Runtime.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6fd8c0aa",
   "metadata": {},
   "source": [
    "# Qiskit Runtime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10624c06",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "There are several methods by which one can access IBM Quantum systems.  Each of these routines can be thought of as a [device driver](https://en.wikipedia.org/wiki/Device_driver), providing a software interface to quantum hardware."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f286cd8f",
   "metadata": {},
   "source": [
    "### `backend.run()`\n",
    "\n",
    " - The original method of access to IBM Quantum systems\n",
    " \n",
    " - Access via `qiskit-ibm-provider` package\n",
    " \n",
    " - Can be viewed as an open-source, vendor agnostic device driver\n",
    " \n",
    " - Provides direct access to sampling data from device with no pre- or post-processing\n",
    " \n",
    " - Ideal for users wanting to characterize / benchmark quantum systems, or looking to develop software such as error mitigation techniques\n",
    " \n",
    " - Requires users to possess comprehensive knowledge of hardware details to be successful"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e8b89e",
   "metadata": {},
   "source": [
    "### Qiskit Runtime\n",
    "\n",
    "As quantum computing matures, and users further detached from device physics gain interest, it is important to abstract as much of the knowledge required for using `backend.run()` away from users, so that focus can be on building applications and algorithms.\n",
    "\n",
    "The **Qiskit Runtime** is a quantum device driver designed to abstract many details away from users, and give a high-fidelity managed user experience to those who prefer a more automated execution process.  Specifically, IBM Quantum knows many of the knobs that, to first-order, give best results and can be automatically set for users.  For these features, the options and code can be pushed into a cloud execution environment.\n",
    "\n",
    "The Qiskit Runtime provides:\n",
    "\n",
    "   - A simple interface that abstracts away hardware details, and removes the need for boilerplate code\n",
    "   \n",
    "   - Accessed through the `qiskit-ibm-runtime` package\n",
    "   \n",
    "   - By default, does a variety of pre- and post-processing steps (including error suppression and mitigation) that aim to improve returned results\n",
    "   \n",
    "   - Can be viewed as a closed-source, vendor specific device driver\n",
    "   \n",
    "   - Ideal for users at the level of algorithms and applications\n",
    "   \n",
    "   - Not as flexible as `backend.run()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2582f1f",
   "metadata": {},
   "source": [
    "The Runtime consists of a collection of \"primitives\"; common quantum computational routines that are executed in a dockerized cloud environment.  The two primitives available to date are:\n",
    "\n",
    "| Primitive | What it does | Inputs | Outputs |\n",
    "| --- | --- | --- | --- |\n",
    "| **Sampler** | Samples circuit output distributions, possibly with error mitigation | Quantum circuits | Quasi-probability distributions \\[distributions with (possibly) negative values, but that sums to one\\] |\n",
    "| **Estimator** | Computes expectation values for operators and states defined by circuits, possibly with error mitigation | Quantum circuits and operators | A list of expectation values (and error estimates) |\n",
    "\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "   <div>\n",
    "       <big><b>Info</b></big>\n",
    "   </div>\n",
    "   <br>\n",
    "    Technically <mono>backend.run()</mono> is implemented as a Runtime program like the primitives listed above.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee43f919",
   "metadata": {},
   "source": [
    "In what follows, we will learn how to use the tools available via the Qiskit Runtime to execute quantum circuits on actual quantum hardware."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "058053d2",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e392dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic imports\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "# Optimization from SciPy\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "# Basic Qiskit imports\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.visualization import plot_distribution\n",
    "\n",
    "# Operators from Qiskit and fidelity\n",
    "from qiskit.quantum_info import SparsePauliOp, hellinger_fidelity\n",
    "\n",
    "# Primitives from Qiskit\n",
    "from qiskit.primitives import Sampler as QiskitSampler\n",
    "from qiskit.primitives import Estimator as QiskitEstimator\n",
    "\n",
    "# IBM Quantum Runtime account and primitives\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Estimator, Options, Session\n",
    "\n",
    "# Primitives from the qiskit-aer simulation package\n",
    "from qiskit_aer.primitives import Estimator as AerEstimator\n",
    "\n",
    "# Plotting tools\n",
    "import matplotlib.pyplot as plt\n",
    "try:\n",
    "    plt.style.use('quantum-light')\n",
    "except:\n",
    "    pass\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab5e085",
   "metadata": {},
   "source": [
    "## Runtime service instance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463b14c5",
   "metadata": {},
   "source": [
    "### Saving account information locally (optional)\n",
    "\n",
    "If you have never used IBM Quantum hardware for your local computer, before you begin, you need to save your account information locally.  The token that needs to be saved is available on the [IBM Quantum Platform homepage](https://quantum-computing.ibm.com/).  To save the token you must run:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "30b6d665",
