# qiskit_ibm_transpiler
[](https://badge.fury.io/py/qiskit-ibm-transpiler)
[](https://opensource.org/licenses/Apache-2.0)
Leverage IBM Quantum's cutting-edge [Qiskit Transpiler Service](https://quantum.cloud.ibm.com/docs/guides/qiskit-transpiler-service) and [AI-powered transpiler passes](https://quantum.cloud.ibm.com/docs/guides/ai-transpiler-passes) to achieve superior circuit optimization through reinforcement learning algorithms.
## ✨ Key Features
- 🧠 **AI-Powered Optimization**: Advanced routing and circuit synthesis using reinforcement learning algorithms
- ⚡ **Local & Remote Modes**: Run AI passes locally or leverage cloud resources
- ☁️ **Cloud-ready**: Harness IBM Quantum's cloud infrastructure for intensive computations
- 🎯 **Drop-in Replacement**: Seamlessly integrate with existing Qiskit workflows
- 📈 **Superior Performance**: Our AI models typically outperform traditional heuristic algorithms. [Read the benchmark](https://arxiv.org/abs/2409.08844)
**Note**: The cloud transpilation capabilities are only available for IBM Quantum Premium Plan users. The local mode is available to any user and is enabled by default if the local mode dependencies are installed. Currently in beta release.
## 📦 Installation
### Basic Installation
```bash
pip install qiskit-ibm-transpiler
```
### With Local AI Mode Support
For running AI-powered transpiler passes locally:
```bash
pip install qiskit-ibm-transpiler[ai-local-mode]
```
### 🔐 Authentication
The package automatically authenticates using your [IBM Quantum Platform credentials](https://quantum.cloud.ibm.com/docs/en/guides/cloud-setup) aligned with how [Qiskit Runtime manages it](https://github.com/Qiskit/qiskit-ibm-runtime/tree/0.40.1?tab=readme-ov-file#qiskit-runtime-service-on-ibm-cloud):
- Environment variable: `QISKIT_IBM_TOKEN`
- Configuration file: `~/.qiskit/qiskit-ibm.json` (section: `default-ibm-quantum`)
## 🚀 Getting Started
### Tutorial and Examples
For a comprehensive introduction to the qiskit-ibm-transpiler library, start here:
- **📖 [AI Transpiling Tutorial](ai-transpiling-tutorial.ipynb)** - Complete walkthrough of the library's features and capabilities
- **📁 [Examples Directory](examples/)** - Collection of Jupyter notebooks demonstrating specific use cases:
- [AI Transpiler Demo](examples/ai-transpiler-demo.ipynb) - Basic transpilation examples
- [AI Clifford Synthesis Demo](examples/ai-clifford-synthesis-demo.ipynb) - Clifford circuit optimization
- [AI Linear Function Synthesis Demo](examples/ai-linear-function-synthesis-demo.ipynb) - Linear function synthesis
- [AI Permutation Synthesis Demo](examples/ai-permutation-synthesis-demo.ipynb) - Permutation circuit synthesis
- [AI Large Circuit Speed Test](examples/ai-large-circuit-speed-test.ipynb) - Performance benchmarking
These notebooks provide hands-on examples and detailed explanations to help you get the most out of the AI-powered transpilation capabilities.
