{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1c7ce88",
   "metadata": {},
   "source": [
    "# IBM Optimizations using Qiskit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "varied-shame",
   "metadata": {},
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Infleqtion/client-superstaq/blob/main/docs/source/optimizations/ibm/ibmq_compile_qss.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Infleqtion/client-superstaq/HEAD?labpath=docs/source/optimizations/ibm/ibmq_compile_qss.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c25005",
   "metadata": {},
   "source": [
    "Below is a brief tutorial on Superstaq optimizations for IBM Quantum superconducting qubit devices. For more information on IBM Quantum, visit their website [here](https://www.ibm.com/quantum)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a7d9b5",
   "metadata": {},
   "source": [
    "## Imports and API Token\n",
    "\n",
    "This example tutorial notebook uses `qiskit-superstaq`, our Superstaq client for Qiskit; you can try it out by running `pip install qiskit-superstaq[examples]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4316dd2c-bad2-42f6-ab39-383a1ba6a6d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import qiskit_superstaq as qss\n",
    "except ImportError:\n",
    "    print(\"Installing qiskit-superstaq...\")\n",
    "    %pip install --quiet 'qiskit-superstaq[examples]'\n",
    "    print(\"Installed qiskit-superstaq.\")\n",
    "    print(\"You may need to restart the kernel to import newly installed packages.\")\n",
    "    import qiskit_superstaq as qss\n",
    "\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import qiskit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4d8cf9",
   "metadata": {},
   "source": [
    "To interface Superstaq via Qiskit, we must first instantiate a provider in `qiskit-superstaq` with `SuperstaqProvider()`. We then supply a Superstaq API token (or key) by either providing the API token as an argument of `qss.SuperstaqProvider()` or by setting it as an environment variable (see more details [here](https://superstaq.readthedocs.io/en/latest/get_started/basics/basics_qss.html#Set-up-access-to-Superstaq%E2%80%99s-API))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d8d329d-5c52-44bd-9e35-3052268a4530",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Provide your Superstaq API key using the \"api_key\" argument\n",
    "provider = qss.SuperstaqProvider()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4828ee8",
   "metadata": {},
   "source": [
    "## Single Circuit Compilation\n",
    "\n",
    "Let us start by creating an example Qiskit circuit that we will then compile and optimize for the 156-qubit IBM Quantum `Kingston` processor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e68dcda-f84a-4b93-aea0-f64c1a5108f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 663.998x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a two-qubit qiskit circuit\n",
    "rng = np.random.default_rng(random.getrandbits(128))\n",
    "theta = rng.uniform(0, 4 * np.pi)\n",
    "circuit = qiskit.QuantumCircuit(2)\n",
    "circuit.cx(0, 1)\n",
    "circuit.rz(theta, 1)\n",
    "circuit.cx(0, 1)\n",
    "circuit.measure_all()\n",
    "\n",
    "# Draw circuit for visualization\n",
    "circuit.draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2116e292",
   "metadata": {},
   "source": [
    "We will now compile the above circuit to IBM's `Kingston` processor and visualize the differences by drawing the compiled circuit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "135bc08f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1476.28x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compile with ibmq compile\n",
    "compiler_output = provider.ibmq_compile(circuit, target=\"ibmq_kingston_qpu\")\n",
    "\n",
    "# Call circuit from the compiler output to get the corresponding output circuit\n",
    "output_circuit = compiler_output.circuit\n",
    "\n",
    "# Visualize the compiled circuit\n",
    "output_circuit.draw(\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa165427",
   "metadata": {},
   "source": [
    "The resulting output is now a circuit compiled to `Kingston`'s native operations. With Superstaq compilation, you can also observe the gates and gate times for the compiled circuit using Qiskit’s timeline drawer (see [Qiskit Timeline Visualizations Documentation](https://docs.quantum.ibm.com/api/qiskit/qiskit.visualization.timeline_drawer)). \n",
    "\n",
    "Additionally, to ensure that the drawer has all the information on the instruction durations, we must also provide the `qiskit.transpiler.Target` of the corresponding backend the timeline is being generated for. It can be retrieved like so: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12e7cd22",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend_target = provider.get_backend(\"ibmq_kingston_qpu\").target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ca9f750",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x300 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The pulse gate circuit is obtained via the `pulse_gate_circuit` attribute\n",
    "pulse_circuit = compiler_output.pulse_gate_circuit\n",
    "\n",
    "custom_style = {\"formatter.general.fig_width\": 30, \"formatter.general.fig_unit_height\": 1}\n",
    "style = qiskit.visualization.timeline.IQXStandard(**custom_style)\n",
    "\n",