   "metadata": {},
   "source": [
    "QiskitRuntimeService.save_account(channel=\"ibm_quantum\",\n",
    "                                  token=\"TOKEN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672404f4",
   "metadata": {},
   "source": [
    "### Loading account\n",
    "\n",
    "Provided that you have saved your account info locally, you can instantiate the service:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec7200f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "service = QiskitRuntimeService()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61e6081",
   "metadata": {},
   "source": [
    "From the service we can do a variety of things.  For example, query the devices and simulators that we have access to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "246f3d1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMBackend('simulator_stabilizer')>,\n",
       " <IBMBackend('ibmq_quito')>,\n",
       " <IBMBackend('ibm_lagos')>,\n",
       " <IBMBackend('simulator_extended_stabilizer')>,\n",
       " <IBMBackend('ibmq_qasm_simulator_de')>,\n",
       " <IBMBackend('simulator_mps')>,\n",
       " <IBMBackend('simulator_statevector')>,\n",
       " <IBMBackend('ibm_seattle')>,\n",
       " <IBMBackend('ibmq_qasm_simulator')>,\n",
       " <IBMBackend('ibm_canberra')>,\n",
       " <IBMBackend('ibmq_jakarta')>,\n",
       " <IBMBackend('alt_cairo')>,\n",
       " <IBMBackend('ibmq_dublin')>,\n",
       " <IBMBackend('alt_auckland')>,\n",
       " <IBMBackend('ibm_nairobi')>,\n",
       " <IBMBackend('ibm_cairo')>,\n",
       " <IBMBackend('ibm_cleveland')>,\n",
       " <IBMBackend('ibm_quebec')>,\n",
       " <IBMBackend('ibmq_manila')>,\n",
       " <IBMBackend('ibm_bangkok')>,\n",
       " <IBMBackend('ibm_sherbrooke')>,\n",
       " <IBMBackend('ibm_brisbane')>,\n",
       " <IBMBackend('ibm_nazca')>,\n",
       " <IBMBackend('ibmq_mumbai')>,\n",
       " <IBMBackend('ibmq_belem')>,\n",
       " <IBMBackend('ibm_kawasaki')>,\n",
       " <IBMBackend('ibm_prague')>,\n",
       " <IBMBackend('ibmq_lima')>,\n",
       " <IBMBackend('ibm_peekskill')>,\n",
       " <IBMBackend('ibmq_kolkata')>,\n",
       " <IBMBackend('ibm_perth')>,\n",
       " <IBMBackend('ibm_wellington')>,\n",
       " <IBMBackend('ibm_auckland')>,\n",
       " <IBMBackend('ibm_kyiv')>,\n",
       " <IBMBackend('ibm_hanoi')>,\n",
       " <IBMBackend('ibm_ithaca')>,\n",
       " <IBMBackend('ibmq_guadalupe')>,\n",
       " <IBMBackend('alt_ithaca_stretch')>,\n",
       " <IBMBackend('ibm_algiers')>,\n",
       " <IBMBackend('ibm_cusco')>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "service.backends()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62951334",
   "metadata": {},
   "source": [
    "Return a list of the previous identification numbers (`job_id`) for jobs we have submitted to any backend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ef26355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<RuntimeJob('cimm1ast4v6egaotrkr0', 'estimator')>,\n",
       " <RuntimeJob('cimaepct4v6egao7gfr0', 'sampler')>,\n",
       " <RuntimeJob('cimad8d5ear1k1gu19cg', 'sampler')>,\n",
       " <RuntimeJob('cim6v7jc243ejridc970', 'estimator')>,\n",
       " <RuntimeJob('cim5qpd5ear1k1gk4cig', 'estimator')>,\n",
       " <RuntimeJob('cim26jeuepl060qr2j6g', 'sampler')>,\n",
       " <RuntimeJob('cim264cb0ilvhaj6ngt0', 'sampler')>,\n",
       " <RuntimeJob('cim0p355ear1k1g9ul30', 'estimator')>,\n",
       " <RuntimeJob('cim0oq5lipn668bgm0k0', 'estimator')>,\n",
       " <RuntimeJob('cim0kg5lipn668bgirsg', 'estimator')>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "service.jobs()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1981dbfc",
   "metadata": {},
   "source": [
    "or filter for a specific backend, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d60cdcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<RuntimeJob('cild076uepl060qcdbn0', 'sampler')>,\n",
       " <RuntimeJob('cikolikb0ilvhai9s9b0', 'sampler')>,\n",
       " <RuntimeJob('cifi0mju5mrpmccdotmg', 'sampler')>,\n",
       " <RuntimeJob('cifhm7j5n9v6ta7kqqrg', 'sampler')>,\n",
       " <RuntimeJob('chjr0q46f7i49rotibq0', 'circuit-runner')>,\n",
       " <RuntimeJob('chjr0pc6f7i49rotiac0', 'circuit-runner')>,\n",
       " <RuntimeJob('chjr0olnopt07g23a1v0', 'circuit-runner')>,\n",
       " <RuntimeJob('chjr0nj7ca17j34c9mv0', 'circuit-runner')>,\n",
       " <RuntimeJob('chjr0mpike34bjgkc2h0', 'circuit-runner')>,\n",
       " <RuntimeJob('chjr0mepqnphtdto4d5g', 'circuit-runner')>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "service.jobs(backend_name='ibm_algiers')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a604a161",
   "metadata": {},
   "source": [
    "By far, the most important thing you can do from the service instance is to select a backend on which to run your circuits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4410570f",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = service.get_backend('ibm_kyiv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fccaced",
   "metadata": {},
   "source": [
    "## Sampling problems\n",
    "\n",
    "Sampling problems are fundamental to quantum computation.  Indeed, at the end of the day, the only thing a quantum computer can do is sample from probability distributions.  Algorithms such as Grover's, Bernstein-Vazarani, Shor's, Quantum Phase Estimation (QPE), etc. all are bit-string sampling routines.  The `Sampler` routine is the runtime primitive that performs such samplings, returning a distribution to the user."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc84d20",
   "metadata": {},
   "source": [
    "To begin, let us define a simple circuit generator that generates Bernstein-Vazarani circuits where the solution is the all-ones bit-string of a given length.  For example, a length 4 bit-string, requiring 5-qubits in total, is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3144da32",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1685.41x802.667 with 1 Axes>"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "image/png": {
       "height": 680,
       "width": 1395
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def bv_ones_circs(N):\n",
    "    \"\"\"Generate a Bernstein-Vazarani (BV) circuit for an all-ones bit-string\n",
    "    \n",
    "    Parameters:\n",
    "        N (int): Number of qubits in circuit.  I.e. a bit-string of length N-1\n",
    "        \n",
    "    Returns:\n",
    "        QuantumCircuit: BV circuit\n",
    "    \"\"\"\n",
    "    qc = QuantumCircuit(N, N-1)\n",
    "    qc.x(N-1)\n",
    "    qc.h(range(N))\n",
    "    qc.cx(range(N-1), N-1)\n",
    "    qc.h(range(N-1))\n",
    "    qc.barrier()\n",
    "    qc.measure(range(N-1), range(N-1))\n",
    "    return qc\n",
    "\n",
    "N = 5\n",
    "qc = bv_ones_circs(N)\n",
    "qc.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3022503e",
   "metadata": {},
   "source": [
    "As a general rule, for small circuits, it is always good to verify that the circuit does what it is supposed to do.  This can be done with the Qiskit built-in `Sampler`, here called `QiskitSampler`, as it computes the numerically-exact answers via statevector simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "719fdbc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "qiskit_sampler = QiskitSampler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cca7c194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SamplerResult(quasi_dists=[{15: 0.9999999999999986}], metadata=[{}])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_res = qiskit_sampler.run(qc).result()\n",
    "ideal_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f796e52",
   "metadata": {},
   "source": [
    "We see that the result object has two items.  First is `quasi_dists`, short-hand for quasi-probability distributions.  Although a quantum computer samples probability distributions, to accommodate error mitigation techniques, the type of distribution that is returned needs to be more general to include distributions that have negative values, but still sum to one.  These distributions are known as quasi-probability distributions.  The second item, `metadata`, which is empty here, is a dictionary that is used to hold additional data, as we will see.\n",
    "\n",
    "The `quasi_dist` attribute is a list with one element for each circuit given:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "49439f6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{15: 0.9999999999999986}]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_res.quasi_dists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c5ccec",
   "metadata": {},
   "source": [
    "Quasi-distribution states are given as integers rather than bit-strings, and the values are not discrete counts, but probabilities.  This is because, as the number of qubits grows, the size of the bit-string keys grows larger than the equivalent integer representation.  We can covert this data to bit-strings if desired:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4287336a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1111': 0.9999999999999986}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_res.quasi_dists[0].binary_probabilities()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9385c2",
   "metadata": {},
   "source": [
    "In this form we can see that indeed, the output is the all-ones bit-string corresponding to the target integer $2^{N-1}-1$.  We now turn to executing the circuit on a real device:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f965a285",
   "metadata": {},
   "source": [
    "First, we need to instantiate an instance of the `Sampler` from the Qiskit Runtime, passing our target backend to let it know which device to sample from:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "164b8ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = Sampler(backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91519f40",
   "metadata": {},
   "source": [
    "Beyond that, everything works pretty much the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "92445d3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cimaepct4v6egao7gfr0'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job = sampler.run(qc)\n",
    "# Print the job_id just in case\n",
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "638b0e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c8a2c0dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{0: 0.014902304247285943,\n",
       "  1: 0.005586800788698605,\n",
       "  2: 0.0008991701634856824,\n",
       "  3: 0.004712054048460839,\n",
       "  4: 0.001954636813402727,\n",
       "  5: 0.0034107379767734894,\n",
       "  6: 0.0005299578317401404,\n",
       "  7: 0.005804150810854489,\n",
       "  8: 0.0010857303469699508,\n",
       "  9: 0.0003709978266879904,\n",
       "  10: 0.0021122416237010708,\n",
       "  11: 0.01919615110785256,\n",
       "  12: -0.0004176100578495513,\n",
       "  13: 0.009730275089731445,\n",
       "  14: 0.014173211255548929,\n",
       "  15: 0.9159491901266554}]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.quasi_dists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d62f8d7b",
