qiskit-documentation/docs/tutorials/ai-transpiler-introduction.ipynb

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    "{/* cspell:ignore fontsize */}\n",
    "\n",
    "# AI Transpiler Introduction\n",
    "*Estimated QPU usage: None (NOTE: no execution was done in this notebook as notebook is focused on the transpilation process)*\n",
    "\n",
    "## Background\n",
    "\n",
    "The **Qiskit AI-powered transpiler service (QTS)** introduces machine learning-based optimizations in both **routing** and **synthesis** passes. These AI modes have been designed to tackle the limitations of traditional transpilation, particularly for **large-scale circuits** and **complex hardware topologies**.\n",
    "\n",
    "**Key Features of the AI-Powered Transpiler**:\n",
    "- **Routing Passes**: AI-powered routing can dynamically adjust qubit paths based on the specific circuit and backend, reducing the need for excessive SWAP gates.\n",
    "    - `AIRouting`: Layout selection and circuit routing\n",
    "\n",
    "- **Synthesis Passes**: AI techniques optimize the decomposition of multi-qubit gates, minimizing the number of 2-qubit gates, which are typically more error-prone.\n",
    "    - `AICliffordSynthesis`: Clifford gate synthesis\n",
    "    - `AILinearFunctionSynthesis`: Linear function circuit synthesis\n",
    "    - `AIPermutationSynthesis`: Permutation circuit synthesis\n",
    "    - `AIPauliNetworkSynthesis`: Pauli Network circuit synthesis (only available in the Qiskit Transpiler Service, not in local environment)\n",
    "\n",
    "**Comparison with Traditional Transpilation**: The standard Qiskit transpiler is a robust tool that can handle a broad spectrum of quantum circuits effectively. However, when circuits grow larger in scale or hardware configurations become more complex, the **Qiskit AI-powered transpiler service (QTS)** can deliver additional optimization gains. By leveraging **learned models** for routing and synthesis, QTS further refines circuit layouts and reduces overhead for challenging or large-scale quantum tasks.\n",
    "\n",
    "\n",
    "In this tutorial, we will evaluate the AI modes using **both routing and synthesis** passes, comparing the results to traditional transpilation to highlight where AI offers performance gains.\n",
    "\n",
    "For more information on the details of QTS, please refer to the [documentation](https://docs.quantum.ibm.com/guides/ai-transpiler-passes).\n",
    "\n",
    "\n",
    "### Why Use AI for Quantum Circuit Transpilation?\n",
    "\n",
    "As quantum circuits grow in size and complexity, traditional transpilation methods struggle to optimize layouts and reduce gate counts efficiently. Larger circuits, particularly those involving hundreds of qubits, impose significant challenges on routing and synthesis due to device constraints, limited connectivity, and qubit error rates.\n",
    "\n",
    "This is where **AI-powered transpilation** offers a potential solution. By leveraging machine learning techniques, the AI-powered transpiler in Qiskit can make smarter decisions about **qubit routing** and **gate synthesis**, leading to better optimization of large-scale quantum circuits.\n",
    "\n",
    "### Brief Benchmarking Results\n",
    "![Graph showing AI transpiler performance against Qiskit](/images/tutorials/ai-transpiler-introduction/ai-transpiler-benchmarks.avif)\n",
    "\n",
    "\n",
    "In benchmarking tests, the **Qiskit AI-powered transpiler service (QTS)** consistently produced **shallower, higher-quality circuits** compared to the standard Qiskit transpiler. For these tests, we used **Qiskit’s default pass manager strategy**—configured via the [`generate_preset_passmanager`](https://docs.quantum.ibm.com/api/qiskit/transpiler_preset). While this default strategy is often effective, it can struggle with larger or more complex circuits. By contrast, QTS’s AI-powered passes achieved an **average 24% reduction** in 2-qubit gate counts and a **36% reduction** in circuit depth for large circuits (100+ qubits) when transpiling to the **heavy hex topology** of IBM Quantum hardware. For more information on these benchmarks, please refer to the following blog post [\\[1\\]](#references).\n",
    "\n",
    "In this notebook, we will explore the **key benefits** of Qiskit AI-powered transpiler service and how it compares to traditional methods."
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    "## Requirements\n",
    "\n",
    "Before starting this tutorial, ensure that you have the following installed:\n",
    "\n",
    "* Qiskit SDK 1.0 or later, with visualization support (`pip install 'qiskit[visualization]'`)\n",
    "* Qiskit Runtime (`pip install qiskit-ibm-runtime`) 0.22 or later\n",
    "* Qiskit IBM Transpiler (`pip install qiskit-ibm-transpiler`)\n",
    "* Qiskit IBM AI Local Transpiler (`pip install qiskit_ibm_ai_local_transpiler`)"
   ]
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   "source": [
    "## Setup"
   ]
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    "from qiskit import QuantumCircuit\n",
    "from qiskit.circuit.library import EfficientSU2, QuantumVolume, QFT\n",
    "from qiskit.transpiler import PassManager\n",
    "from qiskit.circuit.random import random_circuit, random_clifford_circuit\n",
    "from qiskit.transpiler import generate_preset_pass_manager, CouplingMap\n",
    "from qiskit_ibm_transpiler.transpiler_service import TranspilerService\n",
    "from qiskit_ibm_transpiler.ai.collection import CollectPermutations\n",
    "from qiskit_ibm_transpiler.ai.synthesis import AIPermutationSynthesis\n",
    "from qiskit.circuit.library import Permutation\n",
    "from qiskit.synthesis.permutation import (\n",
    "    synth_permutation_acg,\n",
    "    synth_permutation_depth_lnn_kms,\n",
    "    synth_permutation_basic,\n",
    ")\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "\n",
    "# Used for generating permutation circuits in part 2 for comparison\n",
    "def generate_permutation_circuit(width, pattern):\n",
    "    circuit = QuantumCircuit(width)\n",
    "    circuit.append(\n",
    "        Permutation(num_qubits=width, pattern=pattern),\n",
    "        qargs=range(width),\n",
    "    )\n",
    "    return circuit\n",
    "\n",
    "\n",
    "def analyze_transpilation(\n",
    "    results, circuit, pattern, pattern_id, coupling_map\n",
    "):\n",
    "    # AI Pass Manager\n",
    "    pm_ai = PassManager(\n",
    "        [\n",
    "            CollectPermutations(\n",
    "                do_commutative_analysis=True, max_block_size=27\n",
    "            ),\n",