### Quick Start
#### Using AI-powered Transpiler Passes Locally (Recommended)
**AI Routing Pass**
The `AIRouting` pass provides intelligent layout selection and circuit routing using reinforcement learning:
```python
from qiskit.transpiler import PassManager
from qiskit_ibm_transpiler.ai.routing import AIRouting
from qiskit.circuit.library import EfficientSU2
# Local mode execution
ai_routing = PassManager([
AIRouting(
backend_name="ibm_torino",
optimization_level=3,
layout_mode="optimize",
local_mode=True # Run locally for faster execution
)
])
circuit = EfficientSU2(101, entanglement="circular", reps=1).decompose()
routed_circuit = ai_routing.run(circuit)
```
##### Configuration Options
| Parameter | Options | Description |
|-----------|---------|-------------|
| `optimization_level` | 1, 2, 3 | Computational effort (higher = better results, longer time) |
| `layout_mode` | `optimize` | Best for general circuits (default) |
| | `improve` | Uses existing layout as starting point |
| | `keep` | Respects previous layout selection |
| `local_mode` | `True/False` | Run locally or on cloud |
**AI Circuit Synthesis Passes**
Optimize specific circuit blocks using AI-powered synthesis for superior gate count reduction:
```python
from qiskit.transpiler import PassManager
from qiskit_ibm_transpiler.ai.routing import AIRouting
from qiskit_ibm_transpiler.ai.synthesis import (
AILinearFunctionSynthesis, AIPauliNetworkSynthesis
)
from qiskit_ibm_transpiler.ai.collection import (
CollectLinearFunctions, CollectPauliNetworks
)
from qiskit.circuit.library import EfficientSU2
# Complete AI-powered transpilation pipeline
ai_pm = PassManager([
AIRouting(backend_name="ibm_torino", optimization_level=3, layout_mode="optimize"),
# Collect and synthesize linear functions
CollectLinearFunctions(),
AILinearFunctionSynthesis(backend_name="ibm_torino", local_mode=True),
# Collect and synthesize Pauli networks
CollectPauliNetworks(),
AIPauliNetworkSynthesis(backend_name="ibm_torino", local_mode=True),
])
circuit = EfficientSU2(10, entanglement="full", reps=1).decompose()
optimized_circuit = ai_pm.run(circuit)
```
**Available Synthesis Passes**
| Pass | Circuit Type | Max Qubits | Local Mode |
|------|-------------|------------|------------|
| `AICliffordSynthesis` | H, S, CX gates | 9 | ✅ |
| `AILinearFunctionSynthesis` | CX, SWAP gates | 9 | ✅ |
| `AIPermutationSynthesis` | SWAP gates | 65, 33, 27 | ✅ |
| `AIPauliNetworkSynthesis` | H, S, SX, CX, RX, RY, RZ | 6 | ✅ |
#### Using the Transpiler Service (Cloud)
> **Note**: The Qiskit Transpiler Service is currently being migrated. We recommend using local mode instead.
```python
from qiskit.circuit.library import EfficientSU2
from qiskit_ibm_transpiler.transpiler_service import TranspilerService
# Create your circuit
circuit = EfficientSU2(101, entanglement="circular", reps=1).decompose()
# Enable AI optimization for superior results
service = TranspilerService(
backend_name="ibm_torino",
ai="auto", # Service decides: AI passes vs standard Qiskit
optimization_level=3,
)
optimized_circuit = service.run(circuit)
```
**Service Configuration Options:**
| Parameter | Values | Description |
|-----------|--------|-------------|
| `ai` | `"true"`, `"false"`, `"auto"` | AI transpilation mode |
| `optimization_level` | `1`, `2`, `3` | Optimization intensity |
| `backend_name` | Backend string | Target quantum device |
| `coupling_map` | List of tuples | Custom connectivity |
**Service Limits**: Max 1M two-qubit gates per job, 30-minute transpilation timeout, 20-minute result retrieval window.
#### Hybrid Heuristic-AI Circuit Transpilation
The qiskit-ibm-transpiler allows you to configure a hybrid pass manager that automatically combines the best of Qiskit's heuristic and AI-powered transpiler passes. This feature behaves similarly to the Qiskit `generate_pass_manager` method:
```python
from qiskit_ibm_transpiler import generate_ai_pass_manager
from qiskit.circuit.library import efficient_su2
from qiskit_ibm_runtime import QiskitRuntimeService
backend = QiskitRuntimeService().backend("ibm_torino")
torino_coupling_map = backend.coupling_map
su2_circuit = efficient_su2(101, entanglement="circular", reps=1)
ai_hybrid_pass_manager = generate_ai_pass_manager(
coupling_map=torino_coupling_map,
ai_optimization_level=3,
optimization_level=3,
ai_layout_mode="optimize",
)
ai_su2_transpiled_circuit = ai_hybrid_pass_manager.run(su2_circuit)
```
**Configuration Options:**
- `coupling_map`: Specifies which coupling map to use for the transpilation
- `ai_optimization_level`: Level of optimization (1-3) for AI components of the PassManager
- `optimization_level`: Optimization level for heuristic components of the PassManager
- `ai_layout_mode`: How the AI routing handles layout (see AI routing pass section for options)
### Performance Tuning
**Thread Pool Configuration**:
```python
# Method 1: Per-pass configuration
AILinearFunctionSynthesis(backend_name="ibm_torino", max_threads=20)
# Method 2: Global environment variable
import os
os.environ["AI_TRANSPILER_MAX_THREADS"] = "20"
```
**Smart Replacement**:
- Default: Only replaces if synthesis improves gate count
- Force replacement: `replace_only_if_better=False`
> **Note**: Synthesis passes respect device coupling maps and work seamlessly after routing passes.