   "metadata": {},
   "source": [
    "We see that our answer is no longer a single integer, but a distribution of integers with weights, some of which might be negative. That there are multiple integers in the distribution is due to noise that is present in all current-day quantum hardware.  That the probabilities can go negative, is a consequence of error mitigation (for measurement errors) being applied behind the scene.  However, we should still recover the correct answer to high-probability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ca9bee07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9159491901266554"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.quasi_dists[0][2**(N-1)-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e0aeb8",
   "metadata": {},
   "source": [
    "or in bit-string notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b68a3882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9159491901266554"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.quasi_dists[0].binary_probabilities()['1'*(N-1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b779c1",
   "metadata": {},
   "source": [
    "We can also plot the results.  It is best to do so with the `plot_distribution` function since our data is not discrete, i.e. not a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e6b7d054",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1050x750 with 1 Axes>"
      ]
     },
     "execution_count": 96,
     "metadata": {
      "image/png": {
       "height": 714,
       "width": 1043
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_distribution(res.quasi_dists[0].binary_probabilities())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d608fa4",
   "metadata": {},
   "source": [
    "Note that, if need be, we can use the job ID to get the job result at a later time from the service object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7f09e1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<RuntimeJob('cimaepct4v6egao7gfr0', 'sampler')>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "service.job(job.job_id())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888a635a",
   "metadata": {},
   "source": [
    "### Metadata\n",
    "\n",
    "Having run on an actual device, we can now take a look at metadata again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "11158988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'shots': 4000,\n",
       "  'readout_mitigation_overhead': 1.5836261284213184,\n",
       "  'readout_mitigation_time': 0.08018968254327774}]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31bd496e",
   "metadata": {},
   "source": [
    "Here, `metadata` now has three items.  The first, is the number of \"shots\", or number of times the circuit was executed and sampled.  The number given here is the default.  The other two characterize the readout error mitigation process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8aa9f5",
   "metadata": {},
   "source": [
    "### Things to be careful of\n",
    "\n",
    "With the generalization to quasi-probability distributions some things that work for canonical probability distributions do not work when negative values are encountered.  One such example is computing the fidelity between two distributions.  For example, if the quasi-probability distribution has negative values then the fidelity calculation will fail due to the use of sqrts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fe179722",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/paul/mambaforge/envs/qiskit/lib/python3.11/site-packages/qiskit/quantum_info/analysis/distance.py:45: RuntimeWarning: invalid value encountered in sqrt\n",
      "  total += (np.sqrt(val) - np.sqrt(q_normed[key])) ** 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hellinger_fidelity(res.quasi_dists[0], res.quasi_dists[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3edb3e94",
   "metadata": {},
   "source": [
    "## Computing expectation values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a8e593",
   "metadata": {},
   "source": [
    "Most sampling algorithms are not suitable for execution on near-term quantum computers.  Instead, algorithms designed to work on noisy quantum processors typically make use of expectation values computed from the sampled distributions.  Computing expectation values can be viewed as nothing more than sampling with additional classical processing.  However, having a specialized function to do so allows removal of a lot of boilerplate code, and lets one use error mitigation techniques that are designed for mitigating expectation values only.\n",
    "\n",
    "We can compute expectation values via the `Estimator` primitive. The `Estimator` requires both a quantum circuit and an operator written as a linear combination of Pauli terms.  Unlike the `Sampler` above where our circuit must have measurements attached to it, circuits sent the `Estimator` **should not** have any measurements!  This is because the `Estimator` will automatically add post-rotation gates to your circuit that are needed for non-diagonal observables to get the measurement basis correct.\n",
    "\n",
    "Let us begin by constructing a circuit of interest.  Here we choose the \"controlled-Hadamard Ladder\" circuit; a circuit with no practical value, but is useful for testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04011099",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cntrl_hadamard_ladder(N, measurements=False):\n",
    "    \"\"\"Create a controlled-Hadamard ladder circuit\n",
    "    \n",
    "    Parameters:\n",
    "        N (int): number\n",
    "        \n",
    "    Returns:\n",
