qiskit-documentation/docs/tutorials/transpilation-optimizations-with-sabre.ipynb

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    "{/* cspell:ignore edgecolor */}\n",
    "\n",
    "# Transpilation Optimizations with SABRE\n",
    "*Usage estimate: under 1 minute on IBM Sherbrooke (NOTE: This is an estimate only. Your runtime may vary.)*"
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    "## Background\n",
    "Transpilation is a critical step in Qiskit that converts quantum circuits into forms compatible with specific quantum hardware. It involves two key stages: **qubit layout** (mapping logical qubits to physical qubits on the device) and **gate routing** (ensuring multi-qubit gates respect device connectivity by inserting SWAP gates as needed).\n",
    "\n",
    "SABRE (*SWAP-Based Bidirectional heuristic search algorithm*) is a powerful optimization tool for both layout and routing. It is especially effective for **large-scale circuits** (100+ qubits) and devices with complex coupling maps, like the **IBM Heron**, where the exponential growth in possible qubit mappings demands efficient solutions.\n",
    "\n",
    "### Why use SABRE?\n",
    "\n",
    "SABRE minimizes the number of SWAP gates and reduces circuit depth, improving circuit performance on real hardware. Its heuristic-based approach makes it ideal for advanced hardware and large, complex circuits. Recent improvements introduced in the [LightSABRE](https://arxiv.org/abs/2409.08368) algorithm further optimize SABRE’s performance, offering faster runtimes and fewer SWAP gates. These enhancements make it even more effective for large-scale circuits.\n",
    "\n",
    "### What you’ll learn\n",
    "\n",
    "This tutorial is divided into two parts:\n",
    "1. Learn to use SABRE with **Qiskit Patterns** for advanced optimization of large circuits.\n",
    "2. Leverage **qiskit_serverless** to maximize SABRE’s potential for scalable and efficient transpilation.\n",
    "\n",
    "You will:\n",
    "- Optimize SABRE for circuits with 100+ qubits, surpassing default transpilation settings like `optimization_level=3`.\n",
    "- Explore **LightSABRE enhancements** that improve runtime and reduce gate counts.\n",
    "- Customize key SABRE parameters (`swap_trials`, `layout_trials`, `max_iterations`, `heuristic`) to balance **circuit quality** and **transpilation runtime**."
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    "## Requirements\n",
    "\n",
    "Before starting this tutorial, be sure you have the following installed:\n",
    "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n",
    "- Qiskit Runtime 0.28 or later (`pip install qiskit-ibm-runtime`)\n",
    "- Rustworkx graph library (`pip install rustworkx`)\n",
    "- Serverless (`pip install qiskit-ibm-catalog qiskit_serverless`)"
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   "source": [
    "## Setup"
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   "source": [
    "from qiskit import QuantumCircuit\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "from qiskit_ibm_catalog import QiskitServerless, QiskitFunction\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "from qiskit_ibm_runtime import EstimatorOptions\n",
    "from qiskit_ibm_runtime import EstimatorV2 as Estimator\n",
    "from qiskit.transpiler import CouplingMap\n",
    "from qiskit.transpiler.passes import SabreLayout, SabreSwap\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time"
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    "## Part I. Using SABRE with Qiskit Patterns\n",
    "\n",
    "SABRE can be used in Qiskit to optimize quantum circuits by handling both the qubit layout and gate routing stages. In this section, we’ll guide you through the **minimal example** of using SABRE with Qiskit patterns, with the primary focus on step 2 of optimization.\n",
    "\n",
    "To run SABRE, you need:\n",
    "- A **DAG** (Directed Acyclic Graph) representation of your quantum circuit.\n",
    "- The **coupling map** from the backend, which specifies how qubits are physically connected.\n",
    "- The **SABRE pass**, which applies the algorithm to optimize the layout and routing.\n",
    "\n",
    "For this part, we’ll focus on the **SabreLayout** pass. It performs both layout and routing trials, working to find the most efficient initial layout while minimizing the number of SWAP gates needed. Importantly, `SabreLayout`, just by itself, internally optimizes both the layout and routing by storing the solution that adds the least number of SWAP gates. Note that when using just **SabreLayout**, we cannot change the heuristic of SABRE, but we are able to customize the number of `layout_trials`."
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    "### Step 1: Map classical inputs to a quantum problem\n",
    "\n",
    "A **GHZ (Greenberger-Horne-Zeilinger)** circuit is a quantum circuit that prepares an entangled state where all qubits are either in the `|0...0⟩` or `|1...1⟩` state. The GHZ state for $n$ qubits is mathematically represented as:\n",
    "$$ |\\text{GHZ}\\rangle = \\frac{1}{\\sqrt{2}} \\left( |0\\rangle^{\\otimes n} + |1\\rangle^{\\otimes n} \\right) $$\n",
    "\n",
    "It is constructed by applying:\n",
    "1. A Hadamard gate to the first qubit to create superposition.\n",
    "2. A series of CNOT gates to entangle the remaining qubits with the first.\n",
    "\n",
    "For this example, we intentionally construct a **star-topology GHZ circuit** instead of a linear-topology one. In the star topology, the first qubit acts as the \"hub,\" and all other qubits are entangled directly with it using CNOT gates. This choice is deliberate because, while the **linear topology GHZ state** can theoretically be implemented in $ O(N) $ depth on a linear coupling map without any SWAP gates, SABRE would trivially find an optimal solution by mapping a 100-qubit GHZ circuit to a subgraph of the backend's heavy-hex coupling map.\n",
    "\n",
    "The **star topology GHZ circuit** poses a significantly more challenging problem. Although it can still theoretically be executed in $ O(N) $ depth without SWAP gates, finding this solution requires identifying an optimal initial layout, which is much harder due to the non-linear connectivity of the circuit. This topology serves as a better test case for evaluating SABRE, as it demonstrates how configuration parameters impact layout and routing performance under more complex conditions.\n",
    "\n",
    "![ghz_star_topology.png](/images/tutorials/transpilation-optimizations-with-sabre/ghz_star_topology.avif)\n",
    "\n",
    "Notably:\n",
    "- The **HighLevelSynthesis** tool can produce the optimal $ O(N) $ depth solution for the star topology GHZ circuit without introducing SWAP gates, like shown in the image above.\n",
    "- Alternatively, the **StarPrerouting** pass can reduce the depth further by guiding SABRE's routing decisions, though it may still introduce some SWAP gates. However, StarPrerouting increases runtime and requires integration into the initial transpilation process.\n",
    "\n",
    "For the purposes of this notebook, we exclude both HighLevelSynthesis and StarPrerouting to isolate and highlight the direct impact of SABRE configuration on runtime and circuit depth. By measuring the expectation value $ \\langle Z_0 Z_i \\rangle $ for each qubit pair, we analyze:\n",
    "- How well SABRE reduces SWAP gates and circuit depth.\n",
    "- The effect of these optimizations on the fidelity of the executed circuit, where deviations from $ \\langle Z_0 Z_i \\rangle = 1 $ indicate loss of entanglement.!"