## 🔧 Advanced Configuration
### Logging
Customize logging levels for debugging and monitoring:
```python
import logging
# Available levels: NOTSET, DEBUG, INFO, WARNING, ERROR, CRITICAL
logging.getLogger("qiskit_ibm_transpiler").setLevel(logging.INFO)
```
## 📚 Resources & Support
- 📖 [Official Documentation](https://quantum.cloud.ibm.com/docs/guides/qiskit-transpiler-service)
- 🧠 [AI Transpiler Passes Guide](https://quantum.cloud.ibm.com/docs/guides/ai-transpiler-passes)
- 🎯 [IBM Quantum Platform](https://quantum.cloud.ibm.com/)
- 💡 [Give us feedback](https://qiskit.slack.com/archives/C06KF8YHUAU)
## 📄 Citation
If you use this library in your research, please cite:
```bibtex
@misc{kremer2024practical,
title={Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning},
author={David Kremer and Victor Villar and Hanhee Paik and Ivan Duran and Ismael Faro and Juan Cruz-Benito},
year={2024},
eprint={2405.13196},
archivePrefix={arXiv},
primaryClass={quant-ph}
}
```
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"description": "# qiskit_ibm_transpiler\n\n[](https://badge.fury.io/py/qiskit-ibm-transpiler)\n[](https://opensource.org/licenses/Apache-2.0)\n\nLeverage IBM Quantum's cutting-edge [Qiskit Transpiler Service](https://quantum.cloud.ibm.com/docs/guides/qiskit-transpiler-service) and [AI-powered transpiler passes](https://quantum.cloud.ibm.com/docs/guides/ai-transpiler-passes) to achieve superior circuit optimization through reinforcement learning algorithms.\n\n## \u2728 Key Features\n\n- \ud83e\udde0 **AI-Powered Optimization**: Advanced routing and circuit synthesis using reinforcement learning algorithms \n- \u26a1 **Local & Remote Modes**: Run AI passes locally or leverage cloud resources\n- \u2601\ufe0f **Cloud-ready**: Harness IBM Quantum's cloud infrastructure for intensive computations \n- \ud83c\udfaf **Drop-in Replacement**: Seamlessly integrate with existing Qiskit workflows\n- \ud83d\udcc8 **Superior Performance**: Our AI models typically outperform traditional heuristic algorithms. [Read the benchmark](https://arxiv.org/abs/2409.08844)\n\n**Note**: The cloud transpilation capabilities are only available for IBM Quantum Premium Plan users. The local mode is available to any user and is enabled by default if the local mode dependencies are installed. Currently in beta release.\n\n## \ud83d\udce6 Installation\n\n### Basic Installation\n```bash\npip install qiskit-ibm-transpiler\n```\n\n### With Local AI Mode Support\n\nFor running AI-powered transpiler passes locally:\n```bash\npip install qiskit-ibm-transpiler[ai-local-mode]\n```\n\n### \ud83d\udd10 Authentication\n\nThe package automatically authenticates using your [IBM Quantum Platform credentials](https://quantum.cloud.ibm.com/docs/en/guides/cloud-setup) aligned with how [Qiskit Runtime manages it](https://github.com/Qiskit/qiskit-ibm-runtime/tree/0.40.1?tab=readme-ov-file#qiskit-runtime-service-on-ibm-cloud):\n- Environment variable: `QISKIT_IBM_TOKEN`\n- Configuration file: `~/.qiskit/qiskit-ibm.json` (section: `default-ibm-quantum`)\n\n## \ud83d\ude80 Getting Started\n\n### Tutorial and Examples\n\nFor a comprehensive introduction to the qiskit-ibm-transpiler library, start here:\n\n- **\ud83d\udcd6 [AI Transpiling Tutorial](ai-transpiling-tutorial.ipynb)** - Complete walkthrough of the library's features and capabilities\n- **\ud83d\udcc1 [Examples Directory](examples/)** - Collection of Jupyter notebooks demonstrating specific use cases:\n - [AI Transpiler Demo](examples/ai-transpiler-demo.ipynb) - Basic transpilation examples\n - [AI Clifford Synthesis Demo](examples/ai-clifford-synthesis-demo.ipynb) - Clifford circuit optimization\n - [AI Linear Function Synthesis Demo](examples/ai-linear-function-synthesis-demo.ipynb) - Linear function synthesis\n - [AI Permutation Synthesis Demo](examples/ai-permutation-synthesis-demo.ipynb) - Permutation circuit synthesis\n - [AI Large Circuit Speed Test](examples/ai-large-circuit-speed-test.ipynb) - Performance benchmarking\n\nThese notebooks provide hands-on examples and detailed explanations to help you get the most out of the AI-powered transpilation capabilities.