    "        QuantumCircuit\n",
    "    \"\"\"\n",
    "    qc = QuantumCircuit(N)\n",
    "\n",
    "    qc.x(range(N))\n",
    "    qc.h(range(N))\n",
    "\n",
    "    for kk in range(N // 2, 0, -1):\n",
    "        qc.ch(kk, kk - 1)\n",
    "    for kk in range(N // 2, N - 1):\n",
    "        qc.ch(kk, kk + 1)\n",
    "    if measurements:\n",
    "        qc.measure_all()\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d4a6b5",
   "metadata": {},
   "source": [
    "We will create a 6-qubit version of this circuit with no measurements (as required for the `Estimator`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b77d4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 807.495x802.667 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "image/png": {
       "height": 680,
       "width": 684
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 6\n",
    "qc2 = cntrl_hadamard_ladder(N, measurements=False) \n",
    "qc2.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435f385c",
   "metadata": {},
   "source": [
    "Again, for computing expectation values we need not only a circuit that generates a quantum state, but also a operator whose value we wish to measure in that state.  Let us select 3 possible expectation values here, using the `SparsePauliOp` class to represent them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3128f57f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[SparsePauliOp(['ZZZZZZ'],\n",
       "               coeffs=[1.+0.j]),\n",
       " SparsePauliOp(['IIIIZZ'],\n",
       "               coeffs=[1.+0.j]),\n",
       " SparsePauliOp(['IIIIIZ'],\n",
       "               coeffs=[1.+0.j])]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opers = [SparsePauliOp('Z'*N), SparsePauliOp('I'*(N-2)+'ZZ'), SparsePauliOp('I'*(N-1)+'Z')]\n",
    "opers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af7386f8",
   "metadata": {},
   "source": [
    "Like before, since the problem size is small, we can directly compute the ideal answer using the primitives built into Qiskit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e59bf4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "qiskit_estimator = QiskitEstimator()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73fd9dbb",
   "metadata": {},
   "source": [
    "Because we have three operators of interest, we need to send the same circuit each time (send one circuit per operator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d64c5f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([ 0.4375,  0.875 , -0.875 ]), metadata=[{}, {}, {}])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_res = qiskit_estimator.run([qc2]*3, opers).result()\n",
    "ideal_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143e2642",
   "metadata": {},
   "source": [
    "The answers obtained are the ideal answers with no noise or finite sampling effects, and are returned as floating-point values in a NumPy array as the `values` attribute of the `EstimatorResult` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9b30c761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.4375,  0.875 , -0.875 ])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_res.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c63ab80",
   "metadata": {},
   "source": [
    "Making a call to real hardware is similar to what we did for the `Sampler`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "da2bca15",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = Estimator(backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bde95411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([ 0.40582488,  0.82278218, -0.84675938]), metadata=[{'variance': 1.943515457105084, 'shots': 4000, 'readout_mitigation_num_twirled_circuits': 16, 'readout_mitigation_shots_calibration': 8192}, {'variance': 0.5761564525274007, 'shots': 4000, 'readout_mitigation_num_twirled_circuits': 16, 'readout_mitigation_shots_calibration': 8192}, {'variance': 0.3326239158638849, 'shots': 4000, 'readout_mitigation_num_twirled_circuits': 16, 'readout_mitigation_shots_calibration': 8192}])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_res = estimator.run([qc2]*3, opers).result()\n",
    "est_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a1bccc70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.40582488,  0.82278218, -0.84675938])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_res.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c276c4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'variance': 1.943515457105084,\n",
       "  'shots': 4000,\n",
       "  'readout_mitigation_num_twirled_circuits': 16,\n",
       "  'readout_mitigation_shots_calibration': 8192},\n",
       " {'variance': 0.5761564525274007,\n",
       "  'shots': 4000,\n",
       "  'readout_mitigation_num_twirled_circuits': 16,\n",
       "  'readout_mitigation_shots_calibration': 8192},\n",
       " {'variance': 0.3326239158638849,\n",
       "  'shots': 4000,\n",
       "  'readout_mitigation_num_twirled_circuits': 16,\n",
       "  'readout_mitigation_shots_calibration': 8192}]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_res.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef077030",
   "metadata": {},
   "source": [
    "New in the metadata is the `variance` field that takes into account the fact that our estimate of the expectation value has some uncertainty with it.  This combined with the number of shots is useful in error analysis.\n",
    "\n",
    "Additionally, the `readout_mitigation_*` items relate to readout error mitigation, but differ from those in the `Sampler` as it uses a different method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2764c78e",
   "metadata": {},