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    "# set seed for reproducibility\n",
    "seed = 42\n",
    "num_qubits = 100\n",
    "\n",
    "# Create GHZ circuit\n",
    "qc = QuantumCircuit(num_qubits)\n",
    "qc.h(0)\n",
    "for i in range(1, num_qubits):\n",
    "    qc.cx(0, i)\n",
    "\n",
    "qc.measure_all()"
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    "Next, we will map the operators of interest to evaluate the behavior of the system. Specifically, we will use `ZZ` operators between qubits to examine how the entanglement degrades as the qubits become farther apart. This analysis is critical because inaccuracies in the expectation values  $\\langle Z_0 Z_i \\rangle$ for distant qubits can reveal the impact of noise and errors in the circuit execution. By studying these deviations, we gain insight into how well the circuit preserves entanglement under different SABRE configurations and how effectively SABRE minimizes the impact of hardware constraints."
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'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZIII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZII', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZI', 'ZIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIZ']\n",
      "99\n"
     ]
    }
   ],
   "source": [
    "# ZZII...II, ZIZI...II, ... , ZIII...IZ\n",
    "operator_strings = [\n",
    "    \"Z\" + \"I\" * i + \"Z\" + \"I\" * (num_qubits - 2 - i)\n",
    "    for i in range(num_qubits - 1)\n",
    "]\n",
    "print(operator_strings)\n",
    "print(len(operator_strings))\n",
    "\n",
    "operators = [SparsePauliOp(operator) for operator in operator_strings]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf0689f-fd02-4d3d-b7e7-aac9fdf10032",
   "metadata": {},
   "source": [
    "### Step 2: Optimize problem for quantum hardware execution\n",
    "\n",
    "In this step, we focus on optimizing the circuit layout for execution on a specific quantum hardware device with 127 qubits. This is the main focus of the notebook, as we perform **SABRE optimizations and transpilation** to achieve the best circuit performance. Using the `SabreLayout` pass, we determine an initial qubit mapping that minimizes the need for SWAP gates during routing. By passing the `coupling_map` of the target backend, `SabreLayout` adapts the layout to the device's connectivity constraints.\n",
    "\n",
    "We will use `generate_preset_pass_manager` with `optimization_level=3` for the transpilation process and customize the `SabreLayout` pass with different configurations. The goal is to find a setup that produces a transpiled circuit with the **lowest size and/or depth**, demonstrating the impact of SABRE optimizations.\n",
    "\n",
    "#### Why Are Circuit Size and Depth Important?\n",
    "\n",
    "- **Lower size (gate count):** Reduces the number of operations, minimizing opportunities for errors to accumulate.\n",
    "- **Lower depth:** Shortens the overall execution time, which is critical for avoiding decoherence and maintaining quantum state fidelity.\n",
    "\n",
    "By optimizing these metrics, we improve the circuit’s reliability and execution accuracy on noisy quantum hardware."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908ff593-9573-4d49-b2ac-8b3516f33414",
   "metadata": {},
   "source": [
    "Select the backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c426faa1-6656-4ac4-8c41-b37720f7bdc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using backend: <IBMBackend('ibm_sherbrooke')>\n"
     ]
    }
   ],
   "source": [
    "# QiskitRuntimeService.save_account(channel=\"ibm_quantum\", token=\"<MY_IBM_QUANTUM_TOKEN>\", overwrite=True, set_as_default=True)\n",
    "service = QiskitRuntimeService(channel=\"ibm_quantum\")\n",
    "backend = service.least_busy(\n",
    "    operational=True, simulator=False, min_num_qubits=127\n",
    ")\n",
    "print(f\"Using backend: {backend}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f5d55c-6f88-46c5-9fe7-fc9e4f6490da",
   "metadata": {},
   "source": [
    "To evaluate the impact of different configurations on circuit optimization, we will create three pass managers, each with unique settings for the `SabreLayout` pass. These configurations enable us to analyze the trade-off between circuit quality and transpilation time.\n",
    "\n",
    "#### Key parameters\n",
    "- **`max_iterations`**: The number of forward-backward routing iterations to refine the layout and reduce routing costs.\n",
    "- **`layout_trials`**: The number of random initial layouts tested, selecting the one that minimizes SWAP gates.\n",
    "- **`swap_trials`**: The number of routing trials for each layout, refining gate placement for better routing.\n",
    "\n",
    "Increasing `layout_trials` and `swap_trials` enables more thorough optimization but increases transpilation time.\n",
    "\n",
    "#### Configurations in this notebook\n",
    "1. **`pm_1`**: Default settings with `optimization_level=3`.\n",
    "   - `max_iterations=4`\n",
    "   - `layout_trials=20`\n",
    "   - `swap_trials=20`\n",
    "\n",
    "2. **`pm_2`**: Increases the number of trials for better exploration.\n",
    "   - `max_iterations=4`\n",
    "   - `layout_trials=200`\n",
    "   - `swap_trials=200`\n",
    "\n",
    "3. **`pm_3`**: Extends `pm_2` by increasing the number of iterations for further refinement.\n",
    "   - `max_iterations=8`\n",
    "   - `layout_trials=200`\n",
    "   - `swap_trials=200`\n",
    "\n",
    "By comparing the results of these configurations, we aim to determine which achieves the best balance between circuit quality (e.g., size and depth) and computational cost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "967e8795-8fe4-4baa-9d99-04fccd5b8680",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the coupling map from the backend\n",
    "cmap = CouplingMap(backend().configuration().coupling_map)\n",
    "\n",
    "# Create the SabreLayout passes for the custom configurations\n",
    "sl_2 = SabreLayout(\n",
    "    coupling_map=cmap,\n",
    "    seed=seed,\n",
    "    max_iterations=4,\n",
    "    layout_trials=200,\n",
    "    swap_trials=200,\n",
    ")\n",
    "sl_3 = SabreLayout(\n",
    "    coupling_map=cmap,\n",
    "    seed=seed,\n",
    "    max_iterations=8,\n",
    "    layout_trials=200,\n",