\n\n### Quick Start\n\n#### Using AI-powered Transpiler Passes Locally (Recommended)\n\n**AI Routing Pass**\n\nThe `AIRouting` pass provides intelligent layout selection and circuit routing using reinforcement learning:\n\n```python\nfrom qiskit.transpiler import PassManager\nfrom qiskit_ibm_transpiler.ai.routing import AIRouting\nfrom qiskit.circuit.library import EfficientSU2\n\n# Local mode execution\nai_routing = PassManager([\n AIRouting(\n backend_name=\"ibm_torino\", \n optimization_level=3, \n layout_mode=\"optimize\",\n local_mode=True # Run locally for faster execution\n )\n])\n\ncircuit = EfficientSU2(101, entanglement=\"circular\", reps=1).decompose()\nrouted_circuit = ai_routing.run(circuit)\n```\n\n##### Configuration Options\n\n| Parameter | Options | Description |\n|-----------|---------|-------------|\n| `optimization_level` | 1, 2, 3 | Computational effort (higher = better results, longer time) |\n| `layout_mode` | `optimize` | Best for general circuits (default) |\n| | `improve` | Uses existing layout as starting point |\n| | `keep` | Respects previous layout selection |\n| `local_mode` | `True/False` | Run locally or on cloud |\n\n**AI Circuit Synthesis Passes**\n\nOptimize specific circuit blocks using AI-powered synthesis for superior gate count reduction:\n\n```python\nfrom qiskit.transpiler import PassManager\nfrom qiskit_ibm_transpiler.ai.routing import AIRouting\nfrom qiskit_ibm_transpiler.ai.synthesis import (\n AILinearFunctionSynthesis, AIPauliNetworkSynthesis\n)\nfrom qiskit_ibm_transpiler.ai.collection import (\n CollectLinearFunctions, CollectPauliNetworks\n)\nfrom qiskit.circuit.library import EfficientSU2\n\n# Complete AI-powered transpilation pipeline\nai_pm = PassManager([\n AIRouting(backend_name=\"ibm_torino\", optimization_level=3, layout_mode=\"optimize\"),\n \n # Collect and synthesize linear functions\n CollectLinearFunctions(),\n AILinearFunctionSynthesis(backend_name=\"ibm_torino\", local_mode=True),\n \n # Collect and synthesize Pauli networks\n CollectPauliNetworks(),\n AIPauliNetworkSynthesis(backend_name=\"ibm_torino\", local_mode=True),\n])\n\ncircuit = EfficientSU2(10, entanglement=\"full\", reps=1).decompose()\noptimized_circuit = ai_pm.run(circuit)\n```\n\n**Available Synthesis Passes**\n\n| Pass | Circuit Type | Max Qubits | Local Mode |\n|------|-------------|------------|------------|\n| `AICliffordSynthesis` | H, S, CX gates | 9 | \u2705 |\n| `AILinearFunctionSynthesis` | CX, SWAP gates | 9 | \u2705 |\n| `AIPermutationSynthesis` | SWAP gates | 65, 33, 27 | \u2705 |\n| `AIPauliNetworkSynthesis` | H, S, SX, CX, RX, RY, RZ | 6 | \u2705 |\n\n\n#### Using the Transpiler Service (Cloud)\n\n> **Note**: The Qiskit Transpiler Service is currently being migrated. We recommend using local mode instead.