   "source": [
    "## Simulators in Runtime\n",
    "\n",
    "The simulation capabilities are also available in the Qiskit Runtime.  Namely I can ask the service to show us the simulators using the `simulator` keyword argument to filter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "93ba3fae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMBackend('simulator_stabilizer')>,\n",
       " <IBMBackend('simulator_extended_stabilizer')>,\n",
       " <IBMBackend('ibmq_qasm_simulator_de')>,\n",
       " <IBMBackend('simulator_mps')>,\n",
       " <IBMBackend('simulator_statevector')>,\n",
       " <IBMBackend('ibmq_qasm_simulator')>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "service.backends(simulator=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "738b4ab4",
   "metadata": {},
   "source": [
    "The most useful for us will be the `ibmq_qasm_simulator` that will sample the distribution generated by a circuit, possibly under noisy evolution, a finite number of times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1220e589",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = service.get_backend('ibmq_qasm_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab21d80",
   "metadata": {},
   "source": [
    "## Setting options\n",
    "\n",
    "There are many options available to users of Runtime.  Chief among these is the number of shots, but there are several other important options that control how circuits are pre-processed, executed, and post-processed.  We will show how to set some of these options now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2d4fe1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([0.4462073]), metadata=[{'variance': 0.8085937499999999, 'shots': 12345}])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qiskit_estimator.run(qc2, opers[0], shots=12345).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8dc751eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([0.44949372]), metadata=[{'shots': 12345, 'variance': (0.7979553937434967+0j), 'simulator_metadata': [{'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.008761083, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'device': 'CPU', 'active_input_qubits': [0, 1, 2, 3, 4, 5], 'measure_sampling': True, 'num_clbits': 6, 'input_qubit_map': [[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], 'num_qubits': 6, 'method': 'statevector', 'fusion': {'applied': False, 'max_fused_qubits': 5, 'threshold': 14, 'enabled': True}}]}])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aer_estimator.run(qc2, opers[0], shots=12345).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "11451fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([0.44139467]), metadata=[{'variance': 0.8051707431678025, 'shots': 54321}])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_estimator = Estimator(sim)\n",
    "sim_estimator.run(qc2, opers[0], shots=54321).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6211c7",
   "metadata": {},
   "source": [
    "### Qiskit Runtime `Options` class\n",
    "\n",
    "The Qiskit runtime also supports setting options via an `Options` class.  We can see the default options by creating an empty `Options` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df39512c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Options(optimization_level=None, resilience_level=None, max_execution_time=None, transpilation=TranspilationOptions(skip_transpilation=False, initial_layout=None, layout_method=None, routing_method=None, approximation_degree=None), resilience=ResilienceOptions(noise_amplifier='TwoQubitAmplifier', noise_factors=(1, 3, 5), extrapolator='LinearExtrapolator'), execution=ExecutionOptions(shots=4000, init_qubits=True), environment=EnvironmentOptions(log_level='WARNING', callback=None, job_tags=[]), simulator=SimulatorOptions(noise_model=None, seed_simulator=None, coupling_map=None, basis_gates=None))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options = Options()\n",
    "options"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "476286d0",
   "metadata": {},
   "source": [
    "There are top-level / commonly used settings such as `optimization_level`, `resilience_level`, and `max_execution_time`, and a collection of sub-options, sorted by what they do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c5e19925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExecutionOptions(shots=4000, init_qubits=True)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options.execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b57f1faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimulatorOptions(noise_model=None, seed_simulator=None, coupling_map=None, basis_gates=None)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options.simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5bea918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TranspilationOptions(skip_transpilation=False, initial_layout=None, layout_method=None, routing_method=None, approximation_degree=None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options.transpilation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9e7dc990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResilienceOptions(noise_amplifier='TwoQubitAmplifier', noise_factors=(1, 3, 5), extrapolator='LinearExtrapolator')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options.resilience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d05294f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Options(optimization_level=None, resilience_level=None, max_execution_time=None, transpilation=TranspilationOptions(skip_transpilation=False, initial_layout=None, layout_method=None, routing_method=None, approximation_degree=None), resilience=ResilienceOptions(noise_amplifier='TwoQubitAmplifier', noise_factors=(1, 3, 5), extrapolator='LinearExtrapolator'), execution=ExecutionOptions(shots=12345, init_qubits=True), environment=EnvironmentOptions(log_level='WARNING', callback=None, job_tags=[]), simulator=SimulatorOptions(noise_model=None, seed_simulator=None, coupling_map=None, basis_gates=None))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options.execution.shots = 12345\n",
