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}
}