    "            AIPermutationSynthesis(coupling_map=coupling_map),\n",
    "        ]\n",
    "    )\n",
    "    start = time.time()\n",
    "    qc_ai = pm_ai.run(circuit).decompose(reps=3)\n",
    "    ai_time = time.time() - start\n",
    "    results.append(\n",
    "        {\n",
    "            \"Pattern\": pattern_id,\n",
    "            \"Method\": \"AI\",\n",
    "            \"Depth\": qc_ai.depth(),\n",
    "            \"Gates(2q)\": qc_ai.size(),\n",
    "            \"Time (s)\": ai_time,\n",
    "        }\n",
    "    )\n",
    "\n",
    "    # Non-AI Pass Manager\n",
    "    pm_no_ai = generate_preset_pass_manager(\n",
    "        coupling_map=coupling_map, optimization_level=1\n",
    "    )\n",
    "\n",
    "    # ACG Method\n",
    "    qc_acg = synth_permutation_acg(pattern)\n",
    "    start = time.time()\n",
    "    qc_acg = pm_no_ai.run(qc_acg).decompose(reps=3)\n",
    "    acg_time = time.time() - start\n",
    "    results.append(\n",
    "        {\n",
    "            \"Pattern\": pattern_id,\n",
    "            \"Method\": \"ACG\",\n",
    "            \"Depth\": qc_acg.depth(),\n",
    "            \"Gates(2q)\": qc_acg.size(),\n",
    "            \"Time (s)\": acg_time,\n",
    "        }\n",
    "    )\n",
    "\n",
    "    # Depth-LNN-KMS Method\n",
    "    qc_depth_lnn_kms = synth_permutation_depth_lnn_kms(pattern)\n",
    "    start = time.time()\n",
    "    qc_depth_lnn_kms = pm_no_ai.run(qc_depth_lnn_kms).decompose(reps=3)\n",
    "    depth_lnn_time = time.time() - start\n",
    "    results.append(\n",
    "        {\n",
    "            \"Pattern\": pattern_id,\n",
    "            \"Method\": \"Depth-LNN-KMS\",\n",
    "            \"Depth\": qc_depth_lnn_kms.depth(),\n",
    "            \"Gates(2q)\": qc_depth_lnn_kms.size(),\n",
    "            \"Time (s)\": depth_lnn_time,\n",
    "        }\n",
    "    )\n",
    "\n",
    "    # Basic Method\n",
    "    qc_basic = synth_permutation_basic(pattern)\n",
    "    start = time.time()\n",
    "    qc_basic = pm_no_ai.run(qc_basic).decompose(reps=3)\n",
    "    basic_time = time.time() - start\n",
    "    results.append(\n",
    "        {\n",
    "            \"Pattern\": pattern_id,\n",
    "            \"Method\": \"Basic\",\n",
    "            \"Depth\": qc_basic.depth(),\n",
    "            \"Gates(2q)\": qc_basic.size(),\n",
    "            \"Time (s)\": basic_time,\n",
    "        }\n",
    "    )\n",
    "\n",
    "\n",
    "# Used for benchmarking the circuits in part 3 for comparison\n",
    "def run_transpilation(qc, no_ai_service, ai_service):\n",
    "    # Standard transpilation\n",
    "    time_start = time.time()\n",
    "    qc_tr_no_ai = no_ai_service.run(qc)\n",
    "    time_end = time.time()\n",
    "\n",
    "    time_no_ai = time_end - time_start\n",
    "    depth_no_ai = qc_tr_no_ai.depth(lambda x: x.operation.num_qubits == 2)\n",
    "    gate_count_no_ai = qc_tr_no_ai.size()\n",
    "\n",
    "    # AI transpilation\n",
    "    time_start = time.time()\n",
    "    qc_tr_ai = ai_service.run(qc)\n",
    "    time_end = time.time()\n",
    "\n",
    "    time_ai = time_end - time_start\n",
    "    depth_ai = qc_tr_ai.depth(lambda x: x.operation.num_qubits == 2)\n",
    "    gate_count_ai = qc_tr_ai.size()\n",
    "\n",
    "    return {\n",
    "        \"depth_no_ai\": depth_no_ai,\n",
    "        \"gate_count_no_ai\": gate_count_no_ai,\n",
    "        \"time_no_ai\": time_no_ai,\n",
    "        \"depth_ai\": depth_ai,\n",
    "        \"gate_count_ai\": gate_count_ai,\n",
    "        \"time_ai\": time_ai,\n",
    "    }\n",
    "\n",
    "\n",
    "# Creates a Bernstein-Vazirani circuit given the number of qubits\n",
    "def create_bv_circuit(num_qubits):\n",
    "    qc = QuantumCircuit(num_qubits, num_qubits - 1)\n",
    "    qc.x(num_qubits - 1)\n",
    "    qc.h(qc.qubits)\n",
    "    for i in range(num_qubits - 1):\n",
    "        qc.cx(i, num_qubits - 1)\n",
    "    qc.h(qc.qubits[:-1])\n",
    "    return qc"
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    "# Part I. Qiskit Patterns\n",
    "\n",
    "Let's now see how to use the **AI transpiler service** with a simple quantum circuit and using Qiskit patterns. The key is creating a **TranspilerService instance** and specifying the use of AI modes during transpilation."
   ]
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    "## Step 1: Map classical inputs to a quantum problem\n",
    "\n",
    "In this section, we will test the AI transpiler on the `EfficientSU2` circuit, a widely used hardware-efficient ansatz. This circuit is particularly relevant for variational quantum algorithms (e.g., VQE) and quantum machine learning tasks, making it an ideal test case for assessing transpilation performance.\n",
    "\n",
    "The `EfficientSU2` circuit consists of alternating layers of single-qubit rotations and entangling gates like CNOTs. These layers enable flexible exploration of the quantum state space while keeping the gate depth manageable, which is crucial for NISQ devices. By optimizing this circuit, we aim to reduce gate count, improve fidelity, and minimize noise. This makes it a strong candidate for testing the AI transpiler’s efficiency."
   ]
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",
      "text/plain": [
       "<Figure size 872.774x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For our transpilation, we will use a large circuit of 101 qubits\n",
    "qc = EfficientSU2(101, entanglement=\"circular\", reps=1).decompose()\n",
    "\n",
    "# Draw a smaller version of the circuit to get a visual representation\n",
    "qc_small = EfficientSU2(5, entanglement=\"circular\", reps=1).decompose()\n",
    "qc_small.draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b4f574a-eaa8-41e4-8f41-addb02e6cc86",
   "metadata": {},
   "source": [
    "## Step 2: Optimize problem for quantum hardware execution\n",
    "\n",
    "**Choose a backend**\n",
    "\n",
    "For this example, we will use the `ibm_brisbane` backend. This backend features a heavy hexagonal topology with 127 qubits, making it a suitable target for large-scale circuit demonstrations, especially when assessing performance on modern quantum hardware.\n",
    "\n",
    "**Create TranspilerService Instances**\n",
    "\n",
    "To evaluate the effectiveness of the AI transpiler, we will perform two transpilation runs. First, we will transpile the circuit using the AI transpiler. Then, we will run a comparison by transpiling the same circuit without the AI transpiler, using traditional methods. Both transpilation processes will use the following configuration:\n",
