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QuantumForge: Advanced Quantum Circuit Design and Optimization Framework

QuantumForge is a comprehensive quantum circuit design and optimization framework that combines reinforcement learning, circuit cutting, error mitigation, and dynamic compilation to create optimal quantum circuits.

Features

Core Circuit Design

  • Support for both Qiskit and Cirq backends
  • Gymnasium-compatible environment
  • Advanced action space including multi-qubit gates
  • Realistic noise simulation
  • Reinforcement learning integration with Stable Baselines3
  • Hyperparameter optimization using Optuna
  • Circuit optimization and visualization

Circuit Optimization

  • Template-based optimization
  • Quantum Shannon Decomposition
  • Gate commutation analysis
  • Multi-objective optimization
  • Hardware-aware optimization
  • Resource estimation
  • Circuit equivalence verification

Circuit Cutting

  • Intelligent cut-point selection
  • Dependency graph analysis
  • Balanced subcircuit generation
  • Entanglement cost minimization
  • Automated qubit remapping
  • Result reconstruction
  • Cutting visualization

Error Mitigation

  • Zero-noise extrapolation
  • Probabilistic error cancellation
  • Measurement error mitigation
  • Noise characterization
  • Confidence interval calculation
  • Error analysis visualization
  • Fidelity improvement tracking

Dynamic Compilation

  • Runtime optimization with caching
  • Pulse-level optimization
  • Hardware-specific compilation
  • Automated gate decomposition
  • Timing constraint optimization
  • Qubit mapping optimization
  • Compilation analysis tools

Installation

  1. Clone this repository
  2. Install the required dependencies: 
    pip install -r requirements.txt

Usage

Basic Example

See example.py for basic usage demonstrating:

  • Training a PPO agent on the QuantumForge environment
  • Evaluating the trained agent
  • Visualizing rewards and quantum states
python example.py

Advanced Examples

Hardware-Aware Optimization

See hardware_aware_example.py for:

  • Hardware constraint integration
  • Noise-aware optimization
  • Connectivity optimization
python hardware_aware_example.py

Circuit Cutting

See circuit_cutting_example.py for:

  • Large circuit decomposition
  • Subcircuit execution
  • Result reconstruction
python circuit_cutting_example.py

Error Mitigation

See advanced_examples.py for:

  • Error mitigation techniques
  • Dynamic compilation
  • Advanced optimization strategies
python advanced_examples.py

Components

Environment (QuantumForgeEnv)

  • Observations: Current quantum state as complex vector
  • Actions: (operation, qubit1, qubit2, parameter)
    • Operations: X, Z, H, RY, CNOT, CZ, RXX, RZZ, etc.
    • Qubits: Indices for operation application
    • Parameters: Used for parameterized gates

Circuit Optimizer

  • Gate sequence optimization
  • Template matching
  • Quantum Shannon Decomposition
  • Resource estimation
  • Circuit equivalence checking

Circuit Cutter

  • Dependency analysis
  • Cut-point selection
  • Subcircuit generation
  • Result reconstruction
  • Performance analysis

Error Mitigator

  • Multiple mitigation strategies
  • Noise characterization
  • Error analysis
  • Result improvement tracking

Dynamic Compiler

  • Runtime optimization
  • Pulse-level control
  • Hardware adaptation
  • Performance analysis
  • Visualization tools

Backends

QuantumForge supports two quantum computing backends:

  1. Qiskit: IBM's quantum computing framework

    • Full noise simulation
    • Hardware-specific optimization
    • Pulse-level control

  2. Cirq: Google's quantum computing framework

    • Noise modeling
    • Device specification
    • Custom gate sets

Visualization

The framework generates various visualizations:

  • Reward plots
  • Quantum state visualizations
  • Circuit cut diagrams
  • Error mitigation results
  • Compilation analysis
  • Resource usage comparisons

Contributing

Contributions are welcome! Please feel free to submit pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use QuantumForge in your research, please cite:

@software{quantumforge2024,
  title = {QuantumForge: Advanced Quantum Circuit Design and Optimization Framework},
  year = {2024},
  author = {Moudather Chelbi},
  url = {https://github.com/vinerya/QuantumForge}
}