    "options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "75424ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Options(optimization_level=None, resilience_level=None, max_execution_time=None, transpilation=TranspilationOptions(skip_transpilation=False, initial_layout=None, layout_method=None, routing_method=None, approximation_degree=None), resilience=ResilienceOptions(noise_amplifier='TwoQubitAmplifier', noise_factors=(1, 3, 5), extrapolator='LinearExtrapolator'), execution=ExecutionOptions(shots=54321, init_qubits=True), environment=EnvironmentOptions(log_level='WARNING', callback=None, job_tags=[]), simulator=SimulatorOptions(noise_model=None, seed_simulator=None, coupling_map=None, basis_gates=None))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Options(execution={'shots':54321})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6de52d98",
   "metadata": {},
   "source": [
    "In order to use the `Option` class, you must pass it when initializing a primitive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ffc8bc2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_estimator = Estimator(sim, options=options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ef1b3c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([0.43232078]), metadata=[{'variance': 0.8130987452183503, 'shots': 12345}])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_estimator.run(qc2, opers[0]).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f268404",
   "metadata": {},
   "source": [
    "But I can still overload the value in `run()` if I want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "36033209",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([0.8]), metadata=[{'variance': 0.3599999999999999, 'shots': 10}])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_estimator.run(qc2, opers[0], shots=10).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2899d4eb",
   "metadata": {},
   "source": [
    "## Runtime Sessions\n",
    "\n",
    "Part of the Qiskit Runtime is the ability to combine multiple calls to primitives together in what are called  **Sessions**.  The notion of session closely follows that of a [networking session](https://en.wikipedia.org/wiki/Session_(computer_science)).  Once a session is opened, users can submit multiple primitive jobs to a session, of either type of primitive, and .  Each of these jobs gets executed sequentially until either there are no more jobs to process or the session time limit is reached.\n",
    "\n",
    "In general, there are two ways in which sessions can be utilized.  First, an user can execute a collection of one or more jobs that are independent.  Second, one can have two or more jobs that are interconnected, with later jobs conditioned on the results from those previously executed.  This latter case is typical for iterative algorithms such as variational methods."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956de007",
   "metadata": {},
   "source": [
    "![independent](images/runtime2/independent.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a79d1f",
   "metadata": {},
   "source": [
    "![related](images/runtime2/related.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4165239",
   "metadata": {},
   "source": [
    "### Sessions for independent jobs\n",
    "\n",
    "There are two ways to ways to make use of sessions.  First, we can directly make a session instance using the backend instance that we want to run on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "95100480",
   "metadata": {},
   "outputs": [],
   "source": [
    "session = Session(backend=backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3a4e444",
   "metadata": {},
   "source": [
    "when we create either a `Sampler` or an `Estimator`, if we pass the `session` to it, it tells the primitives that the jobs should be logically connected, and executed as a group on the quantum processor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1a6e2404",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = Sampler(session=session)\n",
    "estimator = Estimator(session=session)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0ef067",
   "metadata": {},
   "source": [
    "We can now use these primitives to launch multiple jobs that will execute one after another on the QPU.  For example, lets run our previous two circuits back-to-back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c5d03969",
   "metadata": {},
   "outputs": [],
   "source": [
    "samp_job = sampler.run(qc)\n",
    "est_job = estimator.run(qc2, opers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84ca4fc0",
   "metadata": {},
   "source": [
    "That's it.  We can continue to add jobs to the session at any point, provided that the total runtime of the session is not longer than the default 8hr time limit.  Once all the results are returned, we can **optionally** close the session:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cdac2479",
   "metadata": {},
   "outputs": [],
   "source": [
    "samp_res = samp_job.result()\n",
    "est_res = est_job.result()\n",
    "session.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be2c200",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "   <div>\n",