    "    swap_trials=200,\n",
    ")\n",
    "\n",
    "# Create the pass managers, need to first create then configure the SabreLayout passes\n",
    "pm_1 = generate_preset_pass_manager(\n",
    "    optimization_level=3, backend=backend, seed_transpiler=seed\n",
    ")\n",
    "pm_2 = generate_preset_pass_manager(\n",
    "    optimization_level=3, backend=backend, seed_transpiler=seed\n",
    ")\n",
    "pm_3 = generate_preset_pass_manager(\n",
    "    optimization_level=3, backend=backend, seed_transpiler=seed\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33cc2403-bcd6-44dc-8fda-9501afeb3f91",
   "metadata": {},
   "source": [
    "Now we can configure the `SabreLayout` pass in the custom pass managers. To do this we know that for the default `generate_preset_pass_manager` on `optimization_level=3`, the `SabreLayout` pass is at index 2, as `SabreLayout` occurs after `SetLayout` and `VF2Laout` passes. We can access this pass and modify its parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "911e77c8-163d-46f6-b381-743fb9a4163f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pm_2.layout.replace(index=2, passes=sl_2)\n",
    "pm_3.layout.replace(index=2, passes=sl_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f81a90-191a-4f2f-b18b-f1b26fdf28fc",
   "metadata": {},
   "source": [
    "With each pass manager configured, we will now execute the transpilation process for each. To compare results, we will track key metrics, including the transpilation time, the depth of the circuit (measured as the two-qubit gate depth), and the total number of gates in the transpiled circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c1b41d7-8972-4789-93a1-31cc67620e2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass manager 1 (4,20,20)  : Depth 382, Size 3119, Time 0.5739 s\n",
      "Pass manager 2 (4,200,200): Depth 370, Size 2864, Time 1.8647 s\n",
      "  - Depth improvement: 3.14%\n",
      "  - Size improvement: 8.18%\n",
      "  - Time increase: 224.94%\n",
      "Pass manager 3 (8,200,200): Depth 327, Size 2618, Time 1.2319 s\n",
      "  - Depth improvement: 14.40%\n",
      "  - Size improvement: 16.06%\n",
      "  - Time increase: 114.67%\n"
     ]
    }
   ],
   "source": [
    "# Transpile the circuit with each pass manager and measure the time\n",
    "t0 = time.time()\n",
    "tqc_1 = pm_1.run(qc)\n",
    "t1 = time.time() - t0\n",
    "t0 = time.time()\n",
    "tqc_2 = pm_2.run(qc)\n",
    "t2 = time.time() - t0\n",
    "t0 = time.time()\n",
    "tqc_3 = pm_3.run(qc)\n",
    "t3 = time.time() - t0\n",
    "\n",
    "# Obtain the depths and the total number of gates (circuit size)\n",
    "depth_1 = tqc_1.depth(lambda x: x.operation.num_qubits == 2)\n",
    "depth_2 = tqc_2.depth(lambda x: x.operation.num_qubits == 2)\n",
    "depth_3 = tqc_3.depth(lambda x: x.operation.num_qubits == 2)\n",
    "size_1 = tqc_1.size()\n",
    "size_2 = tqc_2.size()\n",
    "size_3 = tqc_3.size()\n",
    "\n",
    "# Transform the observables to match the backend's ISA\n",
    "operators_list_1 = [op.apply_layout(tqc_1.layout) for op in operators]\n",
    "operators_list_2 = [op.apply_layout(tqc_2.layout) for op in operators]\n",
    "operators_list_3 = [op.apply_layout(tqc_3.layout) for op in operators]\n",
    "\n",
    "# Compute improvements compared to pass manager 1 (default)\n",
    "depth_improvement_2 = ((depth_1 - depth_2) / depth_1) * 100\n",
    "depth_improvement_3 = ((depth_1 - depth_3) / depth_1) * 100\n",
    "size_improvement_2 = ((size_1 - size_2) / size_1) * 100\n",
    "size_improvement_3 = ((size_1 - size_3) / size_1) * 100\n",
    "time_increase_2 = ((t2 - t1) / t1) * 100\n",
    "time_increase_3 = ((t3 - t1) / t1) * 100\n",
    "\n",
    "print(\n",
    "    f\"Pass manager 1 (4,20,20)  : Depth {depth_1}, Size {size_1}, Time {t1:.4f} s\"\n",
    ")\n",
    "print(\n",
    "    f\"Pass manager 2 (4,200,200): Depth {depth_2}, Size {size_2}, Time {t2:.4f} s\"\n",
    ")\n",
    "print(f\"  - Depth improvement: {depth_improvement_2:.2f}%\")\n",
    "print(f\"  - Size improvement: {size_improvement_2:.2f}%\")\n",
    "print(f\"  - Time increase: {time_increase_2:.2f}%\")\n",
    "print(\n",
    "    f\"Pass manager 3 (8,200,200): Depth {depth_3}, Size {size_3}, Time {t3:.4f} s\"\n",
    ")\n",
    "print(f\"  - Depth improvement: {depth_improvement_3:.2f}%\")\n",
    "print(f\"  - Size improvement: {size_improvement_3:.2f}%\")\n",
    "print(f\"  - Time increase: {time_increase_3:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7575463a-773d-4939-9241-05499caf2807",
   "metadata": {},
   "source": [
    "The results demonstrate that increasing the number of trials (`layout_trials` and `swap_trials`) can significantly improve circuit quality by reducing both depth and size. However, this improvement often comes at the cost of increased runtime due to the additional computation required to explore more potential layouts and routing paths.\n",
    "\n",
    "Increasing the `max_iterations` can further enhance optimization by refining the layout through more forward-backward routing cycles. In this case, increasing `max_iterations` resulted in the most significant reduction in circuit depth and size, even reducing runtime compared to `pm_2`, likely by streamlining subsequent optimization stages.  It’s important to note, however, that the effectiveness of increasing `max_iterations` can vary significantly depending on the circuit. While more iterations may yield better layout and routing choices, they provide no guarantees and depend heavily on the circuit’s structure and the complexity of the connectivity constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f39d3c4-a091-4004-b230-b8fc1aec85f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results of the metrics\n",
    "times = [t1, t2, t3]\n",
    "depths = [depth_1, depth_2, depth_3]\n",
    "sizes = [size_1, size_2, size_3]\n",
    "pm_names = [\n",
    "    \"pm_1 (4 iter, 20 trials)\",\n",
    "    \"pm_2 (4 iter, 200 trials)\",\n",
    "    \"pm_3 (8 iter, 200 trials)\",\n",
    "]\n",
    "colors = plt.cm.viridis(np.linspace(0.2, 0.8, len(pm_names)))\n",
    "\n",
    "# Create a figure with three subplots\n",
    "fig, axs = plt.subplots(3, 1, figsize=(6, 9), sharex=True)\n",
    "axs[0].bar(pm_names, times, color=colors)\n",
    "axs[0].set_ylabel(\"Time (s)\", fontsize=12)\n",
