# 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
- ✅ GPU compute shader framework with wgpu
- ✅ Advanced noise models (amplitude damping, thermal relaxation)
- ✅ Dynamic qubit allocation support
- ✅ Enhanced state vector with lazy evaluation
- ✅ Linear algebra operations module

### In Progress

- 🔄 SciRS2 integration for advanced numerical algorithms
- 🔄 Distributed simulation across multiple nodes
- 🔄 Quantum error mitigation techniques
- 🔄 Hardware-aware simulation optimization

## Planned Enhancements

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

- [ ] Integrate SciRS2 sparse matrix operations for large circuits
- [ ] Implement Trotter-Suzuki decomposition using SciRS2
- [ ] Add quantum Monte Carlo simulation with SciRS2 RNG
- [ ] Create adaptive precision control for state vectors
- [ ] Implement gate fusion using SciRS2 matrix multiplication
- [ ] Add support for Pauli string evolution
- [ ] Create stabilizer simulator for Clifford circuits
- [ ] Implement matrix product state (MPS) simulator
- [ ] Add support for open quantum system simulation
- [ ] Create shot-based sampling with statistical analysis

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

- [ ] Implement decision diagram simulator using SciRS2 graphs
- [ ] Add Feynman path integral simulation method
- [ ] Create quantum supremacy verification algorithms
- [ ] Implement cross-entropy benchmarking
- [ ] Add support for fermionic simulation with SciRS2
- [ ] Create quantum algorithm debugger interface
- [ ] Implement automatic differentiation for VQE
- [ ] Add support for photonic simulation
- [ ] Create noise extrapolation techniques
- [ ] Implement quantum volume calculation

### Long-term (Future Versions)

- [ ] Implement quantum cellular automata simulation
- [ ] Add support for topological quantum simulation
- [ ] Create quantum field theory simulators
- [ ] Implement lattice gauge theory simulation
- [ ] Add support for quantum chemistry DMRG
- [ ] Create quantum gravity simulation tools
- [ ] Implement holographic quantum error correction
- [ ] Add support for quantum machine learning layers
- [ ] Create quantum-inspired classical algorithms
- [ ] Implement quantum reservoir computing

## Implementation Notes

### Performance Optimization
- Use SciRS2 BLAS Level 3 operations for matrix multiplication
- Implement cache-oblivious algorithms for state vector updates
- Use thread-local storage for parallel simulations
- Implement vectorized operations for Pauli measurements
- Create memory pools for temporary allocations

### Technical Architecture
- State vectors stored in interleaved complex format
- Use lazy evaluation for gate sequences
- Implement just-in-time compilation for circuits
- Support both row-major and column-major layouts
- Create pluggable backend system for simulators

### SciRS2 Integration Points
- Linear algebra: Use SciRS2 BLAS/LAPACK bindings
- Sparse operations: Leverage SciRS2 sparse matrices
- Optimization: Use SciRS2 optimization algorithms
- Statistics: Integrate SciRS2 for result analysis
- Parallel computing: Use SciRS2 parallel primitives

## 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

### SciRS2 Integration
- [ ] Replace custom linear algebra with SciRS2 routines
- [ ] Use SciRS2 FFT for quantum Fourier transform
- [ ] Integrate SciRS2 sparse solvers for large systems
- [ ] Leverage SciRS2 eigensolvers for spectral analysis
- [ ] Use SciRS2 optimization for variational algorithms

### Hardware Integration
- [ ] Create CUDA kernels using SciRS2 GPU support
- [ ] Implement OpenCL backend for AMD GPUs
- [ ] Add support for TPU acceleration
- [ ] Create FPGA-optimized implementations
- [ ] Integrate with quantum cloud services

### Module Integration
- [ ] Create efficient interfaces with circuit module
- [ ] Add support for device noise models
- [ ] Implement ML module integration for QML
- [ ] Create visualization hooks for debugging
- [ ] Add telemetry for performance monitoring