    "\n",
    "`backend_name=\"ibm_brisbane\"`\n",
    "\n",
    "`optimization_level=3`\n",
    "\n",
    "By comparing the results from these two runs, we will be able to assess how much optimization is gained by using the AI transpiler in terms of circuit depth, gate count, and runtime efficiency.\n",
    "\n",
    "Notes:\n",
    "- For best results, user can use `ai=auto` as it will run both the standard Qiskit heuristic passes and the AI-powered passes and return the best result. For our example, we will run the AI-powered passes explicitly to show the difference in results.\n",
    "\n",
    "- Similar to `generate_preset_passmanager`, the `generate_ai_passmanager` function can be used for a hybrid AI-powered transpilation, example usage shown below.\n",
    "\n",
    "```python\n",
    "    from qiskit.circuit.library import EfficientSU2\n",
    "    from qiskit_ibm_transpiler import generate_ai_pass_manager\n",
    "\n",
    "    su2_circuit = EfficientSU2(101, entanglement=\"circular\", reps=1).decompose()\n",
    "\n",
    "    ai_transpiler_pass_manager = generate_ai_pass_manager(\n",
    "        coupling_map=coupling_map,\n",
    "        ai_optimization_level=ai_optimization_level,\n",
    "        include_ai_synthesis=include_ai_synthesis,\n",
    "        optimization_level=optimization_level,\n",
    "        ai_layout_mode=ai_layout_mode,\n",
    "        qiskit_transpile_options=qiskit_transpile_options,\n",
    "    )\n",
    "    transpiled_circuit = ai_transpiler_pass_manager.run(su2_circuit)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6989a85-96e0-4453-a28b-a504ca69750d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ai_service = TranspilerService(\n",
    "    backend_name=\"ibm_brisbane\",\n",
    "    ai=\"true\",\n",
    "    optimization_level=3,\n",
    ")\n",
    "\n",
    "no_ai_service = TranspilerService(\n",
    "    backend_name=\"ibm_brisbane\",\n",
    "    ai=\"false\",\n",
    "    optimization_level=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32cfb7eb-6209-4ec6-b8bc-d00b6559a237",
   "metadata": {},
   "source": [
    "Transpile the circuits and record the times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f60cfd26-bbcc-4519-98a9-8d4123f3150e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/henryzou/.venvs/ibm-learning-sabre/lib/python3.11/site-packages/qiskit/qpy/interface.py:305: UserWarning: The qiskit version used to generate the provided QPY file, 1.2.4, is newer than the current qiskit version 1.2.0. This may result in an error if the QPY file uses instructions not present in this current qiskit version\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard transpilation: 43.817320108413696 seconds\n",
      "AI transpilation      : 21.043448209762573 seconds\n"
     ]
    }
   ],
   "source": [
    "time_start = time.time()\n",
    "qc_tr_no_ai = no_ai_service.run(qc)\n",
    "time_end = time.time()\n",
    "time_no_ai = time_end - time_start\n",
    "print(f\"Standard transpilation: {time_no_ai} seconds\")\n",
    "\n",
    "time_start = time.time()\n",
    "qc_tr_ai = ai_service.run(qc)\n",
    "time_end = time.time()\n",
    "time_ai = time_end - time_start\n",
    "print(f\"AI transpilation      : {time_ai} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d96b9e88-bfe1-4426-b9c1-1b00cbd784d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard transpilation: Depth 482, Gate count 4467, Time 43.817320108413696\n",
      "AI transpilation      : Depth 131, Gate count 2358, Time 21.043448209762573\n"
     ]
    }
   ],
   "source": [
    "depth_no_ai = qc_tr_no_ai.depth(lambda x: x.operation.num_qubits == 2)\n",
    "depth_ai = qc_tr_ai.depth(lambda x: x.operation.num_qubits == 2)\n",
    "\n",
    "gate_count_no_ai = qc_tr_no_ai.size()\n",
    "gate_count_ai = qc_tr_ai.size()\n",
    "\n",
    "print(\n",
    "    f\"Standard transpilation: Depth {depth_no_ai}, Gate count {gate_count_no_ai}, Time {time_no_ai}\"\n",
    ")\n",
    "print(\n",
    "    f\"AI transpilation      : Depth {depth_ai}, Gate count {gate_count_ai}, Time {time_ai}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b05b65-7583-4514-83bc-4d99ba4864db",
   "metadata": {},
   "source": [
    "In this test, we compare the performance of the AI transpiler and the standard transpilation method on the EfficientSU2 circuit using the `ibm_brisbane` backend. The results show a significant improvement in the circuit's depth, gate count, and transpilation time when using the AI transpiler:\n",
    "\n",
    "- **Circuit Depth**: This reduction in depth is substantial (over 50%), as it means the AI transpiler has found a more efficient arrangement of qubit interactions, which can directly impact the overall fidelity of the circuit on quantum hardware. A shallower circuit helps mitigate qubit decoherence and reduces the likelihood of noise affecting the outcome.\n",
    "\n",
    "- **Gate Count**: The AI transpiler also reduced the gate count by nearly 50%. Since each gate has an associated error rate, fewer gates directly lower the overall chance of error, which is critical for maintaining coherence and improving the reliability of results in practical quantum computing tasks.\n",
    "\n",
    "\n",
    "- **Transpilation Time Efficiency**: While the time varies for the device, the time it took to transpile the circuit was drastically reduced, over a 50% improvement. This shows that the AI-powered transpiler not only optimizes the resulting circuit but also improves the efficiency of the transpilation process itself, making it faster to generate optimized circuits.\n",
    "\n",
    "It is important to note that these results are based on just one circuit. To obtain a comprehensive understanding of how the AI transpiler compares to traditional methods, it is necessary to test a variety of circuits. The performance of QTS can vary greatly depending on the type of circuit being optimized. For a broader comparison, refer to the benchmarks above or visit the following IBM Blog Post[\\[1\\]](#references)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36556b22-258b-4c6f-b14a-33ef759f54ed",
   "metadata": {},
   "source": [
    "## Step 3: Execute using Qiskit primitives\n",
    "As this tutorial focuses on transpilation, no experiments will be executed on the quantum device. The goal is to leverage the optimizations from Step 2 to obtain a transpiled circuit with reduced depth and/or gate count."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86398ce2-beb4-4944-8961-57cadf03096a",
   "metadata": {},
   "source": [