    "axs[0].set_title(\"Transpilation Time\", fontsize=14)\n",
    "axs[0].grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "axs[1].bar(pm_names, depths, color=colors)\n",
    "axs[1].set_ylabel(\"Depth\", fontsize=12)\n",
    "axs[1].set_title(\"Circuit Depth\", fontsize=14)\n",
    "axs[1].grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "axs[2].bar(pm_names, sizes, color=colors)\n",
    "axs[2].set_ylabel(\"Size\", fontsize=12)\n",
    "axs[2].set_title(\"Circuit Size\", fontsize=14)\n",
    "axs[2].set_xticks(range(len(pm_names)))\n",
    "axs[2].set_xticklabels(pm_names, fontsize=10, rotation=15)\n",
    "axs[2].grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "# Add some spacing between subplots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dbaa723-b6a5-48de-a407-e3225b5026e0",
   "metadata": {},
   "source": [
    "### Step 3: Execute using Qiskit primitives\n",
    "\n",
    "In this step, we use the `Estimator` primitive to calculate the expectation values $\\langle Z_0 Z_i \\rangle$ for the `ZZ` operators, evaluating the entanglement and execution quality of the transpiled circuits. To align with typical user workflows, we submit the job for execution and apply error suppression using **dynamical decoupling**, a technique that mitigates decoherence by inserting gate sequences to preserve qubit states. Additionally, we specify a resilience level to counteract noise, with higher levels providing more accurate results at the cost of increased processing time. This approach allows us to assess the performance of each pass manager configuration under realistic execution conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48763105-5beb-4ce2-bd92-f46c53e0e265",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = EstimatorOptions()\n",
    "options.resilience_level = 1\n",
    "options.dynamical_decoupling.enable = True\n",
    "options.dynamical_decoupling.sequence_type = \"XY4\"\n",
    "\n",
    "# Create an Estimator object\n",
    "estimator = Estimator(backend, options=options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "623a630a-f9ac-4f45-b09e-9aec852687e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cx7rsyfbqkhg0088r41g\n",
      "cx7rsyzrkac00089ay9g\n",
      "cx7rszfztp30008fspm0\n"
     ]
    }
   ],
   "source": [
    "# Submit the circuit to Estimator\n",
    "job_1 = estimator.run([(tqc_1, operators_list_1)])\n",
    "job_1_id = job_1.job_id()\n",
    "print(job_1_id)\n",
    "\n",
    "job_2 = estimator.run([(tqc_2, operators_list_2)])\n",
    "job_2_id = job_2.job_id()\n",
    "print(job_2_id)\n",
    "\n",
    "job_3 = estimator.run([(tqc_3, operators_list_3)])\n",
    "job_3_id = job_3.job_id()\n",
    "print(job_3_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fe47039-17d5-4588-8f6f-eef9fb131808",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job 1 done\n",
      "Job 2 done\n",
      "Job 3 done\n"
     ]
    }
   ],
   "source": [
    "# Run the jobs\n",
    "result_1 = job_1.result()[0]\n",
    "print(\"Job 1 done\")\n",
    "result_2 = job_2.result()[0]\n",
    "print(\"Job 2 done\")\n",
    "result_3 = job_3.result()[0]\n",
    "print(\"Job 3 done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e69e69b-2d29-430e-ad6b-053f11db5b54",
   "metadata": {},
   "source": [
    "### Step 4: Post-process and return result in desired classical format\n",
    "\n",
    "Once the job completes, we analyze the results by plotting the expectation values  $\\langle Z_0 Z_i \\rangle$ for each qubit. In an ideal simulation, all  $\\langle Z_0 Z_i \\rangle$ values should be 1, reflecting perfect entanglement across the qubits. However, due to noise and hardware constraints, the expectation values typically decrease as `i` increases, revealing how entanglement degrades over distance.\n",
    "\n",
    "In this step, we compare the results from each pass manager configuration to the ideal simulation. By examining the deviation of $\\langle Z_0 Z_i \\rangle$ from 1 for each configuration, we can quantify how well each pass manager preserves entanglement and mitigates the effects of noise. This analysis allows us to directly assess the impact of the SABRE optimizations on execution fidelity, providing insights into which configuration achieves the best balance between optimization quality and execution performance.\n",
    "\n",
    "The results will be visualized to highlight differences across pass managers, showcasing how improvements in layout and routing influence the final circuit execution on noisy quantum hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6cef357f-10a4-48ec-a197-c50a37cdf353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = list(range(1, len(operators) + 1))  # Distance between the Z operators\n",
    "\n",
    "# Normalize and process expectation values for each result\n",
    "values_1 = [v / result_1.data.evs[0] for v in result_1.data.evs]\n",
    "values_2 = [v / result_2.data.evs[0] for v in result_2.data.evs]\n",
    "values_3 = [v / result_3.data.evs[0] for v in result_3.data.evs]\n",
    "\n",
    "plt.plot(\n",
    "    data,\n",
    "    values_1,\n",
    "    marker=\"o\",\n",
    "    label=\"pm_1 (iters=4, swap_trials=20, layout_trials=20)\",\n",
    ")\n",
    "plt.plot(\n",
    "    data,\n",
    "    values_2,\n",
    "    marker=\"s\",\n",
    "    label=\"pm_2 (iters=4, swap_trials=200, layout_trials=200)\",\n",
    ")\n",
    "plt.plot(\n",
    "    data,\n",
    "    values_3,\n",
    "    marker=\"^\",\n",
    "    label=\"pm_3 (iters=8, swap_trials=200, layout_trials=200)\",\n",
    ")\n",
    "plt.xlabel(\"Distance between qubits $i$\")\n",
    "plt.ylabel(r\"$\\langle Z_i Z_0 \\rangle / \\langle Z_1 Z_0 \\rangle $\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f81e102-2ee3-4d1b-ab30-82120ad75c7b",
   "metadata": {},
   "source": [
    "### Analysis of Results\n",
    "\n",
    "The plot shows how the normalized expectation values $\\langle Z_0 Z_i \\rangle$  vary across qubits for the three pass managers. Ideally, all values should remain close to 1, reflecting strong entanglement. However, noise and hardware errors introduce deviations, influenced by circuit size and depth.\n",
    "\n",