    "qiskit.visualization.timeline_drawer(\n",
    "    pulse_circuit, idle_wires=False, style=style, target=backend_target\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c3a395",
   "metadata": {},
   "source": [
    "## Multiple Circuits Compilation\n",
    "\n",
    "All the functionalities we have seen so far can also be used on a multiple-circuit input as well. To illustrate this, let us create a different example two-qubit circuit: a Bell-state circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1520302f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 580.387x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create second circuit\n",
    "bell_circuit = qiskit.QuantumCircuit(2)\n",
    "bell_circuit.h(0)\n",
    "bell_circuit.cx(0, 1)\n",
    "bell_circuit.measure_all()\n",
    "\n",
    "# Draw second circuit for visualization\n",
    "bell_circuit.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c055c11",
   "metadata": {},
   "source": [
    "By passing multiple circuits as a list to the `ibmq_compile` endpoint, we can compile all of them individually with a single call to the endpoint. This will return all the corresponding compiled circuits and pulse gate circuits back as a list, like so:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9fa01c3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of circuits\n",
    "circuit_list = [circuit, bell_circuit]\n",
    "\n",
    "# Compile list of circuits\n",
    "compiler_output_list = provider.ibmq_compile(circuit_list, \"ibmq_kingston_qpu\")\n",
    "\n",
    "# The list of compiled output circuits is stored in the `circuits` attribute instead of `circuit`. Likewise for\n",
    "# pulse sequences.\n",
    "output_circuits = compiler_output_list.circuits\n",
    "pulse_gate_circuits = compiler_output_list.pulse_gate_circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "736f6d27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiled circuit 1 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1476.28x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the first compiled circuit\n",
    "print(\"Compiled circuit 1 \\n\")\n",
    "output_circuits[0].draw(\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f3c6525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x300 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the pulse gate circuit for the first compiled circuit\n",
    "qiskit.visualization.timeline_drawer(\n",
    "    pulse_gate_circuits[0], idle_wires=False, style=style, target=backend_target\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "714c2859",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiled circuit 2 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 891.005x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the second compiled circuit\n",
    "print(\"Compiled circuit 2 \\n\")\n",
    "output_circuits[1].draw(\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91121471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x300 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the pulse gate circuit for the first compiled circuit\n",
    "qiskit.visualization.timeline_drawer(\n",
    "    pulse_gate_circuits[1], idle_wires=False, style=style, target=backend_target\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490036bb",
   "metadata": {},
   "source": [
    "## Using the Superstaq Simulator\n",
    "\n",
    "Lastly, we will show (a) how to execute a circuit on a backend and (b) how to simulate circuit execution. Simulation is available to free trial users, and can be done by passing the `\"dry-run\"` method parameter when calling `run()` on the Superstaq provider."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "331aec28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11': 491, '00': 509}\n"
     ]
    }
   ],
   "source": [
    "# Get IBM Quantum backend from provider\n",
    "backend = provider.get_backend(\"ibmq_kingston_qpu\")\n",
    "job = backend.run(\n",
    "    bell_circuit,\n",
    "    shots=1000,\n",
    "    method=\"dry-run\",  # Specify \"dry-run\" as the method to run Superstaq simulation\n",
    ")\n",
    "\n",
    "# Get the counts from the measurement\n",
    "print(job.result().get_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324f3dd0",
   "metadata": {},
   "source": [
    "### Addendum: Compiling via Superstaq backend\n",
    "You can also compile circuits via the `compile` method of the backend directly. This is identical to calling `ibmq_compile` on the provider, but more closely matches Qiskit syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b45ebadf",
   "metadata": {},
   "outputs": [],
   "source": [
    "compiler_output = backend.compile(bell_circuit)\n",
    "output_circuit = compiler_output.circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9eb7512b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 891.005x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the compiled circuit\n",
    "output_circuit.draw(\"mpl\", idle_wires=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d94c141c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x300 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the pulse gate circuit\n",
    "qiskit.visualization.timeline_drawer(\n",
    "    compiler_output.pulse_gate_circuit, idle_wires=False, style=style, target=backend.target\n",
    ")"
   ]
  }
 ],
 "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": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}