    "## Step 4: Post-process and return result in desired classical format\n",
    "Since there is no execution for this notebook, there are no results to post-process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c546005f-1a73-4906-8804-f925846fe380",
   "metadata": {},
   "source": [
    "# Part II. Analyzing and benchmarking the transpiled circuits\n",
    "\n",
    "In this section, we will demonstrate how to analyze the transpiled circuit and benchmark it against the original version in more detail. We will focus on metrics such as circuit depth, gate count, and transpilation time to assess the effectiveness of the optimization. Additionally, we will discuss how the results may differ across various circuit types, offering insights into the broader performance of the transpiler across different scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11499cdb-b2f4-4ca3-8546-9c354f772088",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/henryzou/.venvs/ibm-learning-sabre/lib/python3.11/site-packages/qiskit/qpy/interface.py:305: UserWarning: The qiskit version used to generate the provided QPY file, 1.2.4, is newer than the current qiskit version 1.2.0. This may result in an error if the QPY file uses instructions not present in this current qiskit version\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed transpilation for Random\n",
      "Completed transpilation for Clifford\n",
      "Completed transpilation for QFT\n",
      "Completed transpilation for BV\n",
      "Completed transpilation for QV\n",
      "    Circuit  Depth (No AI)  Gate Count (No AI)  Time (No AI)  Depth (AI)  \\\n",
      "0    Random            345                7842      3.944334         298   \n",
      "1  Clifford             84                2284      2.354044          67   \n",
      "2       QFT            328                4847      3.657239         217   \n",
      "3        BV            135                1116      2.536958         103   \n",
      "4        QV            198                5075      2.670678         174   \n",
      "\n",
      "   Gate Count (AI)  Time (AI)  \n",
      "0             8738  34.042681  \n",
      "1             2173  18.066975  \n",
      "2             4602  25.296369  \n",
      "3              791  21.240081  \n",
      "4             5613  26.749141  \n"
     ]
    }
   ],
   "source": [
    "# Circuits to benchmark\n",
    "seed = 42\n",
    "circuits = [\n",
    "    {\n",
    "        \"name\": \"Random\",\n",
    "        \"qc\": random_circuit(num_qubits=30, depth=10, seed=seed),\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"Clifford\",\n",
    "        \"qc\": random_clifford_circuit(\n",
    "            num_qubits=40, num_gates=200, seed=seed\n",
    "        ),\n",
    "    },\n",
    "    {\"name\": \"QFT\", \"qc\": QFT(20, do_swaps=False).decompose()},\n",
    "    {\n",
    "        \"name\": \"BV\",\n",
    "        \"qc\": create_bv_circuit(40),\n",
    "    },  # Using the BV circuit function\n",
    "    {\"name\": \"QV\", \"qc\": QuantumVolume(num_qubits=20, depth=10, seed=seed)},\n",
    "]\n",
    "\n",
    "results = []\n",
    "\n",
    "# Run the transpilation for each circuit and store the results\n",
    "for circuit in circuits:\n",
    "    data = run_transpilation(circuit[\"qc\"], no_ai_service, ai_service)\n",
    "    print(\"Completed transpilation for\", circuit[\"name\"])\n",
    "    results.append(\n",
    "        {\n",
    "            \"Circuit\": circuit[\"name\"],\n",
    "            \"Depth (No AI)\": data[\"depth_no_ai\"],\n",
    "            \"Gate Count (No AI)\": data[\"gate_count_no_ai\"],\n",
    "            \"Time (No AI)\": data[\"time_no_ai\"],\n",
    "            \"Depth (AI)\": data[\"depth_ai\"],\n",
    "            \"Gate Count (AI)\": data[\"gate_count_ai\"],\n",
    "            \"Time (AI)\": data[\"time_ai\"],\n",
    "        }\n",
    "    )\n",
    "df = pd.DataFrame(results)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4aae7f9-3a73-4798-992a-55f2bea3dcc2",
   "metadata": {},
   "source": [
    "Average percentage reduction for each metric. Positive are improvements, negative are degradations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d3aad52-a63d-458e-b048-afd546f40a1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average reduction in depth: 21.19%\n",
      "Average reduction in gate count: -3.56%\n",
      "Average reduction in transpilation time: -726.97%\n"
     ]
    }
   ],
   "source": [
    "# Average reduction from non-AI to AI transpilation as a percentage\n",
    "avg_reduction_depth = (\n",
    "    (df[\"Depth (No AI)\"] - df[\"Depth (AI)\"]).mean()\n",
    "    / df[\"Depth (No AI)\"].mean()\n",
    "    * 100\n",
    ")\n",
    "avg_reduction_gates = (\n",
    "    (df[\"Gate Count (No AI)\"] - df[\"Gate Count (AI)\"]).mean()\n",
    "    / df[\"Gate Count (No AI)\"].mean()\n",
    "    * 100\n",
    ")\n",
    "avg_reduction_time = (\n",
    "    (df[\"Time (No AI)\"] - df[\"Time (AI)\"]).mean()\n",
    "    / df[\"Time (No AI)\"].mean()\n",
    "    * 100\n",
    ")\n",
    "\n",
    "print(f\"Average reduction in depth: {avg_reduction_depth:.2f}%\")\n",
    "print(f\"Average reduction in gate count: {avg_reduction_gates:.2f}%\")\n",
    "print(f\"Average reduction in transpilation time: {avg_reduction_time:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "541960d9-4c2a-4149-bd44-6077553c699b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2100x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(21, 6))\n",
    "df.plot(x=\"Circuit\", y=[\"Depth (No AI)\", \"Depth (AI)\"], kind=\"bar\", ax=axs[0])\n",
    "axs[0].set_title(\"Circuit Depth Comparison\")\n",
    "axs[0].set_ylabel(\"Depth\")\n",
    "axs[0].set_xlabel(\"Circuit\")\n",
    "axs[0].tick_params(axis=\"x\", rotation=45)\n",
    "df.plot(\n",
    "    x=\"Circuit\",\n",
    "    y=[\"Gate Count (No AI)\", \"Gate Count (AI)\"],\n",
    "    kind=\"bar\",\n",
    "    ax=axs[1],\n",
    ")\n",
    "axs[1].set_title(\"Gate Count Comparison\")\n",
    "axs[1].set_ylabel(\"Gate Count\")\n",
    "axs[1].set_xlabel(\"Circuit\")\n",
    "axs[1].tick_params(axis=\"x\", rotation=45)\n",
    "df.plot(x=\"Circuit\", y=[\"Time (No AI)\", \"Time (AI)\"], kind=\"bar\", ax=axs[2])\n",
    "axs[2].set_title(\"Time Comparison\")\n",
    "axs[2].set_ylabel(\"Time (seconds)\")\n",
    "axs[2].set_xlabel(\"Circuit\")\n",
    "axs[2].tick_params(axis=\"x\", rotation=45)\n",
    "fig.suptitle(\n",
    "    \"Benchmarking AI transpilation vs Non-AI transpilation for various circuits\"\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee11b216-4966-467f-9fa3-1eeada3703a1",
