3O//y78xshL/96kbUJCIiIh5qzJiPt2tpm+R/Bfoj8HRdOzGzCCADeLTGqgX45gqpz0ozi8Q3tfsDzrklAeuG+j870D+BW+uo41DtCQkJfPbZZwAkJibSsWNHNm7cCEB0dDR9+/Zl1apVALRv3560tDTy8vLYs2cPACkpKZSUlAAxDZQvcqSSkhKcc2zevBmA+Ph44uPjWbfO9wSDDh06kJKSwhdffMGBAwcASEtLY8uWLezcuROAvn37UlFRwdatWwHo3r07sbGx5ObmAtCpUyeSk5PJycmhqsr34On09HTy8/PZtWsXAP369WPv3r0UFBQAvu9FVFQUeXl5AHTu3Jn+/fuzatUqnHOYGenp6WzYsIHdu3cDkJycTFlZGYWFhcCxfZ927NgBQK9evYiIiCA/Px+AmJgYkpKSyMnJASA8PJxBgwaRm5vLvn2+CZYHDBhAUVERRUVFAPTu3RszY9OmTQDExcWRkJDAmjVrAIiMjCQ1NZW1a9dSXl4OwMCBAyksLKS4uBiAPn366Pd0nP2epPmZc86bDzJLBLYB5zvnlga0/zcw3jmXXMs2ycAIfHfYRAA/A24Ehlfvw8wqgInOuVcCtvs58JxzLrK+mtLT011WVtYxH9tdLyt81GXqM9NDXUKLFfvxbaEuQURqERsbm+2cOyPUdbRlxzTJ2FGqmXasljZfR+fygLyApo/MrA8wBVga2LWx+xQREZHQOppJxi42s6Vm9p2ZFZnZv8zsokZs+h1QBfSo0d4N2BFECZ8A/QOWtzfBPkVERMQjwU4yNhHfw+W+Au4GpgJfA38zs+vr29Y5VwFkA6NqrBqF766XxjodKAxY/qgJ9ikiIiIeCfayy93AHc65pwLaXjCzbHxB5MUGtn8ceNXMPgWW4Ru/kQj8GcDMXgFwzv3cv3w7sAlYi2/MxwTgMuDygH0+CSz1T3T2N+An+MaJnBPksYmIiIgHgg0fScD/1tI+nyPvYjmCc+6vZhYH/Be++TrWABc55zYH7D9QhH+/PYF9+ELIxc65eQH7XG5mV+GbYXUGvrMyVzrnPgnmwERERMQbwYaPLfguaWys0f5vwOYjux/JOfc0ddyS65wbXmM5E8hsxD7fpvZbgkVERKSFCTZ8PAr80f8U2+X47ig5B98tsL9q4tpERESkDQoqfDjnnjWzb4HJ+GY1BcgFfuqc+0dTFyciIiJtT6PDh5mF4bu8stQ597fmK0lERETaskbfauucqwTmAp2brxwRERFp64KdZCwH6NcchYiIiMjxIdjwMR14zMwuM7OTzCw28NUM9YmIiEgbE+zdLv/j/zmXw5+dUv0slfZNUZSIiIi0XcGGjxHNUoWIiIgcNxoVPsysI/AIvqnNw4FFwK+dc981Y20iIiLSBjV2zMcM4Fp8l13ewDfL6TPNVJOIiIi0YY297DIW+IVz7k0AM3sdWGZm7Z1zVc1WnYiIiLQ5jT3zcRLwQfWCc+5ToBLfE2lFREREGq2x4aM9UFGjrZLgB6yKiIjIca6x4cGA18ysPKDtBOA5M9tb3eCcu7QpixMREZG2p7Hh4+Va2l5rykJERETk+NCo8OGcu665CxEREZHjQ7DTq4uIiIgcE4UPERER8ZTCh4iIiHhK4UNEREQ8pfAhIiIinlL4EBEREU8pfIiIiIinFD5ERETEUwofIiIi4imFDxEREfGUwoeIiIh4SuFDREREPKXwISIiIp5S+BARERFPKXyIiIiIpxQ+RERExFMKHyIiIuIphQ8RERHxlMKHiIiIeErhQ0RERDyl8CEiIiKeUvgQERERTyl8iIiIiKcUPkRERMRTCh8iIiLiKYUPERER8ZTCh4iIiHhK4UNEREQ8pfAhIiIinvI8fJjZzWb2tZntN7NsMzu3nr5jzWyBmRWZ2W6gWVr3AAALSElEQVQz+8TMLq3R51ozc7W8Tmj+oxEREZFgeRo+zOxK4EngQSAdWA7MN7OkOjY5H8gCLvb3nwf8rZbAshdICHw55/Y3/RGIiIjIsQrz+PPuAF5yzj3nX/6VmV0I3ARMq9nZOXdbjaYZZnYxcBnwweFd3fbmKFhERESalmdnPswsAsgAFtRYtQAYFsSuOgM7a7R1MLPNZrbVzN4zs/RjKFVERESakZdnProC7YEdNdp3ACMbswMzuwXoBbwa0JwHXA/k4AsmtwHLzCzNObehln1MAiYBJCQk8NlnnwGQmJhIx44d2bhxIwDR0dH07duXVatWAdC+fXvS0tLIy8tjz549AKSkpFBSUgLENKZ8kcOUlJTgnGPz5s0AxMfHEx8fz7p16wDo0KEDKSkpfPHFFxw4cACAtLQ0tmzZws6dvvzdt29fKioq2Lp1KwDdu3cnNjaW3NxcADp16kRycjI5OTlUVVUBkJ6eTn5+Prt27QKgX79+7N27l4KCAsD3vYiKiiIvLw+Azp07079/f1atWoVzDjMjPT2dDRs2sHv3bgCSk5MpKyujsLAQOLbv044dvr8ievXqRUREBPn5+QDExMSQlJRETk4OAOHh4QwaNIjc3Fz27dsHwIABAygqKqKoqAiA3r17Y2Zs2rQJgLi4OBISElizZg0AkZGRpKamsnbtWsrLywEYOHAghYWFFBcXA9CnTx/9no6z35M0P3POefNBZonANuA859wHAe33AVc7537QwPaX4wsdVznn3qmnX3tgNbDEOffr+vaZnp7usrKygjiK2t31ssJHXaY+Mz3UJbRYsR/XvKooIi1BbGxstnPujFDX0ZZ5OeD0O6AK6FGjvRtHng05TEDw+Hl9wQPAOVcFrAT6H32pIiIi0lw8Cx/OuQogGxhVY9UofHe91MrMfgq8BlzrnHu7oc8xMwNOAwqPvloRERFpLl7f7fI48KqZfQosA24EEoE/A5jZKwDOuZ/7l6/Cd8ZjCrDUzKrPmlQ450r8fe4DPgY2AFHAr/GFj5s8OiYREREJgqfhwzn3VzOLA/4L33wca4CLnHOb/V1qzvdxI74aZ/pf1f4FDPe/7wLMwnc5ZxewCt+4kk+b4xhERETk2Hh95gPn3NPA03WsG17fch3b/Cfwn01Rm4iIiDQ/PdtFREREPKXwISIiIp5S+BARERFPKXyIiIiIpxQ+RERExFMKHyIiIuIphQ8RERHxlMKHiIiIeErhQ0RERDyl8CEiIiKeUvgQERERTyl8iIiIiKcUPkRERMRTCh8iIiLiKYUPERER8ZTCh4iIiHhK4UNEREQ8pfAhIiIinlL4EBEREU8pfIiIiIinFD5ERETEUwofIiIi4imFDxEREfGUwoeIiIh4SuFDREREPKXwISIiIp5S+BARERFPKXyIiIiIpxQ+RERExFMKHyIiIuIphQ8RERHxlMKHiIiIeErhQ0RERDyl8CEiIiKeUvgQERERTyl8iIiIiKcUPkRERMRTCh8iIiLiKYUPERER8ZTCh4iIALBo0SKGDBlCRkYGM2fOPGJ9eXk5119/PRkZGYwcOZItW7YcWvfEE0+QkZHBkCFDWLx48aH2p59+mqFDhzJs2DAmTpzI/v37PTkWadkUPkRE6tDQP8ZtSVVVFXfddRezZ8/mo48+Ys6cOaxfv/6wPq+99hpdunQhOzubm266ienTpwOwfv165s6dy/Lly3nrrbe48847qaqqoqCggFmzZpGVlcXy5cupqqpi7ty5ITg6aWkUPkREatGYf4zbkuzsbE4++WT69OlDREQEY8eOZf78+Yf1mTdvHldddRUA//7v/87SpUtxzjF//nzGjh1LZGQkvXv35uSTTyY7OxuAyspK9u/fT2VlJfv27aNHjx6eH5u0PAofIiK1aMw/xm1JYWEhPXv2PLScmJhIYWFhnX3CwsKIioqipKSkzm0TExO59dZbOe2000hJSSEqKooLLrjAmwOSFk3hQ0SkFo35x7gtcc4d0WZmjepTV3tpaSnz589n1apVrFu3jr179zJ79uymK1paLYUPEZFaNOYf47YkMTGRbdu2HVouKCg44hJJYJ/KykrKysqIiYmpc9v333+fpKQkunbtSnh4OD/+8Y/59NNPvTkgadEUPkREatGYf4zbksGDB5Ofn8/mzZupqKhg7ty5XHjhhYf1GTNmDG+++SYA//jHPzj33HMxMy688ELmzp1LeXk5mzdvJj8/n4yMDHr16sXKlSvZu3cvzjmWLl3KqaeeGorDkxbG8/BhZjeb2ddmtt/Mss3s3Ab6n+/vt9/M8s3sxmPdp4hIQxrzj3FbEhYWRmZmJuPGjeOss87isssuIyUlhQcffPDQWJcJEyZQUlJCRkYGzzzzDPfddx8AKSkpXHbZZQwdOpQrrriCzMxM2rdvzxlnnMGll17KiBEjOPvsszl48CDXXHNNKA9TWgir7dRis32Y2ZXAa8DNwIf+n9cBA5xzW2rpfzKwBngReBo4x//zKufcnKPZZ6D09HSXlZV1zMd118sxx7yPtmrqM9NDXUKLFfvxbaEuQRqwcOFC7rnnHqqqqhg/fjyTJ08OdUnigdjY2Gzn3BmhrqMtC/P48+4AXnLOPedf/pWZXQjcBEyrpf+NQIFz7lf+5Vwz+yEwBZhzlPsUEWmUUaNGMWrUqFCXcZiSs54MdQktlgJ96+HZZRcziwAygAU1Vi0AhtWx2dBa+v8TOMPMwo9ynyIiIhJCXp756Aq0B3bUaN8BjKxjmx7Aolr6h/n3Z8Hu08wmAZP8i9/HxsbmNaZ4OTrP+35P34W6jhYpdkaoK5DWSd+pujTdd6p3U+1Iauf1ZReAmoNMrJa2hvpXt1s9fWrdp3NuFjCr4TKlKZjZSl07FWk6+k5JW+Bl+PgOqMJ3NiNQN448c1Ftex39K4FifCEj2H2KiIhICHk25sM5VwFkAzVHb40Cltex2UcceflkFLDSOXfgKPcpIiIiIeT1ZZfHgVfN7FNgGb67WRKBPwOY2SsAzrmf+/v/GbjVzGYCzwJnA9cCVzd2nxJyusQl0rT0nZJWz9N5PsA3IRhwF5CAbw6P/3TOLfWvex/AOTc8oP/5wBNAKlAAPOyc+3Nj9ykiIiIti+fhQ0RERI5veraLiIiIeErhQ0RERDyl8CFNzsz6m1n3UNchIiItk8Z8SJMws27Az4D/BIrwzcVSCLwNzHHO7QlheSIi0oIofEiTMLOXgAHAe/gmgIsDTgdSgK1ApnNuYcgKFBGRFkPhQ46ZmRmwG7go4LZpA04CfgjcgO9ZCVc651aHrFCRVsbMooDdTn9RSxujMR/SFAYAXwMV1Q3OZ4tz7i3gx/jCyZUhqk+ktXoEuN7MBvmDyBHMLM7jmkSOmcKHNIV84FvgCf9g08P+u/JPg/8yMCYUxYm0RmZ2Nb6zho8B/wAeMbOfmNkpZtbB36cD8IKZDQphqSJB02UXaRJmdha+Ke334ZsKfzFQ4pzbY2YdgVeA/c65CSEsU6TVMLPn8D04MxMYC1wDnALkAfPwfceSgSedcxGhqlPkaCh8SJMxs4HAvcClwB58DwYswvdwwEJgonPui9BVKNI6mFkYvkdGRDnnpga0p+I7GzIOOAHoArzsnPtFSAoVOUoKH9Lk/LfdXgxcBuzH97ydt5xz60NamEgrYmYxQHfn3HoziwAOBA48NbMrgTeAwRrILa2Nwoc0KzNr55w7GOo6RNoC/3gqc85VmdkN+C65dAx1XSLBCgt1AdK2KXiINJ0a36fOwH2hqkXkWOjMh4hIK2Rm4UCVAr60RgofIiIi4inN8yEiIiKeUvgQERERTyl8iIiIiKcUPkRERMRTCh8iIiLiqf8PmowkOXTgeRQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram([ccounts, qcounts], legend=[\"QASM\", \"QC\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that in simulation the initialization to vector $|0\\rangle$ is perfect - out of all 1024 runs all ended with measurement outcome 0. Quantum computers, however, at present have a high faoult rate and so even in this simplest case we can obtain an error - measurement of vector $|0\\rangle$ can give outcome 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}