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quantrs2-sim-0.1.0-alpha.4
QuantRS2-Sim: Advanced Quantum Simulation Suite
QuantRS2-Sim is the comprehensive simulation engine of the QuantRS2 quantum computing framework, providing state-of-the-art quantum simulation algorithms, error correction codes, and performance optimization techniques for simulating quantum systems up to 30+ qubits on standard hardware.
Core Features
Multi-Backend Simulation Architecture
- State Vector Simulators: Classical dense state vector simulation with SIMD acceleration
- Matrix Product States (MPS): Memory-efficient simulation for low-entanglement circuits
- Stabilizer Simulators: Exponentially fast simulation for Clifford circuits
- Decision Diagram (DD): Symbolic simulation using quantum decision diagrams
- Tensor Network Simulators: Advanced contraction algorithms for arbitrary network topologies
- Path Integral Methods: Feynman path integral approach for quantum evolution
Advanced Quantum Simulation
- Quantum Monte Carlo: Variational (VMC), diffusion (DMC), and path integral (PIMC) methods
- Trotter-Suzuki Decomposition: Time evolution of quantum systems with optimized decompositions
- Automatic Differentiation VQE: Gradient-based optimization for variational quantum eigensolvers
- Photonic Quantum Computing: Continuous variable and discrete photonic system simulation
- Fermionic Simulation: Second quantization with Jordan-Wigner and Bravyi-Kitaev transformations
- Open Quantum Systems: Lindblad master equation integration for realistic quantum dynamics
Error Correction & Fault Tolerance
- Quantum Error Correction: Surface, color, and concatenated codes
- Noise Models: Comprehensive realistic noise modeling including correlated errors
- Error Mitigation: Zero-noise extrapolation, virtual distillation, and symmetry verification
- Process Tomography: Quantum channel characterization and benchmarking
- Quantum Volume: IBM's quantum volume protocol
Performance & Verification
- Quantum Supremacy: Cross-entropy benchmarking and Porter-Thomas verification
- Hardware Optimization: SIMD acceleration, memory-efficient algorithms, and GPU computing
- SciRS2 Integration: Advanced linear algebra operations using optimized BLAS/LAPACK
- Quantum Debugging: Interactive debugging tools with breakpoints and state inspection
- Performance Profiling: Comprehensive benchmarking and optimization analysis
Usage Examples
Basic State Vector Simulation
use *;
Matrix Product State (MPS) Simulation
use *;
Stabilizer Simulation for Clifford Circuits
use *;
Quantum Monte Carlo Simulation
use *;
Automatic Differentiation VQE
use *;
Noise Simulation and Error Mitigation
use *;
Quantum Supremacy Verification
use *;
Photonic Quantum Computing
use *;
Comprehensive Module Structure
Core Simulation Engines
- statevector.rs: Dense state vector simulation with SIMD optimizations
- enhanced_statevector.rs: Enhanced state vector with lazy evaluation and memory optimization
- mps_simulator.rs: Basic matrix product state simulator
- mps_basic.rs: Lightweight MPS simulator for low-entanglement circuits
- mps_enhanced.rs: Advanced MPS with optimized contraction algorithms
- stabilizer.rs: Stabilizer formalism for exponentially fast Clifford simulation
- clifford_sparse.rs: Sparse representation of Clifford operations
- decision_diagram.rs: Quantum decision diagram symbolic simulation
Advanced Simulation Methods
- qmc.rs: Quantum Monte Carlo methods (VMC, DMC, PIMC)
- path_integral.rs: Feynman path integral simulation techniques
- trotter.rs: Trotter-Suzuki decomposition for time evolution
- photonic.rs: Photonic quantum computing simulation (continuous variables)
- fermionic_simulation.rs: Second quantization with Jordan-Wigner transforms
- open_quantum_systems.rs: Lindblad master equation integration
Error Correction & Noise
- error_correction/: Comprehensive quantum error correction codes
- codes.rs: Surface, color, and concatenated codes
- mod.rs: Error correction framework and utilities
- noise.rs: Basic noise models (bit-flip, phase-flip, depolarizing)
- noise_advanced.rs: Realistic device noise models with correlations
- noise_extrapolation.rs: Zero-noise extrapolation and error mitigation
Optimization & Performance
- optimized_simd.rs: SIMD-accelerated quantum operations
- optimized_chunked.rs: Memory-efficient chunked processing
- optimized_simple.rs: Simplified optimized simulators
- specialized_gates.rs: Hardware-optimized gates
- specialized_simulator.rs: Simulator with specialized gate optimizations
- fusion.rs: Gate fusion optimization for reduced circuit depth
- precision.rs: Adaptive precision control for state vectors
Variational & Machine Learning
- autodiff_vqe.rs: Automatic differentiation for variational quantum eigensolvers
- pauli.rs: Pauli string operations and expectation value computation
Hardware Integration
- gpu.rs: GPU-accelerated simulation using WGPU compute shaders
- gpu_linalg.rs: GPU linear algebra operations for quantum simulation
- scirs2_integration.rs: SciRS2 backend integration for high-performance computing
- scirs2_qft.rs: SciRS2-accelerated quantum Fourier transform
- scirs2_sparse.rs: Sparse matrix operations using SciRS2
- scirs2_eigensolvers.rs: Spectral analysis and eigenvalue computations
Verification & Benchmarking
- quantum_supremacy.rs: Cross-entropy benchmarking and Porter-Thomas verification
- quantum_volume.rs: IBM quantum volume protocol
- benchmark.rs: Performance benchmarking across different simulation methods
- debugger.rs: Interactive quantum debugging with breakpoints and state inspection
Utility & Analysis
- shot_sampling.rs: Statistical sampling and measurement simulation
- sparse.rs: Sparse matrix representations for large quantum systems
- linalg_ops.rs: Linear algebra operations optimized for quantum simulation
- utils.rs: Common utilities and helper functions
- tensor.rs: Tensor manipulation utilities
- tensor_network/: Advanced tensor network contraction algorithms
- contraction.rs: Optimal contraction order algorithms
- opt_contraction.rs: Optimized contraction algorithms
- tensor.rs: Tensor data structures and operations
- dynamic.rs: Dynamic qubit allocation and circuit optimization
Feature Flags
- default: Optimized simulators with SIMD acceleration
- gpu: GPU acceleration using WGPU compute shaders (CUDA/OpenCL/Vulkan)
- simd: Platform-specific SIMD instructions for vectorized operations
- optimize: Advanced optimization algorithms and memory management
- memory_efficient: Large state vector optimizations for 25+ qubit simulation
- advanced_math: SciRS2 integration with optimized BLAS/LAPACK operations
- mps: Matrix Product State simulation with linear algebra support
Performance Characteristics
Simulation Capabilities
- State Vector: Up to 30+ qubits on standard hardware (16GB RAM)
- MPS: 50+ qubits for low-entanglement circuits with bond dimension control
- Stabilizer: Unlimited qubits for Clifford circuits (exponential speedup)
- Decision Diagram: Symbolic simulation with compact representations
- GPU Acceleration: 10-100x speedups for 20+ qubit circuits on compatible hardware
Optimization Features
- SIMD Vectorization: AVX2/AVX-512 support for parallel complex arithmetic
- Multi-threading: Rayon-based parallelization across CPU cores
- Memory Efficiency: Chunked processing and lazy evaluation for large circuits
- Gate Specialization: Hardware-optimized processing for common gates
- SciRS2 Integration: Professional-grade linear algebra with Intel MKL/OpenBLAS
Benchmarking Results
- Single-qubit gates: ~10ns per operation (SIMD optimized)
- Two-qubit gates: ~50ns per operation (depending on entanglement)
- GPU acceleration: 20-50x speedup for 25+ qubit state vector simulation
- MPS simulation: 100x memory reduction for product states
Advanced Simulation Features
Quantum Algorithm Support
- VQE: Automatic differentiation with gradient-based optimization
- QAOA: Optimized Hamiltonian evolution with Trotter decomposition
- Quantum Monte Carlo: Ground state estimation with statistical analysis
- Quantum Supremacy: Cross-entropy benchmarking with statistical verification
Error Correction & Mitigation
- Surface Codes: Topological error correction with syndrome decoding
- Zero-Noise Extrapolation: Richardson extrapolation for error mitigation
- Virtual Distillation: Quantum error mitigation using symmetry verification
- Process Tomography: Complete characterization of quantum channels
Hardware-Aware Simulation
- Device Noise Models: Realistic simulation matching hardware specifications
- Calibration Integration: Real-time hardware parameter updates
- Cross-Platform GPU: WGPU backend supporting CUDA, OpenCL, and Vulkan
- Memory Hierarchy: Cache-aware algorithms for optimal performance
Integration with QuantRS2 Ecosystem
Core Module Integration
- Leverages advanced gate decomposition algorithms for simulation optimization
- Uses optimized matrix representations from core for maximum performance
- Integrates quantum error correction codes for fault-tolerant simulation
Circuit Module Integration
- Simulates circuits with automatic optimization pass selection
- Supports circuit compilation with hardware-aware gate translation
- Provides feedback for circuit optimization based on simulation performance
Device Module Integration
- Accurately simulates real quantum hardware with calibrated noise models
- Provides device characterization through quantum volume and benchmarking
- Supports cloud quantum computer simulation with realistic latency models
Machine Learning Module Integration
- Optimized simulation for variational quantum algorithms
- Automatic differentiation support for quantum neural network training
- Quantum kernel methods with efficient expectation value computation
Research & Development Applications
Quantum Computing Research
- Novel algorithm development with comprehensive simulation backends
- Quantum advantage verification with statistical confidence intervals
- Error correction threshold estimation with realistic noise models
Quantum Chemistry & Physics
- Molecular simulation using fermionic operators and transformations
- Condensed matter physics with many-body quantum systems
- Quantum field theory simulation using path integral methods
Quantum Machine Learning
- Quantum neural network training with automatic differentiation
- Quantum kernel methods for classical machine learning
- Variational quantum algorithm optimization and benchmarking
License
This project is licensed under either:
at your option.