   "metadata": {},
   "source": [
    "The AI transpiler's performance varies significantly based on the type of circuit being optimized. In some cases, it achieves notable reductions in circuit depth and gate count compared to the standard transpiler. However, these improvements often come with a substantial increase in runtime.\n",
    "\n",
    "For certain types of circuits, the AI transpiler may yield slightly better results in terms of circuit depth but may also lead to an increase in gate count and a significant runtime penalty. These observations suggest that the AI transpiler's benefits are not uniform across all circuit types. Instead, its effectiveness depends on the specific characteristics of the circuit, making it more suitable for some use cases than others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac7b9f26-97e6-443a-b7c6-e23f6126303f",
   "metadata": {},
   "source": [
    "## When should users choose AI-powered transpilation?\n",
    "\n",
    "The AI-powered transpiler in Qiskit excels in scenarios where traditional transpilation methods struggle—particularly with large-scale and complex quantum circuits. For circuits involving hundreds of qubits or those targeting hardware with intricate coupling maps, the AI transpiler offers superior optimization in terms of circuit depth, gate count, and runtime efficiency. In benchmarking tests, it has consistently outperformed traditional methods, delivering significantly shallower circuits and reducing gate counts, which are critical for enhancing performance and mitigating noise on real quantum hardware.\n",
    "\n",
    "Users should consider AI-powered transpilation when working with:\n",
    "- Large circuits where traditional methods fail to efficiently handle the scale.\n",
    "- Complex hardware topologies where device connectivity and routing challenges arise.\n",
    "- Performance-sensitive applications where reducing circuit depth and improving fidelity are paramount.\n",
    "\n",
    "#### Getting the Best Results: Using `ai=\"auto\"`\n",
    "For optimal results, users can set ai=\"auto\" in the Qiskit Transpiler Service. This automatically selects the best AI-based optimizations based on the circuit and target hardware, ensuring that the transpiler delivers the highest performance without manual configuration. This mode is ideal for users who want the convenience of automatic optimization while still benefiting from the powerful AI-driven enhancements to circuit routing and synthesis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02c6498-4f7b-43df-9008-4602089d1fec",
   "metadata": {},
   "source": [
    "# Part III. Exploring AI-powered permutation network synthesis\n",
    "\n",
    "Permutation networks are foundational in quantum computing, particularly for systems constrained by restricted topologies. These networks facilitate long-range interactions by dynamically swapping qubits to mimic all-to-all connectivity on hardware with limited connectivity. Such transformations are essential for implementing complex quantum algorithms on near-term devices, where interactions often span beyond nearest neighbors.\n",
    "\n",
    "In this section, we highlight the synthesis of permutation networks as a compelling use case for the AI-powered transpiler in Qiskit. Specifically, the `AIPermutationSynthesis` pass leverages AI-driven optimization to generate efficient circuits for qubit permutation tasks. By contrast, generic synthesis approaches often struggle to balance gate count and circuit depth, especially in scenarios with dense qubit interactions or when attempting to achieve full connectivity.\n",
    "\n",
    "We will walk through a Qiskit patterns example showcasing the synthesis of a permutation network to achieve all-to-all connectivity for a set of qubits. We will compare the performance of `AIPermutationSynthesis` against the standard synthesis methods in Qiskit. This example will demonstrate how the AI transpiler optimizes for lower circuit depth and gate count, highlighting its advantages in practical quantum workflows.\n",
    "\n",
    "For more details about the AI-powered permutation synthesis in QTS, please refer to the [Qiskit API documentation](https://docs.quantum.ibm.com/api/qiskit-ibm-transpiler/qiskit_ibm_transpiler.ai.AIPermutationSynthesis)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ef1d50-3221-44b3-bbc9-221f8f473d0b",
   "metadata": {},
   "source": [
    "## Step 1: Map classical inputs to a quantum problem\n",
    "\n",
    "To represent a classical permutation problem on a quantum computer, we start by defining the structure of the quantum circuits. For this example:\n",
    "\n",
    "1. **Quantum Circuit Initialization**:\n",
    "   We allocate 27 qubits to match the backend we will use, which has 27 qubits.\n",
    "\n",
    "2. **Applying Permutations**:\n",
    "   We generate five random permutation patterns (`pattern_1` through `pattern_5`) using a fixed seed (42) for reproducibility. Each permutation pattern is applied to a separate quantum circuit (`qc_1` through `qc_5`).\n",
    "\n",
    "3. **Circuit Decomposition**:\n",
    "   Each permutation operation is decomposed into native gate sets compatible with the target quantum hardware. We analyze the depth and the number of two-qubit gates (nonlocal gates) for each decomposed circuit.\n",
    "\n",
    "The results provide insight into the complexity of representing classical permutation problems on a quantum device, demonstrating the resource requirements for different permutation patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d0259c1-9de4-4371-a9d7-0ace6fd7391d",
   "metadata": {},
   "outputs": [
    {