    "With the largest circuit size and depth, `pm_1` exhibits significant variations in $\\langle Z_0 Z_i \\rangle$ , particularly for qubits farther apart, indicating poorer preservation of entanglement due to accumulated noise. In comparison, `pm_2` shows slightly improved results, with fewer deviations, reflecting its modest reductions in circuit size and depth (1–10% better than `pm_1`). These smaller improvements lead to slightly better fidelity but do not fully address the noise issues.\n",
    "\n",
    "`pm_3`, with the most significant reductions in size and depth (11–20% better than `pm_1`), delivers the best results, with values much closer to 1 and fewer deviations. This highlights the critical role of minimizing circuit size and depth in reducing noise and error propagation, which significantly improves execution fidelity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b6b6df-9954-4b89-86b6-7123293f72de",
   "metadata": {},
   "source": [
    "## Part II. Configuring the heuristic in SABRE and using Serverless\n",
    "\n",
    "In addition to adjusting trial numbers, SABRE allows customization of the routing heuristic used during transpilation. By default, `SabreLayout` employs the decay heuristic, which dynamically weights qubits based on their likelihood of being swapped. To use a different heuristic (such as the `lookahead` heuristic), you can create a custom `SabreSwap` pass and connect it to `SabreLayout` by running a `PassManager` with `FullAncillaAllocation`, `EnlargeWithAncilla`, and `ApplyLayout`. When using `SabreSwap` as a parameter for `SabreLayout`, only one layout trial is performed by default. To efficiently run multiple layout trials, we leverage the serverless runtime for parallelization. For more about serverless, see the [Serverless documentation](https://docs.quantum.ibm.com/guides/serverless).\n",
    "\n",
    "### How to Change the Routing Heuristic\n",
    "1. Create a custom `SabreSwap` pass with the desired heuristic.\n",
    "2. Use this custom `SabreSwap` as the routing method for the `SabreLayout` pass.\n",
    "\n",
    "While it is possible to run multiple layout trials using a loop, serverless runtime is the better choice for large-scale and more vigorous experiments. Serverless allows for parallel execution of layout trials, significantly speeding up the process when optimizing larger circuits or conducting numerous experiments. This makes it especially valuable when working with resource-intensive tasks or when time efficiency is critical.\n",
    "\n",
    "This section focuses solely on step 2 of optimization: minimizing circuit size and depth to achieve the best possible transpiled circuit. Building on the earlier results, we now explore how heuristic customization and serverless parallelization can further enhance optimization performance, making it suitable for large-scale quantum circuit transpilation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc1226e3-f599-4048-9cd2-b736d134695c",
   "metadata": {},
   "source": [
    "### Results without serverless runtime (1 layout trial):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "93349450-0479-4a2d-a667-1f7d38c06740",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default (heuristic='decay')    : Depth 395, Size 3552, Time 1.8260750770568848\n",
      "Custom  (heuristic='lookahead'): Depth 389, Size 3384, Time 0.8961391448974609\n"
     ]
    }
   ],
   "source": [
    "swap_trials = 200\n",
    "\n",
    "# Default PassManager with `SabreLayout` and `SabreSwap`, using heuristic \"decay\"\n",
    "sr_default = SabreSwap(\n",
    "    coupling_map=cmap, heuristic=\"decay\", trials=swap_trials, seed=seed\n",
    ")\n",
    "sl_default = SabreLayout(\n",
    "    coupling_map=cmap, routing_pass=sr_default, seed=seed\n",
    ")\n",
    "pm_default = generate_preset_pass_manager(\n",
    "    optimization_level=3, backend=backend, seed_transpiler=seed\n",
    ")\n",
    "pm_default.layout.replace(index=2, passes=sl_default)\n",
    "pm_default.routing.replace(index=1, passes=sr_default)\n",
    "\n",
    "t0 = time.time()\n",
    "tqc_default = pm_default.run(qc)\n",
    "t_default = time.time() - t0\n",
    "size_default = tqc_default.size()\n",
    "depth_default = tqc_default.depth(lambda x: x.operation.num_qubits == 2)\n",
    "\n",
    "\n",
    "# Custom PassManager with `SabreLayout` and `SabreSwap`, using heuristic \"lookahead\"\n",
    "sr_custom = SabreSwap(\n",
    "    coupling_map=cmap, heuristic=\"lookahead\", trials=swap_trials, seed=seed\n",
    ")\n",
    "sl_custom = SabreLayout(coupling_map=cmap, routing_pass=sr_custom, seed=seed)\n",
    "pm_custom = generate_preset_pass_manager(\n",
    "    optimization_level=3, backend=backend, seed_transpiler=seed\n",
    ")\n",
    "pm_custom.layout.replace(index=2, passes=sl_custom)\n",
    "pm_custom.routing.replace(index=1, passes=sr_custom)\n",
    "\n",
    "t0 = time.time()\n",
    "tqc_custom = pm_custom.run(qc)\n",
    "t_custom = time.time() - t0\n",
    "size_custom = tqc_custom.size()\n",
    "depth_custom = tqc_custom.depth(lambda x: x.operation.num_qubits == 2)\n",
    "\n",
    "print(\n",
    "    f\"Default (heuristic='decay')    : Depth {depth_default}, Size {size_default}, Time {t_default}\"\n",
    ")\n",
    "print(\n",
    "    f\"Custom  (heuristic='lookahead'): Depth {depth_custom}, Size {size_custom}, Time {t_custom}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "341c692f-4442-44ee-8e35-b8165fa6c737",
   "metadata": {},
   "source": [
    "Here we see that the `lookahead` heuristic performs better than the `decay` heuristic in terms of circuit depth, size, and time. This improvements highlights how we can improve SABRE beyond just trials and iterations for your specific circuit and hardware constraints. Note that these results are based on a single layout trial. To achieve more accurate results, we recommend running multiple layout trials, which can be done efficiently using the serverless runtime."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bfa197e-9b7d-48f3-a1bb-edbbb392157c",
   "metadata": {},
   "source": [
    "### Results with serverless runtime (multiple layout trials)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de59e51f-004b-4eb6-8aa9-4a1427856ee3",
   "metadata": {},
   "source": [
    "Qiskit Serverless requires setting up your workload’s `.py` files into a dedicated directory. The following code cell is a Python file in the `source_files` directory named `transpile_remote.py`. This file contains the function that runs the transpilation process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0acdd652-154d-4feb-a4ab-f1f7f7f9d82b",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# This cell is hidden from users, it makes sure the `source_files` directory exists\n",
