GraphQOMB

GraphQOMB (Qompiler for Measurement-Based Quantum Computing, pronounce as graphcomb) is a modular graph-based compiler for measurement-based quantum computing (MBQC), providing a high-level interface to the graphix package for applications such as fault-tolerant MBQC.

GraphQOMB's philosophy is to use graph-like ZX diagrams as MBQC representation, with feedforward strategy treated independently. This allows, for example, to integrate Pauli frame tracking needed for fault-tolerant MBQC, into basic feedforward strategy of MBQC, allowing streamlined compilation.

Features

Computation Design

• ZX-Calculus Integration: Use ZX-diagrams as an abstract expression of measurement pattern
• Feedforward Strategy Design: Our library accepts general feedforward strategy and optimization, eliminating the necessity of measurement calculus
• Scheduler: Scheduling the node preparation and measurement time

Compilation

• MBQC Pattern Generation: Measurement pattern is treated as a quantum assembly
• Pauli Frame Tracking: Manage all the classical feedforward with Pauli Frame, enabling fault-tolerant computing as well

Simulation

• Pattern Simulation: Simulate measurement patterns with statevector backend
• Simulation in Stim circuit: Generate a stim circuit implementing a fault-tolerant MBQC

Others

• Transpilation into Graphix Pattern: Transpile generated pattern into graphix.pattern.Pattern object for variety of execution backend (WIP)
• Visualization: Visualize graph states

Installation

From PyPI (Recommended)

pip install graphqomb

From Source (Development)

git clone https://github.com/TeamGraphix/graphqomb.git
cd graphqomb/
pip install -e .

Install with development dependencies:

pip install -e .[dev]

Install with documentation dependencies:

pip install -e .[doc]

Quick Start

Prepare Resource State and Feedforward

from graphqomb.circuit import Circuit, circuit2graph
from graphqomb.gates import H, CNOT
from graphqomb.qompiler import qompile
from graphqomb.simulator import PatternSimulator, SimulatorBackend

# Create a quantum circuit
circuit = Circuit(2)
circuit.apply_macro_gate(H(0))
circuit.apply_macro_gate(CNOT((0, 1)))

graph, feedforward = circuit2graph(circuit)

# Compile into pattern
pattern = qompile(graph, feedforward)

# Simulate the pattern
simulator = PatternSimulator(pattern, SimulatorBackend.StateVector)
simulator.simulate()
print(simulator.state)

Creating and Visualizing Graph States

from graphqomb.graphstate import GraphState
from graphqomb.visualizer import visualize

# Create a graph state using from_graph
graph, node_map = GraphState.from_graph(
    nodes=["input", "middle", "output"],
    edges=[("input", "middle"), ("middle", "output")],
    inputs=["input"],
    outputs=["output"]
)

# Visualize the graph
visualize(graph)

Documentation

• Tutorial: [WIP] for detailed usage guides
• Examples: See examples for code demonstrations
• API Reference: Full API documentation is available here

Development

Running Tests

pytest                              # Run all tests
pytest tests/test_specific.py       # Run specific test file

Code Quality

ruff check                          # Lint code
ruff format                         # Format code
mypy                                # Type checking
pyright                             # Type checking

Building Documentation

cd docs/
make html                           # Build HTML documentation
# Output will be in docs/build/html/

Project Structure

graphqomb/
├── graphqomb/               # Main source code
│   ├── circuit.py           # Quantum circuit implementation
│   ├── graphstate.py        # Graph state manipulation
|   ├── scheduler.py         # Scheduling computaional order
│   ├── qompiler.py          # Generate MBQC pattern
│   ├── simulator.py         # Pattern simulation
│   ├── visualizer.py        # Visualization tools
│   └── ...
├── tests/                   # Test suite
├── examples/                # Example scripts
├── docs/                    # Sphinx documentation
│   └── source/
│       ├── gallery/         # Example gallery
│       └── ...
└── pyproject.toml          # Project configuration

Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Ensure all tests pass and code is properly formatted
5. Submit a pull request

Related Projects

• graphix: The original MBQC library
• PyZX: ZX-calculus library for Python
• swiflow: Rust-based fast flow finding algorithms

License

MIT License

Citation

If you use GraphQOMB in your research, please cite:

@software{graphqomb,
  title = {GraphQOMB: A Modular Graph State Qompiler for Measurement-Based Quantum Computation},
  author = {Masato Fukushima, Sora Shiratani, Yuki Watanabe, and Daichi Sasaki},
  year = {2025},
  url = {https://github.com/TeamGraphix/graphqomb}
}

Acknowledgements

We acknowledge the NICT Quantum Camp for supporting our development.

Special thanks to Fixstars Amplify: