quantrs2 0.1.2

High-performance quantum computing framework with ML, error mitigation, and annealing capabilities

QuantRS2-Py: Python Bindings for QuantRS2

QuantRS2-Py provides Python bindings for the QuantRS2 quantum computing framework, allowing Python users to access the high-performance Rust implementation with a user-friendly Python API.

Version 0.1.2 🎉

Production-Ready Stable Release! This release benefits from refined SciRS2 v0.1.2 integration and comprehensive platform support:

🖥️ Platform Support

• macOS Optimization: Native Apple Silicon and Intel Mac support with optimized performance
• CUDA/Linux Support: Full CUDA GPU acceleration on Linux platforms
• Cross-Platform Compatibility: Unified codebase supporting Windows, macOS, and Linux

🚀 SciRS2 Integration & Performance

• Enhanced Performance: SciRS2's parallel operations with automatic optimization
• SIMD Acceleration: Hardware-aware vectorized quantum operations
• GPU Computing: Complete GPU backend with CUDA support and memory optimization
• Memory Management: Advanced algorithms for 30+ qubit simulations
• Automatic Backend Selection: Intelligent selection based on problem characteristics

Features

Core Quantum Computing

• Seamless Python Integration: Easy-to-use Python interface for QuantRS2
• High Performance: Leverages Rust's performance while providing Python's usability
• Complete Gate Set: All quantum gates from the core library exposed to Python
• Simulator Access: Run circuits on state vector and other simulators
• GPU Acceleration: Optional GPU acceleration via feature flag
• PyO3-Based: Built using the robust PyO3 framework for Rust-Python interoperability

Advanced Features

🤖 Quantum Machine Learning Suite

• Autograd Quantum ML: Automatic differentiation for quantum machine learning
• Enhanced QGANs: Improved Quantum Generative Adversarial Networks
• Quantum CNNs: Quantum Convolutional Neural Networks implementation
• QNN Training: Parameter-shift rule gradients with adaptive learning rates
• VQE: Multi-ansatz support with hardware-efficient circuits
• QAOA: Quantum Approximate Optimization Algorithm with MaxCut examples
• Quantum PCA: Principal Component Analysis using quantum computing

🛠️ Developer Experience Tools

• Circuit Optimizer: Advanced quantum circuit optimization with ZX-calculus
• Tensor Network Optimization: High-performance tensor network simulations
• Performance Profiler: Comprehensive execution analysis and optimization recommendations
• Enhanced Testing: Expanded test coverage with GPU backend validation
• Resource Estimator: Advanced complexity and performance analysis

🏭 Production Features

• Dynamic Qubit Allocation: Runtime resource management with efficient memory usage
• Hardware Backend Integration: Support for IBM Quantum, Google Quantum AI, and AWS Braket
• Error Mitigation: Zero-noise extrapolation and other mitigation techniques
• Quantum Annealing: QUBO/Ising model optimization framework
• Cryptography Protocols: BB84, E91, and quantum signature implementations
• Comprehensive Examples: 50+ working examples demonstrating all features

Installation

From PyPI

pip install quantrs2

From Source (with GPU support)

pip install git+https://github.com/cool-japan/quantrs.git#subdirectory=py[gpu]

With Machine Learning Support

pip install quantrs2[ml]

Usage

Creating a Bell State

import quantrs2 as qr
import numpy as np

# Create a 2-qubit circuit
circuit = qr.PyCircuit(2)

# Build a Bell state
circuit.h(0)
circuit.cnot(0, 1)

# Run the simulation
result = circuit.run()

# Print the probabilities
probs = result.state_probabilities()
for state, prob in probs.items():
    print(f"|{state}⟩: {prob:.6f}")

Advanced Usage Examples

Quantum Machine Learning

Quantum Neural Network (QNN)

from quantrs2.ml import QNN

# Create a QNN with 4 qubits and 2 layers
qnn = QNN(n_qubits=4, n_layers=2)

# Train on quantum data
qnn.fit(X_train, y_train, epochs=100)

# Make predictions
predictions = qnn.predict(X_test)

Variational Quantum Eigensolver (VQE)

from quantrs2.algorithms import VQE
from quantrs2.optimizers import COBYLA

# Define a Hamiltonian
hamiltonian = qr.Hamiltonian.from_string("ZZ + 0.5*XI + 0.5*IX")

# Create VQE instance
vqe = VQE(hamiltonian, ansatz='ry', optimizer=COBYLA())

# Find ground state
result = vqe.run()
print(f"Ground state energy: {result.eigenvalue}")

Hardware Integration

from quantrs2.hardware import IBMQuantumBackend

# Connect to IBM Quantum
backend = IBMQuantumBackend(api_token="your_token")

# Create and execute circuit
circuit = qr.PyCircuit(5)
circuit.h(0)
circuit.cnot(0, 1)

# Execute on real hardware
job = backend.execute(circuit, shots=1024)
result = job.result()

Error Mitigation

from quantrs2.mitigation import ZeroNoiseExtrapolation

# Create a noisy circuit
circuit = qr.PyCircuit(3)
circuit.h(0)
circuit.cnot(0, 1)
circuit.cnot(1, 2)

# Apply zero-noise extrapolation
zne = ZeroNoiseExtrapolation(noise_factors=[1, 3, 5])
mitigated_result = zne.run(circuit)