    "from pathlib import Path\n",
    "\n",
    "Path(\"source_files\").mkdir(exists_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbd1cae7-cec2-41c5-8903-c65a73f278e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile source_files/transpile_remote.py\n",
    "import time\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "from qiskit.transpiler.passes import SabreLayout, SabreSwap\n",
    "from qiskit.transpiler import CouplingMap\n",
    "from qiskit_serverless import get_arguments, save_result, distribute_task, get\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "\n",
    "service = QiskitRuntimeService()\n",
    "\n",
    "@distribute_task(target={\n",
    "    \"cpu\": 1,\n",
    "    \"mem\": 1024 * 1024 * 1024\n",
    "})\n",
    "def transpile_remote(qc, optimization_level, backend, seed, swap_trials, heuristic):\n",
    "    \"\"\"Transpiles an abstract circuit into an ISA circuit for a given backend.\"\"\"\n",
    "    pm = generate_preset_pass_manager(\n",
    "        optimization_level=optimization_level,\n",
    "        backend=backend,\n",
    "        seed_transpiler=seed\n",
    "    )\n",
    "    # Changing the `SabreLayout` and `SabreSwap` passes to use the custom configurations\n",
    "    cmap = CouplingMap(backend().configuration().coupling_map)\n",
    "    sr = SabreSwap(coupling_map=cmap, heuristic=heuristic, trials=swap_trials, seed=42)\n",
    "    sl = SabreLayout(coupling_map=cmap, routing_pass=sr, seed=seed)\n",
    "    pm.layout.replace(index=2, passes=sl)\n",
    "    pm.routing.replace(index=1, passes=sr)\n",
    "\n",
    "    # Measure the transpile time\n",
    "    start_time = time.time()  # Start timer\n",
    "    tqc = pm.run(qc)  # Transpile the circuit\n",
    "    end_time = time.time()  # End timer\n",
    "\n",
    "    transpile_time = end_time - start_time  # Calculate the elapsed time\n",
    "    return tqc, transpile_time  # Return both the transpiled circuit and the transpile time\n",
    "\n",
    "# Get program arguments\n",
    "arguments = get_arguments()\n",
    "circuit = arguments.get(\"circuit\")\n",
    "backend_name = arguments.get(\"backend_name\")\n",
    "optimization_level = arguments.get(\"optimization_level\")\n",
    "seed_list = arguments.get(\"seed_list\")\n",
    "swap_trials = arguments.get(\"swap_trials\")\n",
    "heuristic = arguments.get(\"heuristic\")\n",
    "\n",
    "# Get the backend\n",
    "service = QiskitRuntimeService(channel='ibm_quantum')\n",
    "backend = service.backend(backend_name)\n",
    "print(backend)\n",
    "\n",
    "# Transpile the circuits\n",
    "transpile_worker_references = [\n",
    "    transpile_remote(circuit, optimization_level, backend, seed, swap_trials, heuristic)\n",
    "    for seed in arguments.get(\"seed_list\")\n",
    "]\n",
    "\n",
    "results_with_times = get(transpile_worker_references)\n",
    "\n",
    "# Separate the transpiled circuits and their transpile times\n",
    "transpiled_circuits = [result[0] for result in results_with_times]\n",
    "transpile_times = [result[1] for result in results_with_times]\n",
    "\n",
    "# Save both results and transpile times\n",
    "save_result({\"transpiled_circuits\": transpiled_circuits, \"transpile_times\": transpile_times})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfafbd2-c01d-407c-ac00-5326c5e70dbc",
   "metadata": {},
   "source": [
    "The following cell uploads the `transpile_remote.py` file as a Qiskit Serverless program under the name `transpile_remote_serverless`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "068395ed-1c05-47ec-9bfe-06f1bf348641",
   "metadata": {},
   "outputs": [],
   "source": [
    "serverless = QiskitServerless()\n",
    "\n",
    "transpile_remote_demo = QiskitFunction(\n",
    "    title=\"transpile_remote_serverless\",\n",
    "    entrypoint=\"transpile_remote.py\",\n",
    "    working_dir=\"./source_files/\",\n",
    ")\n",
    "serverless.upload(transpile_remote_demo)\n",
    "transpile_remote_serverless = serverless.load(\"transpile_remote_serverless\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2872bf-ffc0-4194-8957-e17b2c70267c",
   "metadata": {},
   "source": [
    "Generate 20 different seeds to represent 20 different layout trials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "240ab42d-1a31-4453-a39d-cad4f99aa500",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "num_seeds = 200  # represents the different layout trials\n",
    "seed_list = [seed + i for i in range(num_seeds)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a947f246-e9e9-43bd-92ed-dab4a5063fe9",
   "metadata": {},
   "source": [
    "Run the uploaded program and pass inputs for lookahead heuristic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7264ec95-3e8d-48d5-9859-6c04a986450b",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_lookahead = transpile_remote_serverless.run(\n",
    "    circuit=qc,\n",
    "    backend_name=\"ibm_brisbane\",\n",
    "    optimization_level=3,\n",
    "    seed_list=seed_list,\n",
    "    swap_trials=swap_trials,\n",
    "    heuristic=\"lookahead\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b8a0ee4a-2c3c-4f48-be58-43fd5c87ed2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'f5475545-dd80-47fe-8a3d-3ef5d3f478fe'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_lookahead.job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "16a777a8-6e42-4b35-9297-42cf9d737ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'QUEUED'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_lookahead.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a87c46f-3f58-46a9-b691-2baff40931fc",
   "metadata": {},
   "source": [
    "Receive the logs and results from the serverless runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f4cf419b-0a96-494b-9425-257bb23bed89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No logs yet.\n"
     ]
    }
   ],
   "source": [
    "logs_lookahead = job_lookahead.logs()\n",
    "print(logs_lookahead)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cb83f2-7c9c-4e81-a029-31b0503f28a3",
   "metadata": {},
   "source": [