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aO3euBg4cqMJC1y6HioptSbnwTCFOjb3L9TNew3aGYejNz2x72aGgqEyzTJqpAbbZfyRXOSbOfHux2YoAZysuKdeve0+ZFp9jAJ7izc922LR/cYnVtFndAVuZORbzOwCYb+dv2SoprXxVBWdjHADgCZJ2nNQPNr5osmhlmo5kVK3YGgDMZOb52KH0fJ08xfNSAAAAAID78DM7AVdZvXq1JGnAgAGV7nPkyNllhH9fPP3ll1+qfv365/5///79Vb9+fY0ePVobNmxQv379JEmpqal69NFH9cYbb+j+++8/t/+1117r0M8B+yWnmFfAvP23UyopLVeAv69pOeDifvzlhF2FhTMX7dbj93aWr6/XvI/ikcwcA/4Tf/hVLUzNAd5tV2q2SsvMK5Yx+xgEquKXPVnauO2Eze0++mKPnh7XVYEBnOvBvZk5FienZMkwDFksFtNyALzd1t3mvUgnnX2x/Kre0abmAAAXM2NBis1tyssNffj5Hk2d0M0JGQGA42zdbf498qsv43wQAAAAAOAeLIZhGGYn4QoxMTE6cuSItm7dqi5duvxhe1lZmRo1aqTMzEylpqaqZcuWlfa1d+9etWvXTnPmzNGoUaMkSU899ZTeeustZWVlKTAw0CE5d+/eXRkZGQ7pC9KxOo/L8AmucFvS3GGKigyptG1UZLD8fH1UVm5VRmblb8ZnZBaox6hlFfeR/bJ8jQLbkoZL5QVeotOh9r3wEJX9qnyNPAdnBEfKC+yh06HXVbjtYmOAVP1xIKRos+oWfGlb0oADFfm1UlatOyvc5opjIKB0v+rnfmpb0oCL5Qd0U07Y9Xa1bZjzlvys5halARdiSDpW92nJUvELf664Jmp86llZVG5T3gAcJzeoj86EDKpwmyvOB8MKN6p24be2JQ0ALna81jiV+UXZ3C6wZK8i8/7thIwAwHFOhd6iwsCOFW5z1DWhVPn5YN28RQop2W5b0gAAAAAAXEBUVJQ2b95sV1uvmXk6P//ssnmFhRVf0M+fP1+ZmZkKDw9XixYXnhl0zZo1kqTY2Nhzf7dx40a1a9dOn332mZ5//nkdPnxYbdq00ZQpU84VWNsqIyNDR48etastKlCn8lmBoyJDFN0w9KJd+Pn6VGm/imRknJDKTtvVFi5Sv1Cy759XGSeypRLbZ6qEC0W0r/Tft6pjgGT/OFBQWKwCxnSYKbyeVKviTa44BkpKyjmvgfuLaCeF2df0+MlsqYjvONyZRapn7jXRsfTjkrXYrrYAHKB+nlRJPYwrzgfz8guVl85vJQA3F+pj11OT4hKDa14A7q9piVTJ/E+uuCbMzslVdjZjJQAAAADAPXhN8XRUVJSys7OVnJysXr16nbctPT1djz76qCSpc+fOF1xG+OjRo5o8ebIGDx583gzW6enpOnr0qJ544gm9/PLLiomJ0axZs3Tbbbepfv36GjhwoF05w3GOqVyVTbOekXnhGaFtmV2p0j6i6svXsLMaBy6RFxgoe8vboxrUlq/h79B84Fj5gWHKqWTbxcYAqfrjQEhwgOo2aVKFTAHnKPKrrcoW5nTFMRAY4KNIjgG4uYKAIGXb2sgwJItFDevXkp+V7zjc21GjXLL4VrjNFddEjRs1kEXWqiULwOFyg0J1ppJtrjgfDAsNUm3OBwG4uRO+ZSq1o11QgFURjHEA3NypIH9VdhbnqGvCC/VVt06YQkIYKwEAAAAAjlOdGluLYRiV1ZPWKBMnTtQ777yjmJgYrVq1Sm3btpUkJSUl6Y477tD+/ftVWlqqCRMmaPr06RX2kZeXp8svv1wZGRlKSkpSo0aNzm1r27at9u3bp8WLF+uGG26QJBmGoS5duqhOnTr6/vvvnf4ZcWFdRyzWtt32LaV+eOWtim4YqiPH8xUzaJ7N7cNC/HV64x3y8am8MB/m23/kjFoPWShbR8UeHSP185zrnZMUHCZx/WFdO2GF3e2rOw688ECCnhzTxe74QHXtO3habYcusrt9dY+Bu29oo4+f7Wd3fMAVjp3IV7Or56us3LaTgbhWdbTji5su+BIm4A5aD1mg1MO5drWt7u9Ao/ohOvadfasyAXCM+d/s162T1tjdvrrjwDtP9NL9o+Lsjg8ArjB5+hY9/8E2m9vNnHKZxg5v7/iEAMCBHnvjZ70ye7tdbat7LihJP/zzOvXu0tCutgAAAAAAOFrla/bWMJMmTVJERIQOHz6sDh06qFOnTmrTpo169uypli1b6oorrpAkxcfHV9i+sLBQQ4cO1YEDB7RixYrzCqclqV69epJ03gzTFotFAwcO1I4dO5z0qWCLhNhI02J3i42gcNoDtIyupWv6RNvcbvzIWCdkA0dLiDNvDHCH+ECrmFqqFWbeDPkcA/AEjRuE6sYrm9vcbvzIWAqn4RHMHIsTYiNMiw3gLLPPxxLiGAcAuL+xw9vZfB+3Vpi/bru2lZMyAgDHMfN80MfHoi7tOB8EAAAAALgPrymejo6O1vr16zVkyBAFBQUpLS1N9erV08yZM7V8+XLt3btXUsXF06WlpRo+fLg2b96sxMRExcX9cZacDh06VBq7qKjIcR8EdjO1UIAHhB7j8Xvi5etb9QckrZvW0sirWzoxIzhKg4hgRTcMNS2+2YUKgI+PRd1MfJHIzJeYAFtMuruTAvyrfpnUtFGo7riutRMzAhzHzLGYcyHAfK1iwlU7PMCU2D4+FsW35d4IAPcXExWmu69vY1Obh27vqLAQ815WBoCqMvO6LLZFbYUE+5kWHwAAAACA/+U1xdOSFBsbq6+++kq5ubnKzc3VTz/9pLFjxyo/P19paWny8fFRx44dz2tjtVo1evRofffdd1q6dKl69uxZYd/XX3+9JGnFihXntV25cqV69OjhvA+FKru2b7TMmhDwun5NzQkMm/VNiNKsZ/pWaYaZJg1ClPje1QoO4oafpxjaP8aUuJd0qq/IukGmxAZ+z6xjICoymKI5eIzuHerrs5cul18VXqZqUC9Iie9drVph5hSiAba6zqTfAbNjAzjLYrHoun7mHItXXtKIYhkAHuPdp3rrqt5NqrTvHde11pT7ujo5IwBwjJbR4YptWceU2Nf15zkZAAAAAMC9eFXxdGV27twpwzDUpk0bhYSEnLdtwoQJWrhwoR566CGFhIRo06ZN5/6cPHny3H5Dhw5V3759NXbsWH344Yf69ttvNXLkSO3cuVNTpkxx9UdCBZo1DjeliLl9i9oa0LORy+PCfn+6vo2+fGeQOrSqU+F2Hx+Lrh/QVJs+G6bWTWu5NjlUy7gRsabEnXCrOXGB/3XX9W0VFOjr8rhjb24vfxtm8gXMdstVLfTNjMHq0r5ehdstFumaPtHa9NkwxbWq6+LsAPvFtaqr/t2jXB63R8dIde9Q3+VxAfzR+JFmXRP9cRU3AHBXgQG++vKdQXrkT50UHlrxjNKRdYP04sTu+uT5flWahAEA3IHFYjHlfNBike67pb3L4wIAAAAAcCFM+SJp+/btkqT4+Pg/bEtMTJQkTZs2TdOmTTtv2+zZs3XXXXdJOnvDYdmyZXrsscf05JNP6syZM4qPj9fXX3+tK664wrkfAFU2fmSsvvz+kEtjjhsRK4tZU17Dbtf2jdE1faK1fkuG5n97QCdOFcrfz0dtm9XWPTe2VdNGYWanCDt0altPfbo21Iatx10WM6JOoG65qoXL4gEXUq92oEZd01Kzl+xzWUxfX4vG3NzOZfEAR7ny0sZKnn+DNv16QnO+3q+MzAL5+/moZXS47rmxrVpG8wIVPNP4kbH6fnOGS2Oa9QIbgD/qFd9A8e3q6Zc9p1wWMyYqVEP6Mvs8AM8S4O+rVx/uqafHddW/l6dq47bjyisoU60wfw28tLGGD2qhwADXv5wMANV1x3Wt9fibScovLHNZzGv6RKt5k3CXxQMAAAAAoCoontaFi6fT0tKq3E+dOnU0c+ZMzZw501GpwcGuvqyJrurdRCs2HnVJvPYtamvscArGPJXFYlG/7o3Urzszh9ckr/6tpy7701eyWg2XxHtpYncFBfJzC/fxzLhuWrQyTbn5pS6J9/CdHRUdFeqSWICjWSwW9YpvqF7xDc1OBXCYmwc2V+8uDbRx2wmXxOsWG6Hbh7R2SSwAF2exWPT6w5do4NhEl8V89W895efHKiQAPFNYiL/+ckt7/YUZUwHUELXDA/TMuG569B8/uySev5+Ppj3YwyWxAAAAAACwBU8udOHiadQsFotFHz7dp9LlFh3Jx8ei2c/2o2gScDOXxjfQw3d2dEmsQb0a68/MuAs307RRmF5/uKdLYrVvUVtTx3dzSSwAQNX4+vro46l9FRTo/JkS/f189Mlz/eTvz60HwJ1ceWljly2bfvPA5hpxNSvxAAAAuJOH7uigSzvXd0msp+/rqk5t67kkFgAAAAAAtuAJpqTVq1fLMAwNGTLE7FTgAk0bhWn6E71sapORWaAjx/OVkVlQ5TZP/jlel8Y3sDU9AC7w7IRu6tLethu2to4DkXWD9NEzfWWxWOxJEXCqP9/cTkP7N7Wpja3HQFCgrz59vj8vEQGAG2rXoo5es/FFGnuuiV54IIGH5ICbeuVvPdS2WW2b2tg6DjRuEKJ3n+rNNREAAICb8fU9+6JrrbCqTzRkzzVh7y4N9Ng9ne1JEQAAAAAAp7MYhmGYnQRghlc+/lWPvZnklL7H3NxOM6dcxgNCwI0dzypUv7uWa+/B0w7vu3Z4gFZ/eI26xUU6vG/AUQoKyzR43Ddan3zc4X37+/noizeu1HU2FmgDAFxr8vQtev6DbU7p+293dtRrD/fkmghwY4fS89Tv7uU6eCzP4X3Xrxuk72cPUWzLOg7vGwAAAI6xITlDg8d9q/zCMof33bltPa39+FrVrRXo8L4BAAAAAHAEZp6G15p0T2e99dil8vFx7MP8v97eQe9PpnAacHcNI4K17pMh6to+wqH9RkUG6/uPr6VwGm4vJNhPie9drWv6RDu039BgP335ziAKpwHAAzw7oZteeCD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      "text/plain": [
       "<Figure size 3733.98x2290.94 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parameters\n",
    "width = 27\n",
    "num_circuits = 5\n",
    "seed = 42\n",
    "\n",
    "# Set random seed\n",
    "np.random.seed(seed)\n",
    "\n",
    "\n",
    "# Function to generate permutation circuit\n",
    "def generate_permutation_circuit(width, pattern):\n",
    "    circuit = QuantumCircuit(width)\n",
    "    circuit.append(\n",
    "        Permutation(num_qubits=width, pattern=pattern),\n",
    "        qargs=range(width),\n",
    "    )\n",
    "    return circuit\n",
    "\n",
    "\n",
    "# Generate random patterns and circuits\n",
    "patterns = [\n",
    "    np.random.permutation(width).tolist() for _ in range(num_circuits)\n",
    "]\n",
    "circuits = {\n",
    "    f\"qc_{i}\": generate_permutation_circuit(width, pattern)\n",
    "    for i, pattern in enumerate(patterns, start=1)\n",
    "}\n",
    "\n",
    "# Display one of the circuits\n",
    "circuits[\"qc_1\"].decompose(reps=3).draw(output=\"mpl\", fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de10fba-9321-4d74-ab54-644bc3204a1c",
   "metadata": {},
   "source": [
    "## Step 2: Optimize problem for quantum hardware execution\n",
    "In this step, we proceed with optimization using the AI synthesis passes.\n",
    "\n",
    "For the AI synthesis passes, the `PassManager` requires only the coupling map of the backend. However, it is important to note that not all coupling maps are compatible; only those that the `AIPermutationSynthesis` pass has been trained on will work. Currently, the `AIPermutationSynthesis` pass supports blocks of sizes 65, 33, and 27 qubits. Thus we will select `ibm_cario` as the backend for this example, as it has 27 qubits.\n",
    "\n",
    "For comparison, we will evaluate the performance of AI synthesis against generic permutation synthesis methods in Qiskit, including:\n",
    "\n",
    "- **`synth_permutation_depth_lnn_kms`:** This method synthesizes a permutation circuit for a linear nearest-neighbor (LNN) architecture using the Kutin, Moulton, and Smithline (KMS) algorithm. It guarantees a circuit with a depth of at most $ n $ and a size of at most $ n(n-1)/2 $, where both depth and size are measured in terms of SWAP gates.\n",
    "\n",