Quantum Annealing

from quantrs2.anneal import QuboModel

# Define a QUBO problem
Q = {
    (0, 0): -1,
    (1, 1): -1,
    (0, 1): 2
}

# Create and solve
model = QuboModel(Q)
solution = model.solve(sampler='simulated_annealing')
print(f"Optimal solution: {solution.best_sample}")

GPU Acceleration

# Enable GPU acceleration for large circuits
circuit = qr.PyCircuit(20)
# Build your circuit...

# Run with GPU acceleration
result = circuit.run(use_gpu=True)

# Alternatively, check GPU availability
if qr.is_gpu_available():
    result = circuit.run(use_gpu=True)
else:
    result = circuit.run(use_gpu=False)

# Get results
probs = result.probabilities()

Advanced GPU Linear Algebra

from quantrs2.gpu import GPUBackend, GPULinearAlgebra

# Initialize GPU backend
gpu_backend = GPUBackend()

# Create GPU-accelerated linear algebra operations
gpu_linalg = GPULinearAlgebra(gpu_backend)

# Perform high-performance quantum state operations
large_state = qr.create_quantum_state(25)  # 25-qubit state
optimized_state = gpu_linalg.optimize_state(large_state)

# GPU-accelerated tensor network contractions
tensor_result = gpu_linalg.contract_tensor_network(quantum_circuit)

Tensor Network Optimization

from quantrs2.tensor_networks import TensorNetworkOptimizer

# Create and optimize tensor networks
optimizer = TensorNetworkOptimizer()

# Build a complex quantum circuit
circuit = qr.PyCircuit(30)
# Add many gates...

# Optimize using tensor network techniques
optimized_circuit = optimizer.optimize(circuit)
result = optimized_circuit.run()

API Reference

Core Classes

• PyCircuit: Main circuit building and execution
• PySimulationResult: Results from quantum simulations

Module Structure

Machine Learning (quantrs2.ml)

• QNN: Quantum Neural Networks with gradient computation
• VQE: Variational Quantum Eigensolver with multiple ansätze
• QuantumGAN: Quantum Generative Adversarial Networks
• HEPClassifier: High-Energy Physics quantum classifier

Dynamic Allocation (quantrs2.dynamic_allocation)

• QubitAllocator: Runtime qubit resource management
• DynamicCircuit: Thread-safe dynamic circuit construction
• AllocationStrategy: Multiple allocation optimization strategies

Advanced Algorithms (quantrs2.advanced_algorithms)

• AdvancedVQE: Enhanced VQE with multiple optimization methods
• EnhancedQAOA: Advanced QAOA with sophisticated optimization
• QuantumWalk: Comprehensive quantum walk implementations
• QuantumErrorCorrection: Error correction protocol suite

Hardware Backends (quantrs2.hardware_backends)

• HardwareBackendManager: Multi-provider backend management
• IBMQuantumBackend: IBM Quantum integration
• GoogleQuantumBackend: Google Quantum AI integration
• AWSBraketBackend: AWS Braket integration

Enhanced Compatibility

• enhanced_qiskit_compatibility: Advanced Qiskit integration
• enhanced_pennylane_plugin: Comprehensive PennyLane integration

Error Mitigation (quantrs2.mitigation)

• ZeroNoiseExtrapolation: Advanced ZNE implementation
• Observable: Quantum observables with enhanced measurement
• CircuitFolding: Sophisticated noise scaling utilities

Quantum Annealing (quantrs2.anneal)

• QuboModel: Advanced QUBO problem formulation
• IsingModel: Enhanced Ising model optimization
• PenaltyOptimizer: Sophisticated constrained optimization

Performance

QuantRS2-Py v0.1.2 delivers exceptional performance for production quantum computing:

Simulation Capabilities

• Large-Scale Simulation: Efficiently simulates 30+ qubits on standard hardware
• GPU Acceleration: Complete GPU backend with CUDA support for massive speedups
• Memory Optimization: Advanced SciRS2-powered algorithms for efficient memory usage
• SIMD Vectorization: Hardware-aware vectorized operations on all platforms

Platform Optimization

• macOS Native: Optimized for Apple Silicon and Intel Macs
• CUDA/Linux: Full GPU acceleration on Linux with CUDA support
• Cross-Platform: Consistent performance across Windows, macOS, and Linux
• Automatic Detection: Smart hardware capability detection and optimization

Advanced Features

• Parallel Execution: Automatic parallelization via SciRS2 parallel operations
• Tensor Networks: High-performance tensor network contractions
• Circuit Optimization: Automatic quantum circuit optimization
• Backend Selection: Intelligent backend selection based on problem characteristics

Requirements

Basic Requirements

• Python 3.8 or higher
• NumPy >= 1.20.0
• Matplotlib >= 3.3.0 (for visualization)
• IPython >= 7.0.0 (for interactive features)

Optional Dependencies

• GPU Support: CUDA toolkit 11.0+ for GPU acceleration on Linux
• Machine Learning: scikit-learn >= 1.0.0, scipy >= 1.7.0 (install with pip install quantrs2[ml])
• Development: pytest, black, flake8 (install with pip install quantrs2[dev])

Platform Specific

• macOS: Optimized for macOS 10.15+ (both Intel and Apple Silicon)
• Linux: CUDA support requires compatible NVIDIA drivers
• Windows: Full feature support with optional GPU acceleration

Contributing

We welcome contributions! Please see our contributing guidelines for more information.

License

This project is licensed under either:

• Apache License, Version 2.0, (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.