    "Once a program is `DONE`, you can use `job.results()` to fetch the result stored in `save_result()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7c7f553a-df24-4dde-9ff0-366eb30d7e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the job with lookahead heuristic\n",
    "start_time = time.time()\n",
    "results_lookahead = job_lookahead.result()\n",
    "end_time = time.time()\n",
    "\n",
    "job_lookahead_time = end_time - start_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab9f9cf-a15c-4f51-8cdc-08543e7e426c",
   "metadata": {},
   "source": [
    "Now perform the same for decay heuristic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a6d3623a-97f3-423c-b52f-8bd77c2939df",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_decay = transpile_remote_serverless.run(\n",
    "    circuit=qc,\n",
    "    backend_name=\"ibm_brisbane\",\n",
    "    optimization_level=3,\n",
    "    seed_list=seed_list,\n",
    "    swap_trials=swap_trials,\n",
    "    heuristic=\"decay\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d56b0a7b-0898-48fd-b73a-5ca6d3f01a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'084be271-68d0-49a3-a25d-3f5077e264ac'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_decay.job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b390e6df-38c7-4dca-9e95-7fb0ea9ce822",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No logs yet.\n"
     ]
    }
   ],
   "source": [
    "logs_decay = job_decay.logs()\n",
    "print(logs_decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e5936de9-6b7d-408c-8dcc-16cfa8021f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the job with the decay heuristic\n",
    "start_time = time.time()\n",
    "results_decay = job_decay.result()\n",
    "end_time = time.time()\n",
    "\n",
    "job_decay_time = end_time - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f56f6557-e410-4843-8892-10fd5c44487e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Total Transpilation Time (Serial Execution) ===\n",
      "Decay Heuristic    : 462.49 seconds\n",
      "Lookahead Heuristic: 439.38 seconds\n",
      "\n",
      "=== Serverless Job Time (Parallel Execution) ===\n",
      "Decay Heuristic    : 172.37 seconds\n",
      "Lookahead Heuristic: 159.30 seconds\n",
      "\n",
      "=== Average Time Per Transpilation ===\n",
      "Decay Heuristic (Serial)    : 2.31 seconds\n",
      "Decay Heuristic (Serverless): 0.86 seconds\n",
      "Lookahead Heuristic (Serial)    : 2.20 seconds\n",
      "Lookahead Heuristic (Serverless): 0.80 seconds\n",
      "\n",
      "=== Serverless Improvement ===\n",
      "Decay Heuristic    : 62.73%\n",
      "Lookahead Heuristic: 63.74%\n"
     ]
    }
   ],
   "source": [
    "# Extract transpilation times\n",
    "transpile_times_decay = results_decay[\"transpile_times\"]\n",
    "transpile_times_lookahead = results_lookahead[\"transpile_times\"]\n",
    "\n",
    "# Calculate total transpilation time for serial execution\n",
    "total_transpile_time_decay = sum(transpile_times_decay)\n",
    "total_transpile_time_lookahead = sum(transpile_times_lookahead)\n",
    "\n",
    "# Print total transpilation time\n",
    "print(\"=== Total Transpilation Time (Serial Execution) ===\")\n",
    "print(f\"Decay Heuristic    : {total_transpile_time_decay:.2f} seconds\")\n",
    "print(f\"Lookahead Heuristic: {total_transpile_time_lookahead:.2f} seconds\")\n",
    "\n",
    "# Print serverless job time (parallel execution)\n",
    "print(\"\\n=== Serverless Job Time (Parallel Execution) ===\")\n",
    "print(f\"Decay Heuristic    : {job_decay_time:.2f} seconds\")\n",
    "print(f\"Lookahead Heuristic: {job_lookahead_time:.2f} seconds\")\n",
    "\n",
    "# Calculate and print average runtime per transpilation\n",
    "avg_transpile_time_decay = total_transpile_time_decay / num_seeds\n",
    "avg_transpile_time_lookahead = total_transpile_time_lookahead / num_seeds\n",
    "avg_job_time_decay = job_decay_time / num_seeds\n",
    "avg_job_time_lookahead = job_lookahead_time / num_seeds\n",
    "\n",
    "print(\"\\n=== Average Time Per Transpilation ===\")\n",
    "print(f\"Decay Heuristic (Serial)    : {avg_transpile_time_decay:.2f} seconds\")\n",
    "print(f\"Decay Heuristic (Serverless): {avg_job_time_decay:.2f} seconds\")\n",
    "print(\n",
    "    f\"Lookahead Heuristic (Serial)    : {avg_transpile_time_lookahead:.2f} seconds\"\n",
    ")\n",
    "print(\n",
    "    f\"Lookahead Heuristic (Serverless): {avg_job_time_lookahead:.2f} seconds\"\n",
    ")\n",
    "\n",
    "# Calculate and print serverless improvement percentage\n",
    "decay_improvement_percentage = (\n",
    "    (total_transpile_time_decay - job_decay_time) / total_transpile_time_decay\n",
    ") * 100\n",
    "lookahead_improvement_percentage = (\n",
    "    (total_transpile_time_lookahead - job_lookahead_time)\n",
    "    / total_transpile_time_lookahead\n",
    ") * 100\n",
    "\n",
    "print(\"\\n=== Serverless Improvement ===\")\n",
    "print(f\"Decay Heuristic    : {decay_improvement_percentage:.2f}%\")\n",
    "print(f\"Lookahead Heuristic: {lookahead_improvement_percentage:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e0ec1e-5a2f-4f7e-9fdf-d3c655e1f1b7",
   "metadata": {},
   "source": [
    "These results demonstrate the significant efficiency of serverless execution in quantum circuit transpilation. For 200 layout trials, serverless reduced the runtime by over 60% for both the `decay` and `lookahead` heuristics compared to serial execution. The total transpilation time for `decay` (443.29 s) and `lookahead` (400.65 s) highlights the computational effort required for serial execution, while the serverless job times (157.97 s and 158.12 s, respectively) showcase the dramatic runtime reduction achieved through parallelization. These findings emphasize that serverless execution can effectively scale to larger problems, offering substantial time savings and enabling broader exploration of optimization trials with minimal overhead."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72c1959-61e3-4613-8a1f-fcdea1e87234",
   "metadata": {},
   "source": [
    "Obtain the results from the serverless runtime and compare the results of the lookahead and decay heuristics. We will compare the sizes and depths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b2190bc7-c6df-4a34-bb12-f563033d8960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract sizes and depths\n",