    "- **`synth_permutation_acg`:** This method synthesizes a permutation circuit for a fully-connected architecture using the Alon, Chung, and Graham (ACG) algorithm. It produces a circuit with a depth of exactly 2 (in terms of the number of SWAP gates).\n",
    "\n",
    "- **`synth_permutation_basic`:** This is a straightforward implementation that synthesizes permutation circuits without imposing constraints on connectivity or optimization for specific architectures. It serves as a baseline for comparing performance with more advanced methods.\n",
    "\n",
    "Each of these methods represents a distinct approach to synthesizing permutation networks, providing a comprehensive benchmark against the AI-powered methods.\n",
    "\n",
    "For more details about synthesis methods in Qiskit, refer to the [Qiskit API documentation](https://docs.quantum.ibm.com/api/qiskit/synthesis)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240196a2-0382-4f67-9dc7-9184d69c134f",
   "metadata": {},
   "source": [
    "Define the coupling map representing `ibm_cario`, a 27-qubit device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "172ad2e3-5703-44d2-a2a1-567f64630c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=741x1063>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coupling_map = [\n",
    "    [1, 0],\n",
    "    [2, 1],\n",
    "    [3, 2],\n",
    "    [3, 5],\n",
    "    [4, 1],\n",
    "    [6, 7],\n",
    "    [7, 4],\n",
    "    [7, 10],\n",
    "    [8, 5],\n",
    "    [8, 9],\n",
    "    [8, 11],\n",
    "    [11, 14],\n",
    "    [12, 10],\n",
    "    [12, 13],\n",
    "    [12, 15],\n",
    "    [13, 14],\n",
    "    [16, 14],\n",
    "    [17, 18],\n",
    "    [18, 15],\n",
    "    [18, 21],\n",
    "    [19, 16],\n",
    "    [19, 22],\n",
    "    [20, 19],\n",
    "    [21, 23],\n",
    "    [23, 24],\n",
    "    [25, 22],\n",
    "    [25, 24],\n",
    "    [26, 25],\n",
    "]\n",
    "CouplingMap(coupling_map).draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa6db33d-8407-4bd2-a6fc-a9a9aea8cfeb",
   "metadata": {},
   "source": [
    "Create the pass managers and transpile each of the permutation circuits using the AI synthesis passes and generic synthesis methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3112a62-1ce0-4586-8e89-423b34974838",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "\n",
    "# Transpile and analyze all circuits\n",
    "for i, (qc_name, circuit) in enumerate(circuits.items(), start=1):\n",
    "    pattern = patterns[i - 1]\n",
    "    analyze_transpilation(results, circuit, pattern, qc_name, coupling_map)\n",
    "results_df = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e67e64b1-54ac-4df6-932d-f8dec72290ff",
   "metadata": {},
   "source": [
    "Record the metrics (depth, gate count, time) for each circuit after transpilation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd90b81b-970d-4f5b-9d1a-f0760589ab6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Average Metrics ===\n",
      "               Depth  Gates(2q)  Time (s)\n",
      "Method                                   \n",
      "ACG             24.0      120.0      0.01\n",
      "AI              21.0       71.4      0.00\n",
      "Basic           33.6      108.0      0.00\n",
      "Depth-LNN-KMS  138.0      623.4      0.02\n",
      "\n",
      "Best Non-AI Method (based on least average depth): ACG\n",
      "\n",
      "=== Comparison of AI vs Best Non-AI Method ===\n",
      "      Metric    AI     ACG  Improvement (AI vs Best Non-AI)\n",
      "0      Depth  21.0   24.00                            -3.00\n",
      "1  Gates(2q)  71.4  120.00                           -48.60\n",
      "2   Time (s)   0.0    0.01                            -0.01\n"
     ]
    }
   ],
   "source": [
    "# Calculate averages for each metric\n",
    "average_metrics = results_df.groupby(\"Method\")[\n",
    "    [\"Depth\", \"Gates(2q)\", \"Time (s)\"]\n",
    "].mean()\n",
    "average_metrics = average_metrics.round(2)  # Round to 2 decimal places\n",
    "print(\"\\n=== Average Metrics ===\")\n",
    "print(average_metrics)\n",
    "\n",
    "# Identify the best non-AI method based on least average depth\n",
    "non_ai_methods = [\n",
    "    method for method in results_df[\"Method\"].unique() if method != \"AI\"\n",
    "]\n",
    "best_non_ai_method = average_metrics.loc[non_ai_methods][\"Depth\"].idxmin()\n",
    "print(\n",
    "    f\"\\nBest Non-AI Method (based on least average depth): {best_non_ai_method}\"\n",
    ")\n",
    "\n",
    "# Compare AI to the best non-AI method\n",
    "ai_metrics = average_metrics.loc[\"AI\"]\n",
    "best_non_ai_metrics = average_metrics.loc[best_non_ai_method]\n",
    "\n",
    "comparison = {\n",
    "    \"Metric\": [\"Depth\", \"Gates(2q)\", \"Time (s)\"],\n",
    "    \"AI\": [\n",
    "        round(ai_metrics[\"Depth\"], 2),\n",
    "        round(ai_metrics[\"Gates(2q)\"], 2),\n",
    "        round(ai_metrics[\"Time (s)\"], 2),\n",
    "    ],\n",
    "    best_non_ai_method: [\n",
    "        round(best_non_ai_metrics[\"Depth\"], 2),\n",
    "        round(best_non_ai_metrics[\"Gates(2q)\"], 2),\n",
    "        round(best_non_ai_metrics[\"Time (s)\"], 2),\n",
    "    ],\n",
    "    \"Improvement (AI vs Best Non-AI)\": [\n",
    "        round(ai_metrics[\"Depth\"] - best_non_ai_metrics[\"Depth\"], 2),\n",
    "        round(ai_metrics[\"Gates(2q)\"] - best_non_ai_metrics[\"Gates(2q)\"], 2),\n",
    "        round(ai_metrics[\"Time (s)\"] - best_non_ai_metrics[\"Time (s)\"], 2),\n",
    "    ],\n",
    "}\n",
    "\n",
    "comparison_df = pd.DataFrame(comparison)\n",
    "print(\"\\n=== Comparison of AI vs Best Non-AI Method ===\")\n",
    "print(comparison_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1464ba6b-ce1b-412e-b179-6ad690356053",
   "metadata": {},
   "source": [
    "The results demonstrate that the AI transpiler outperforms all other Qiskit synthesis methods for this set of random permutation circuits. Key findings include:\n",
    "\n",
    "1. **Depth**: The AI transpiler achieves the lowest average depth, indicating superior optimization of circuit layouts.\n",
    "2. **Gate Count**: It significantly reduces the number of two-qubit gates compared to other methods, improving execution fidelity and efficiency.\n",
    "3. **Transpilation Time**: All methods, including the AI transpiler, run very quickly at this scale, making them practical for use. However, the AI transpiler provides additional optimization benefits without added runtime cost.\n",
    "\n",
    "These results establish the AI transpiler as the most effective approach for this benchmark, particularly for depth and gate count optimization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "059bf36f-33cb-4438-a73f-07f9a724cd52",
