QuantRS2-Anneal: Comprehensive Quantum Annealing Framework

QuantRS2-Anneal is the premier quantum annealing module of the QuantRS2 quantum computing framework, providing comprehensive support for quantum annealing, optimization problems, and quantum-inspired algorithms.

Version 0.1.0-beta.2

This release features:

• Stable APIs for D-Wave, AWS Braket, and Fujitsu integrations
• Enhanced performance using SciRS2 v0.1.0-beta.3's parallel algorithms for large-scale optimization
• Improved minor graph embedding with refined SciRS2 graph algorithms
• Memory-efficient sparse matrix operations via SciRS2

🚀 Quick Start

use quantrs2_anneal::{
    ising::IsingModel,
    simulator::{ClassicalAnnealingSimulator, AnnealingParams}
};

// Create and solve a simple Max-Cut problem
let mut model = IsingModel::new(4);
model.set_coupling(0, 1, -1.0)?;
model.set_coupling(1, 2, -1.0)?;
model.set_coupling(2, 3, -1.0)?;
model.set_coupling(3, 0, -1.0)?;

let simulator = ClassicalAnnealingSimulator::new(AnnealingParams::default())?;
let result = simulator.solve(&model)?;

println!("Best energy: {}", result.best_energy);
println!("Solution: {:?}", result.best_spins);

🌟 Key Features

Core Problem Formulations

• 🔗 Ising Models: Complete sparse matrix support with biases and couplings
• 🎯 QUBO Formulations: Quadratic Unconstrained Binary Optimization with constraint handling
• 🏗️ Problem Builder: Intuitive DSL for complex optimization problem construction
• 🔄 Format Conversion: Seamless conversion between Ising and QUBO representations

Quantum Annealing Simulators

• 🌡️ Classical Annealing: Simulated annealing with configurable temperature schedules
• 🌊 Path Integral Monte Carlo: Quantum annealing simulation with tunneling effects
• 👥 Population Annealing: Parallel annealing with population-based sampling
• 🎪 Coherent Ising Machine: Quantum-inspired optimization using coherent states
• 📊 Non-Stoquastic Simulators: Support for non-stoquastic Hamiltonians

Cloud Quantum Hardware Integration

• 🌐 D-Wave Leap: Comprehensive client with auto-embedding and advanced parameters
• ☁️ AWS Braket: Full integration with cost tracking and device optimization
• 🗾 Fujitsu DAU: Digital annealer unit integration for hybrid optimization
• 🤖 Hybrid Solvers: Classical-quantum hybrid algorithms for large-scale problems

Advanced Algorithms and Techniques

• 🗺️ Graph Embedding: Minor graph embedding with optimization and layout algorithms
• ⚖️ Penalty Optimization: Automatic constraint penalty weight optimization
• 🔄 Reverse Annealing: Advanced annealing schedules starting from known solutions
• 🌈 Flux Bias Optimization: Per-qubit flux bias tuning for enhanced performance
• 🌟 QAOA Integration: Bridge to variational quantum algorithms via circuit module

Specialized Applications

• ⚡ Energy Systems: Power grid optimization, smart grid scheduling, renewable integration
• 💰 Finance: Portfolio optimization, risk management, algorithmic trading
• 🏥 Healthcare: Drug discovery, treatment planning, resource allocation
• 📦 Logistics: Vehicle routing, supply chain optimization, warehouse management
• 🏭 Manufacturing: Production scheduling, quality control, resource allocation
• 📡 Telecommunications: Network optimization, spectrum allocation, routing protocols

Visualization and Analysis

• 📈 Energy Landscapes: Interactive visualization of optimization energy surfaces
• 📊 Performance Analytics: Comprehensive metrics, timing analysis, and benchmarking
• 🎨 Solution Visualization: Graphical representation of optimization solutions
• 📉 Convergence Analysis: Real-time monitoring of annealing convergence behavior

📦 Installation

Add to your Cargo.toml:

[dependencies]
quantrs2-anneal = "0.1.0-beta.3"

# Optional features for cloud integration
quantrs2-anneal = { version = "0.1.0-beta.3", features = ["dwave", "braket", "fujitsu"] }

🎯 Feature Flags

• default: Core functionality (Ising models, QUBO, classical simulators)
• dwave: D-Wave Leap cloud service integration
• braket: AWS Braket quantum computing platform
• fujitsu: Fujitsu Digital Annealer Unit integration

📚 Module Overview

Core Modules

Advanced Algorithms

Embedding and Optimization

Cloud Integration

Applications

🔧 Usage Examples

1. Basic QUBO Problem with Constraints

use quantrs2_anneal::{
    qubo::{QuboBuilder, QuboFormulation},
    simulator::ClassicalAnnealingSimulator
};

// Portfolio optimization: select assets with target return and risk constraints
let mut qubo = QuboBuilder::new(10); // 10 assets