    "sizes_lookahead = [\n",
    "    circuit.size() for circuit in results_lookahead[\"transpiled_circuits\"]\n",
    "]\n",
    "depths_lookahead = [\n",
    "    circuit.depth(lambda x: x.operation.num_qubits == 2)\n",
    "    for circuit in results_lookahead[\"transpiled_circuits\"]\n",
    "]\n",
    "sizes_decay = [\n",
    "    circuit.size() for circuit in results_decay[\"transpiled_circuits\"]\n",
    "]\n",
    "depths_decay = [\n",
    "    circuit.depth(lambda x: x.operation.num_qubits == 2)\n",
    "    for circuit in results_decay[\"transpiled_circuits\"]\n",
    "]\n",
    "\n",
    "\n",
    "def create_scatterplot(x, y1, y2, xlabel, ylabel, title, labels, colors):\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    plt.scatter(\n",
    "        x, y1, label=labels[0], color=colors[0], alpha=0.8, edgecolor=\"k\"\n",
    "    )\n",
    "    plt.scatter(\n",
    "        x, y2, label=labels[1], color=colors[1], alpha=0.8, edgecolor=\"k\"\n",
    "    )\n",
    "    plt.xlabel(xlabel, fontsize=12)\n",
    "    plt.ylabel(ylabel, fontsize=12)\n",
    "    plt.title(title, fontsize=14)\n",
    "    plt.legend(fontsize=10)\n",
    "    plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "create_scatterplot(\n",
    "    seed_list,\n",
    "    sizes_lookahead,\n",
    "    sizes_decay,\n",
    "    \"Seed\",\n",
    "    \"Size\",\n",
    "    \"Circuit Size\",\n",
    "    [\"lookahead\", \"Decay\"],\n",
    "    [\"blue\", \"red\"],\n",
    ")\n",
    "create_scatterplot(\n",
    "    seed_list,\n",
    "    depths_lookahead,\n",
    "    depths_decay,\n",
    "    \"Seed\",\n",
    "    \"Depth\",\n",
    "    \"Circuit Depth\",\n",
    "    [\"lookahead\", \"Decay\"],\n",
    "    [\"blue\", \"red\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c419bd-a7f3-42ac-94c6-5be75d7f4e40",
   "metadata": {},
   "source": [
    "Each point in the scatter plots above represents a layout trial, with the x-axis indicating the circuit depth and the y-axis indicating the circuit size. The results reveal that the lookahead heuristic generally outperforms the decay heuristic in minimizing circuit depth and circuit size. In practical applications, the goal is to identify the optimal layout trial for your chosen heuristic, whether prioritizing depth or size. This can be achieved by selecting the trial with the lowest value for the desired metric. Importantly, increasing the number of layout trials improves the chances of achieving a better result in terms of size or depth, but it comes at the cost of higher computational overhead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "44d7bc1f-8ebf-4969-a917-c27a7caa10ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lookahead: Min Depth 314 Min Size 2829\n",
      "Decay:     Min Depth 355 Min Size 3221\n"
     ]
    }
   ],
   "source": [
    "min_depth_lookahead = min(depths_lookahead)\n",
    "min_depth_decay = min(depths_decay)\n",
    "min_size_lookahead = min(sizes_lookahead)\n",
    "min_size_decay = min(sizes_decay)\n",
    "print(\n",
    "    \"Lookahead: Min Depth\",\n",
    "    min_depth_lookahead,\n",
    "    \"Min Size\",\n",
    "    min_size_lookahead,\n",
    ")\n",
    "print(\"Decay:     Min Depth\", min_depth_decay, \"Min Size\", min_size_decay)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d50345-90ca-4db4-8de4-f4c8061d61ee",
   "metadata": {},
   "source": [
    "In our initial comparison of heuristics using a single layout trial, the lookahead heuristic yielded the best result with a depth of 389 and size of 3384. By leveraging multiple trials through `QiskitServerless`, we explored various layout options for each heuristic, enabling a more comprehensive evaluation. Interestingly, the `decay` heuristic outperformed the `lookahead` heuristic in some cases, achieving a minimum depth of 355 and a minimum size of 3221 across the trials. However, the lookahead heuristic ultimately delivered the best overall results, with a minimum depth of 314 and a minimum size of 2829. This represents a significant improvement over its single-trial performance, highlighting the advantages of using serverless runtime for multi-trial optimization.\n",
    "\n",
    "These findings underscore the importance of experimenting with different heuristics and tailoring them to specific scenarios. By systematically comparing performance across multiple trials, we can identify the heuristic that delivers the optimal trade-off between depth and size for a given problem. This approach ensures more robust and efficient quantum circuit transpilation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41124313-26d7-4318-96b6-a940d84867ea",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# This cell is hidden from users, it cleans up the `source_files` directory\n",
    "from pathlib import Path\n",
    "\n",
    "Path(\"source_files/transpile_remote.py\").unlink()\n",
    "Path(\"source_files\").rmdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a50ea5-2f2b-4020-878b-d9b546483e48",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this tutorial, we explored how to optimize large circuits using SABRE in Qiskit. We demonstrated how to configure the `SabreLayout` pass with different parameters to balance circuit quality and transpilation runtime. We also showed how to customize the routing heuristic in SABRE and use the `QiskitServerless`runtime to parallelize layout trials efficiently for when `SabreSwap` is involved. By adjusting these parameters and heuristics, you can optimize the layout and routing of large circuits, ensuring they are executed efficiently on quantum hardware."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c06642-5577-4776-bda4-f55f805c067c",
   "metadata": {},
   "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_d9YWUSQIAvU9HXE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b63ce19-8074-470b-a3c8-e64bd0b430ee",
   "metadata": {},
   "source": [
    "© IBM Corp. 2024"
   ]
  }
 ],
 "metadata": {
  "description": "SABRE is an optimization tool for layout and routing. It is especially effective with large-scale circuits and complex coupling maps,",
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  "platform": "cloud",
  "title": "Transpilation Optimizations with SABRE"
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