   "metadata": {},
   "source": [
    "Plot the results to compare the performance of the AI synthesis passes against the generic synthesis methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "37e6bd25-abb9-474a-9366-0af31c44c0dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = results_df[\"Method\"].unique()\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "# Pivot the DataFrame and reorder columns to ensure AI is first\n",
    "pivot_depth = results_df.pivot(\n",
    "    index=\"Pattern\", columns=\"Method\", values=\"Depth\"\n",
    ")[[\"AI\", \"ACG\", \"Depth-LNN-KMS\", \"Basic\"]]\n",
    "pivot_gates = results_df.pivot(\n",
    "    index=\"Pattern\", columns=\"Method\", values=\"Gates(2q)\"\n",
    ")[[\"AI\", \"ACG\", \"Depth-LNN-KMS\", \"Basic\"]]\n",
    "pivot_time = results_df.pivot(\n",
    "    index=\"Pattern\", columns=\"Method\", values=\"Time (s)\"\n",
    ")[[\"AI\", \"ACG\", \"Depth-LNN-KMS\", \"Basic\"]]\n",
    "\n",
    "pivot_depth.plot(kind=\"bar\", ax=axs[0], legend=False)\n",
    "axs[0].set_title(\"Circuit Depth Comparison\")\n",
    "axs[0].set_ylabel(\"Depth\")\n",
    "axs[0].set_xlabel(\"Pattern\")\n",
    "axs[0].tick_params(axis=\"x\", rotation=45)\n",
    "pivot_gates.plot(kind=\"bar\", ax=axs[1], legend=False)\n",
    "axs[1].set_title(\"2Q Gate Count Comparison\")\n",
    "axs[1].set_ylabel(\"Number of 2Q Gates\")\n",
    "axs[1].set_xlabel(\"Pattern\")\n",
    "axs[1].tick_params(axis=\"x\", rotation=45)\n",
    "pivot_time.plot(\n",
    "    kind=\"bar\", ax=axs[2], legend=True, title=\"Legend\"\n",
    ")  # Show legend on the last plot\n",
    "axs[2].set_title(\"Time Comparison\")\n",
    "axs[2].set_ylabel(\"Time (seconds)\")\n",
    "axs[2].set_xlabel(\"Pattern\")\n",
    "axs[2].tick_params(axis=\"x\", rotation=45)\n",
    "fig.suptitle(\n",
    "    \"Benchmarking AI Synthesis Methods vs Non-AI Synthesis Methods For Random Permutations Circuits\",\n",
    "    fontsize=16,\n",
    "    y=1,\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f34546-0b99-4a8f-8294-e6cdc327e2f5",
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    "This graph highlights the individual results for each circuit (`qc_1` to `qc_5`) across different synthesis methods:\n",
    "\n",
    "1. **Circuit Depth**:\n",
    "   The AI transpiler often achieves **comparable or better depth optimizations** than other methods, highlighting its ability to optimize depth efficiently for these permutation circuits.\n",
    "\n",
    "2. **2Q Gate Count**:\n",
    "   The AI transpiler also shows a substantial reduction in the number of two-qubit gates compared to other methods, indicating more efficient resource usage and improved circuit quality.\n",
    "\n",
    "3. **Transpilation Time**:\n",
    "   All methods, including the AI transpiler, run quickly at this scale. However, the AI method achieves its optimizations without introducing additional runtime overhead, making it highly practical.\n",
    "\n",
    "While these results underscore the AI transpiler’s effectiveness for permutation circuits, it is important to note its **limitations**. The AI synthesis method is currently only available for certain coupling maps, which may restrict its broader applicability. This constraint should be considered when evaluating its usage in different scenarios.\n",
    "\n",
    "Overall, the AI transpiler **demonstrates promising improvements** in depth and gate count optimization for these specific circuits while maintaining comparable transpilation times."
   ]
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    "## Step 3: Execute using Qiskit primitives\n",
    "As this tutorial focuses on transpilation, no experiments will be executed on the quantum device. The goal is to leverage the optimizations from Step 2 to obtain a transpiled circuit with reduced depth and/or gate count."
   ]
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    "## Step 4: Post-process and return result in desired classical format\n",
    "Since there is no execution for this notebook, there are no results to post-process."
   ]
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    "## Limitations of QTS\n",
    "\n",
    "While the **Qiskit AI-powered Transpiler Service (QTS)** can offer significant performance benefits for large and complex quantum circuits, there are several service-level constraints and AI pass–specific considerations to be aware of.\n",
    "\n",
    "- **Maximum 2Q Gates per Circuit**\n",
    "  Each circuit can include up to **1 million two-qubit gates** in any AI mode.\n",
    "- **Transpilation Run Time**\n",
    "  Each transpilation process can run for up to **30 minutes**. If it exceeds this limit, the job will be canceled.\n",
    "- **Retrieving Results**\n",
    "  You must retrieve the transpilation result within **20 minutes** after the process finishes. After that, the result is discarded.\n",
    "- **Job Queue Time**\n",
    "  A set of circuits can remain in the internal queue for up to **120 minutes** while waiting to be transpiled. If the process doesn’t start within this window, the job is canceled.\n",
    "- **Qubit Limit**\n",
    "  Currently, there is no strictly defined maximum number of qubits. However, the service has been tested successfully with circuits of **900+ qubits**.\n",
    "\n",
    "While QTS often delivers significant improvements in gate count and circuit depth, overall transpilation performance can vary based on circuit size, connectivity, and runtime constraints. Larger or more complex circuits may still incur additional transpilation time or require specialized configurations.\n",
    "\n",
    "For the latest and most detailed information about QTS constraints, please refer to the [QTS Limitations documentation](https://docs.quantum.ibm.com/guides/qiskit-transpiler-service#limits-of-the-qiskit-transpiler-service)."
   ]
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    "## References\n",
    "\n",
    "[1] IBM Quantum. \"Qiskit Performance and Scaling: A Closer Look.\" IBM Quantum Blog, https://www.ibm.com/quantum/blog/qiskit-performance."
   ]
  },
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   "source": [
    "## Tutorial survey\n",
    "\n",
    "Please take one minute to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n",
    "\n",
    "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_0igXMtMCQfApgDI)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "© IBM Corp. 2025"
   ]
  }
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