// Objective: minimize risk (maximize return)
let risk_matrix = vec![vec![0.1; 10]; 10]; // Simplified risk matrix
qubo.add_quadratic_objective(&risk_matrix, 1.0)?;

// Constraint: select exactly 5 assets
let selection = vec![1.0; 10];
qubo.add_constraint_eq(&(0..10).collect::<Vec<_>>(), &selection, 5.0, 10.0)?;

// Solve
let formulation = qubo.build()?;
let ising_model = formulation.to_ising_model()?;
let simulator = ClassicalAnnealingSimulator::new(Default::default())?;
let result = simulator.solve(&ising_model)?;

let portfolio = formulation.to_binary_solution(&result.best_spins)?;
println!("Selected assets: {:?}", portfolio);

2. Advanced D-Wave Leap Integration

#[cfg(feature = "dwave")]
use quantrs2_anneal::dwave::{
    DWaveClient, SolverSelector, SolverCategory, EmbeddingConfig, 
    ChainStrengthMethod, AdvancedProblemParams, AnnealingSchedule
};

#[cfg(feature = "dwave")]
async fn solve_with_dwave() -> Result<(), Box<dyn std::error::Error>> {
    // Advanced solver selection
    let selector = SolverSelector {
        category: SolverCategory::QPU,
        min_qubits: Some(2000),
        max_queue_time: Some(60.0),
        topology_preference: Some("pegasus".to_string()),
        ..Default::default()
    };

    // Custom embedding configuration
    let embedding_config = EmbeddingConfig {
        auto_embed: true,
        chain_strength_method: ChainStrengthMethod::Adaptive(1.5),
        optimization_level: 3,
        ..Default::default()
    };

    // Advanced annealing parameters
    let schedule = AnnealingSchedule::pause_and_ramp(200.0, 100.0, 20.0);
    let params = AdvancedProblemParams {
        num_reads: 10000,
        anneal_schedule: Some(schedule),
        programming_thermalization: Some(2000),
        auto_scale: Some(true),
        ..Default::default()
    };

    let client = DWaveClient::new(std::env::var("DWAVE_TOKEN")?, None)?;
    let solution = client.submit_ising_with_embedding(
        &ising_model,
        None, // Auto-select best solver
        Some(params),
        Some(&embedding_config),
    )?;

    Ok(())
}

3. AWS Braket with Cost Management

#[cfg(feature = "braket")]
use quantrs2_anneal::braket::{
    BraketClient, DeviceSelector, DeviceType, CostTracker, AdvancedAnnealingParams
};

#[cfg(feature = "braket")]
async fn solve_with_braket() -> Result<(), Box<dyn std::error::Error>> {
    // Cost tracking setup
    let cost_tracker = CostTracker {
        cost_limit: Some(100.0), // $100 budget
        current_cost: 0.0,
        cost_estimates: HashMap::new(),
    };

    // Device selection for cost optimization
    let device_selector = DeviceSelector {
        device_type: Some(DeviceType::QuantumProcessor),
        max_cost_per_shot: Some(0.001), // Prefer cheaper devices
        required_capabilities: vec!["ANNEALING".to_string()],
        ..Default::default()
    };

    let client = BraketClient::with_config(
        std::env::var("AWS_ACCESS_KEY_ID")?,
        std::env::var("AWS_SECRET_ACCESS_KEY")?,
        None,
        "us-east-1",
        device_selector,
        cost_tracker,
    )?;

    // Submit with cost monitoring
    let task_result = client.submit_ising(&ising_model, None, None)?;
    let metrics = client.get_task_metrics(&task_result.task_arn)?;
    
    println!("Cost: ${:.4}", metrics.cost);
    println!("Best energy: {:.6}", metrics.best_energy.unwrap_or(0.0));

    Ok(())
}

4. Coherent Ising Machine for Large Problems

use quantrs2_anneal::{
    coherent_ising_machine::{CoherentIsingMachine, CIMParams},
    ising::IsingModel
};

// Large-scale continuous optimization problem
let mut large_model = IsingModel::new(10000);
// ... populate with problem data ...

let cim_params = CIMParams {
    pump_power: 2.0,
    detuning: 0.1,
    coupling_strength: 0.5,
    num_iterations: 1000,
    convergence_threshold: 1e-6,
    ..Default::default()
};

let cim = CoherentIsingMachine::new(cim_params)?;
let result = cim.solve(&large_model)?;

println!("CIM found energy: {}", result.best_energy);

5. Multi-Objective Optimization

use quantrs2_anneal::{
    multi_objective::{MultiObjectiveOptimizer, ObjectiveWeight},
    ising::IsingModel
};

// Portfolio optimization with multiple objectives: return, risk, diversity
let mut optimizer = MultiObjectiveOptimizer::new();