    "       <big><b>Danger</b></big>\n",
    "   </div>\n",
    "   <br>\n",
    "    If you close a session with unfinished jobs in it, then those jobs will be canceled!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "761dccc3",
   "metadata": {},
   "source": [
    "Because calling `session.close()` is messy at present, we suggest just letting the session go idle after 5 min of inactivity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad52304",
   "metadata": {},
   "source": [
    "There is another way to use sessions, and that is via Python contexts (`with` statements).  The syntax is a bit opaque, so lets go through it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d7db4d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with Session(backend=backend):\n",
    "    # We need to pass nothing to the primitives because they \"know\"\n",
    "    # about the context block\n",
    "    sampler = Sampler()\n",
    "    estimator = Estimator()\n",
    "    samp_job = sampler.run(qc)\n",
    "    est_job = estimator.run(qc2, opers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b654161",
   "metadata": {},
   "source": [
    "At the end of the context block, the session is not closed, and one does not have access to it anymore."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5bf053",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "   <div>\n",
    "       <big><b>Upcoming feature</b></big>\n",
    "   </div>\n",
    "   <br>\n",
    "    There will be an \"I'm done\" feature for sessions that will make life much easier starting around August!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bfe2695",
   "metadata": {},
   "source": [
    "## The headaches of hardware\n",
    "\n",
    "By using the Runtime we have up until now avoided any discussion of how our circuits are being executed on hardware, nor have we discussed how the properties of hardware effect the outcomes of circuit execution, in particular how things like device noise cause deviations from ideal behavior.  \n",
    "\n",
    "To get a feel for this, let us run our all-ones BV circuits, varying the circuit width from 2->19 qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "3a70235e",
   "metadata": {},
   "outputs": [],
   "source": [
    "widths = range(2, 20)\n",
    "circs = [bv_ones_circs(N) for N in widths]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4dc4b18",
   "metadata": {},
   "source": [
    "Since BV is a sampling problem, we will use the sampler primitive. We could use a session but, when possible, it is best to batch as many jobs as possible into a single job.  As such, we will send a single sampler job containing all of our circuits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "429c4ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = Sampler(backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "a9940ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "bv_job = sampler.run(circs, shots=int(1e4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f304fa7c",
   "metadata": {},
   "source": [
    "Since the target bit-string is always `'1'*(N-1)`, we can extract that probability, being careful to check if the value is there or not using `get()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "ce73728d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dists = bv_job.result().quasi_dists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ffd5f535",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_probs = [dists[idx].binary_probabilities().get('1'*(N-1), 0) for idx, N in enumerate(widths)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05801e5",
   "metadata": {},
   "source": [
    "Here I will do a fit, so that we can better see what is going on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "48d899d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting function\n",
    "def sigmoid(x, L ,x0, k, b):\n",
    "    y = L / (1.0 + np.exp(-k*(x-x0))) + b\n",
    "    return y\n",
    "\n",
    "# Initial guess of parameters\n",
    "p0 = [max(target_probs),\n",
    "      np.median(widths),\n",
    "      1,\n",
    "      min(target_probs)]\n",
    "\n",
    "# Do the fitting\n",
    "popt, pcov = curve_fit(sigmoid, widths, target_probs, p0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "114b0bfd",
   "metadata": {},
   "source": [
    "Plot the data and fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "15346065",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 832,
       "width": 1161
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(widths, target_probs, '--o', lw=1.5, label='Data')\n",
    "ax.plot(widths, sigmoid(widths, *popt), '-', lw=3, label='Logistics fit')\n",
    "ax.set_xlim([1.5,19.5])\n",
    "ax.set_ylim([-0.05, 1.05])\n",
    "ax.set_xlabel('# qubits')\n",
    "ax.set_ylabel('Success probability')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "308653e6",
   "metadata": {},
   "source": [
    "There is a lot of information in this plot.  First the general trend is that, as the number of qubits grows in the circuit, the overall success probability goes down.  This is not too surprising given that each operation has some error, and the more qubits there are, the more operations need to be performed.\n",
    "\n",
    "However, this decrease in success is not uniform.  Up until ~5 qubits the decrease is approximately linear, after which there is a sharp drop in the success rate until finally there is no signal at all.  As we will see, the shape of this curve is dictated by the limited qubit-qubit connectivity of IBM Quantum hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "315fcbce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}