\n\n```python\nfrom qiskit.circuit.library import EfficientSU2\nfrom qiskit_ibm_transpiler.transpiler_service import TranspilerService\n\n# Create your circuit\ncircuit = EfficientSU2(101, entanglement=\"circular\", reps=1).decompose()\n\n# Enable AI optimization for superior results\nservice = TranspilerService(\n backend_name=\"ibm_torino\",\n ai=\"auto\", # Service decides: AI passes vs standard Qiskit\n optimization_level=3,\n)\noptimized_circuit = service.run(circuit)\n```\n\n**Service Configuration Options:**\n\n| Parameter | Values | Description |\n|-----------|--------|-------------|\n| `ai` | `\"true\"`, `\"false\"`, `\"auto\"` | AI transpilation mode |\n| `optimization_level` | `1`, `2`, `3` | Optimization intensity |\n| `backend_name` | Backend string | Target quantum device |\n| `coupling_map` | List of tuples | Custom connectivity |\n\n**Service Limits**: Max 1M two-qubit gates per job, 30-minute transpilation timeout, 20-minute result retrieval window.\n\n#### Hybrid Heuristic-AI Circuit Transpilation\n\nThe qiskit-ibm-transpiler allows you to configure a hybrid pass manager that automatically combines the best of Qiskit's heuristic and AI-powered transpiler passes. This feature behaves similarly to the Qiskit `generate_pass_manager` method:\n\n```python\nfrom qiskit_ibm_transpiler import generate_ai_pass_manager\nfrom qiskit.circuit.library import efficient_su2\nfrom qiskit_ibm_runtime import QiskitRuntimeService\n\nbackend = QiskitRuntimeService().backend(\"ibm_torino\")\ntorino_coupling_map = backend.coupling_map\n\nsu2_circuit = efficient_su2(101, entanglement=\"circular\", reps=1)\n\nai_hybrid_pass_manager = generate_ai_pass_manager(\n coupling_map=torino_coupling_map,\n ai_optimization_level=3,\n optimization_level=3,\n ai_layout_mode=\"optimize\",\n)\n\nai_su2_transpiled_circuit = ai_hybrid_pass_manager.run(su2_circuit)\n```\n\n**Configuration Options:**\n- `coupling_map`: Specifies which coupling map to use for the transpilation\n- `ai_optimization_level`: Level of optimization (1-3) for AI components of the PassManager\n- `optimization_level`: Optimization level for heuristic components of the PassManager\n- `ai_layout_mode`: How the AI routing handles layout (see AI routing pass section for options)\n\n\n### Performance Tuning\n\n**Thread Pool Configuration**:\n```python\n# Method 1: Per-pass configuration\nAILinearFunctionSynthesis(backend_name=\"ibm_torino\", max_threads=20)\n\n# Method 2: Global environment variable\nimport os\nos.environ[\"AI_TRANSPILER_MAX_THREADS\"] = \"20\"\n```\n\n**Smart Replacement**:\n- Default: Only replaces if synthesis improves gate count\n- Force replacement: `replace_only_if_better=False`\n\n> **Note**: Synthesis passes respect device coupling maps and work seamlessly after routing passes.\n\n## \ud83d\udd27 Advanced Configuration\n\n### Logging\n\nCustomize logging levels for debugging and monitoring:\n\n```python\nimport logging\n\n# Available levels: NOTSET, DEBUG, INFO, WARNING, ERROR, CRITICAL\nlogging.getLogger(\"qiskit_ibm_transpiler\").setLevel(logging.INFO)\n```\n\n## \ud83d\udcda Resources & Support\n\n- \ud83d\udcd6 [Official Documentation](https://quantum.cloud.ibm.com/docs/guides/qiskit-transpiler-service)\n- \ud83e\udde0 [AI Transpiler Passes Guide](https://quantum.cloud.ibm.com/docs/guides/ai-transpiler-passes)\n- \ud83c\udfaf [IBM Quantum Platform](https://quantum.cloud.ibm.com/)\n- \ud83d\udca1 [Give us feedback](https://qiskit.slack.com/archives/C06KF8YHUAU)\n\n## \ud83d\udcc4 Citation\n\nIf you use this library in your research, please cite:\n\n```bibtex\n@misc{kremer2024practical,\n title={Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning},\n author={David Kremer and Victor Villar and Hanhee Paik and Ivan Duran and Ismael Faro and Juan Cruz-Benito},\n year={2024},\n eprint={2405.13196},\n archivePrefix={arXiv},\n primaryClass={quant-ph}\n}\n```\n",
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