# QuantRS2-Sim Development Roadmap

This document outlines the development plans and future tasks for the QuantRS2-Sim module.

## Current Status

### Completed Features

- ✅ Basic state vector simulator implementation
- ✅ Support for all standard gates
- ✅ Parallel execution using Rayon
- ✅ Memory-efficient implementation for large qubit counts
- ✅ Multiple optimized backends using different strategies
- ✅ SIMD-based optimizations for key operations
- ✅ Initial noise models (bit flip, phase flip, depolarizing)
- ✅ Basic tensor network implementation
- ✅ Basic benchmark utilities

### In Progress

- 🔄 Enhanced GPU acceleration for state vector simulation
- 🔄 Advanced noise models for realistic hardware simulation
- 🔄 Tensor network contraction optimization
- 🔄 Advanced quantum error correction codes

## Planned Enhancements

### Near-term (v0.1.x)

- [ ] Complete the noise model implementations for all common noise channels
- [ ] Finalize GPU shader optimizations
- [ ] Add automated backend selection based on circuit characteristics
- [ ] Implement T1/T2 relaxation models for IBM-like hardware
- [ ] Add extensive benchmarking suite with visualization
- [ ] Optimize tensor network contraction paths
- [ ] Complete documentation with performance guidelines
- [ ] Implement quantum error correction (bit flip code, phase flip code, Shor code)

### Medium-term (v0.2.x)

- [ ] Add adaptive algorithms for large qubit simulation
- [ ] Implement specialized simulators for specific circuit classes
- [ ] Add visualization of quantum states and simulation dynamics
- [ ] Integrate with scientific computing libraries for advanced analysis
- [ ] Implement approximate simulation techniques for very large circuits
- [ ] Add support for multi-GPU acceleration
- [ ] Implement more advanced error correction techniques (surface codes)
- [ ] Add noise-aware circuit optimization

### Long-term (Future Versions)

- [ ] Implement full density matrix simulation
- [ ] Add support for continuous variable quantum computing
- [ ] Implement quantum machine learning extensions
- [ ] Add distributed simulation across multiple machines
- [ ] Support for specialized quantum processor architectures
- [ ] Implement hardware-accelerated tensor network contraction
- [ ] Add advanced visualization and interactive simulation tools

## Implementation Notes

- The GPU implementation needs shader optimization for better performance
- Consider replacing ndarray with custom SIMD-optimized linear algebra for core operations
- Tensor network implementation should leverage better contraction path optimization
- Consider implementing a hybrid classical-quantum simulator for VQE-like algorithms

## Known Issues

- Memory usage can be prohibitive for large qubit counts (> 25) with state vector simulation
- GPU implementation has platform-specific issues on some systems
- Tensor network simulator needs better support for arbitrary circuit topologies
- Some optimized implementations are still being debugged

## Integration Tasks

- [ ] Enhance integration with device-specific simulation capabilities
- [ ] Improve support for running on cloud/cluster computing resources
- [ ] Add hooks for custom gate implementations to improve performance