// Add objectives with weights
optimizer.add_objective("return", return_model, ObjectiveWeight::Maximize(0.4))?;
optimizer.add_objective("risk", risk_model, ObjectiveWeight::Minimize(0.4))?;
optimizer.add_objective("diversity", diversity_model, ObjectiveWeight::Maximize(0.2))?;

// Pareto frontier analysis
let pareto_solutions = optimizer.find_pareto_frontier(100)?;
for solution in &pareto_solutions {
    println!("Return: {:.3}, Risk: {:.3}, Diversity: {:.3}", 
             solution.objectives["return"],
             solution.objectives["risk"], 
             solution.objectives["diversity"]);
}

6. Energy System Optimization

use quantrs2_anneal::applications::energy::{
    PowerGridOptimizer, GridConstraints, RenewableIntegration
};

// Smart grid optimization with renewable integration
let grid_config = GridConstraints {
    total_demand: 1000.0, // MW
    renewable_capacity: 300.0, // MW
    storage_capacity: 100.0, // MWh
    transmission_limits: vec![200.0, 150.0, 250.0], // MW per line
    cost_coefficients: vec![50.0, 80.0, 120.0], // $/MWh per generator
};

let renewable_config = RenewableIntegration {
    solar_forecast: vec![0.0, 50.0, 150.0, 200.0, 150.0, 50.0], // Hourly MW
    wind_forecast: vec![80.0, 90.0, 70.0, 60.0, 85.0, 95.0], // Hourly MW
    storage_schedule: true,
    demand_response: true,
};

let optimizer = PowerGridOptimizer::new(grid_config, renewable_config)?;
let schedule = optimizer.optimize_24_hour_schedule()?;

println!("Optimized power generation schedule:");
for (hour, power) in schedule.generation_schedule.iter().enumerate() {
    println!("Hour {}: {:.1} MW", hour, power);
}

🧪 Testing and Benchmarking

The framework includes comprehensive testing and benchmarking capabilities:

use quantrs2_anneal::{
    applications::performance_benchmarks::{BenchmarkSuite, Algorithm},
    applications::integration_tests::IntegrationTestSuite
};

// Run performance benchmarks
let mut benchmark = BenchmarkSuite::new();
benchmark.add_algorithm(Algorithm::ClassicalAnnealing);
benchmark.add_algorithm(Algorithm::PopulationAnnealing);
benchmark.add_algorithm(Algorithm::CoherentIsingMachine);

let results = benchmark.run_all_benchmarks()?;
results.print_comparison_report();

// Run integration tests
let test_suite = IntegrationTestSuite::new();
let test_results = test_suite.run_all_tests()?;
println!("Integration tests: {}/{} passed", 
         test_results.passed, test_results.total);

📊 Visualization

use quantrs2_anneal::visualization::{
    EnergyLandscapeVisualizer, SolutionVisualizer, ConvergenceAnalyzer
};

// Visualize energy landscape
let visualizer = EnergyLandscapeVisualizer::new(&ising_model)?;
visualizer.plot_2d_projection("energy_landscape.svg")?;

// Analyze solution quality
let solution_viz = SolutionVisualizer::new(&result);
solution_viz.plot_energy_histogram("solution_quality.svg")?;

// Monitor convergence
let convergence = ConvergenceAnalyzer::new(&annealing_trace);
convergence.plot_convergence_curve("convergence.svg")?;

🔗 Integration with QuantRS2 Ecosystem

QuantRS2-Anneal integrates seamlessly with other QuantRS2 modules:

• quantrs2-core: Shared types, error handling, and utilities
• quantrs2-circuit: QAOA bridge for variational quantum algorithms
• quantrs2-ml: Quantum machine learning integration
• quantrs2-sim: Advanced quantum simulation backends

🚀 Performance Characteristics

📖 Documentation

• API Documentation
• D-Wave Leap Client Guide
• AWS Braket Client Guide
• Embedding Algorithms Guide
• Application Tutorials
• Performance Optimization

🛠️ Examples

Comprehensive examples are available in the examples/ directory:

• Basic Usage: simple_penalty_optimization.rs, advanced_embedding.rs
• Cloud Integration: dwave_leap_client_example.rs, aws_braket_client_example.rs
• Advanced Algorithms: coherent_ising_machine_example.rs, population_annealing_example.rs
• Applications: energy_optimization.rs, portfolio_optimization.rs
• Visualization: energy_landscape_visualization.rs

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under either:

• Apache License, Version 2.0
• MIT License

at your option.

🔬 Research and Citations

If you use QuantRS2-Anneal in your research, please cite:

@software{quantrs2_anneal,
  title={QuantRS2-Anneal: Comprehensive Quantum Annealing Framework},
  author={QuantRS2 Development Team},
  year={2024},
  url={https://github.com/cool-japan/quantrs}
}

🌟 Ready to solve optimization problems with quantum annealing? Check out our Quick Start Guide and Examples!