Crate quantrs2_tytan

Expand description

High-level quantum annealing interface inspired by Tytan for the QuantRS2 framework.

This crate provides a high-level interface for formulating and solving quantum annealing problems, with support for multiple backend solvers. It is inspired by the Python Tytan library.

§Features

Symbolic Problem Construction: Define QUBO problems using symbolic expressions
Higher-Order Binary Optimization (HOBO): Support for terms beyond quadratic
Multiple Samplers: Choose from various solvers
Auto Result Processing: Automatically convert solutions to multi-dimensional arrays

§Recent Updates (v0.1.0-rc.1)

Refined SciRS2 v0.1.0-rc.1 integration for enhanced performance
High-performance sparse matrix operations via SciRS2
Parallel optimization using scirs2_core::parallel_ops
SIMD-accelerated energy calculations

§Example

Example with the dwave feature enabled:

use quantrs2_tytan::sampler::{SASampler, Sampler};
use quantrs2_tytan::symbol::symbols;
use quantrs2_tytan::compile::Compile;
use quantrs2_tytan::auto_array::AutoArray;

// Define variables
let mut x = symbols("x");
let mut y = symbols("y");
let z = symbols("z");

// Define expression (3 variables, want exactly 2 to be 1)
let two = 2.into();
let h = (x + y + z - 2).pow(&two);

// Compile to QUBO
let (qubo, offset) = Compile::new(h).get_qubo().expect("Failed to compile QUBO");

// Choose a sampler
let solver = SASampler::new(None);

// Sample
let mut result = solver.run_qubo(&qubo, 100).expect("Failed to run QUBO sampler");

// Display results
for r in &result {
    println!("{:?}", r);
}

Basic example without the dwave feature (no symbolic math):

use quantrs2_tytan::sampler::{SASampler, Sampler};
use std::collections::HashMap;
use scirs2_core::ndarray::Array;

// Create a simple QUBO matrix manually
let mut matrix = Array::<f64, _>::zeros((2, 2));
matrix[[0, 0]] = -1.0;  // Linear term for x
matrix[[1, 1]] = -1.0;  // Linear term for y
matrix[[0, 1]] = 2.0;   // Quadratic term for x*y
matrix[[1, 0]] = 2.0;   // Symmetric

// Create variable map
let mut var_map = HashMap::new();
var_map.insert("x".to_string(), 0);
var_map.insert("y".to_string(), 1);

// Choose a sampler
let solver = SASampler::new(None);

// Sample by converting to the dynamic format for hobo
let matrix_dyn = matrix.into_dyn();
let mut result = solver.run_hobo(&(matrix_dyn, var_map), 100).expect("Failed to run HOBO sampler");

// Display results
for r in &result {
    println!("{:?}", r);
}

Re-exports§

pub use advanced_error_mitigation::create_advanced_error_mitigation_manager;
pub use advanced_error_mitigation::create_lightweight_error_mitigation_manager;
pub use advanced_error_mitigation::AdvancedErrorMitigationManager;
pub use advanced_error_mitigation::ErrorMitigationConfig;
pub use advanced_performance_analysis::create_comprehensive_analyzer;
pub use advanced_performance_analysis::create_lightweight_analyzer;
pub use advanced_performance_analysis::AdvancedPerformanceAnalyzer;
pub use advanced_performance_analysis::AnalysisConfig;
pub use advanced_visualization::create_advanced_visualization_manager;
pub use advanced_visualization::create_lightweight_visualization_manager;
pub use advanced_visualization::AdvancedVisualizationManager;
pub use advanced_visualization::VisualizationConfig;
pub use analysis::calculate_diversity;
pub use analysis::cluster_solutions;
pub use analysis::visualize_energy_distribution;
pub use grover_amplitude_amplification::GroverAmplificationConfig;
pub use grover_amplitude_amplification::GroverAmplifiedSampler;
pub use grover_amplitude_amplification::GroverAmplifiedSolver;
pub use optimize::calculate_energy;
pub use optimize::optimize_hobo;
pub use optimize::optimize_qubo;
pub use quantum_adiabatic_path_optimization::AdiabaticPathConfig;
pub use quantum_adiabatic_path_optimization::PathInterpolation;
pub use quantum_adiabatic_path_optimization::QuantumAdiabaticPathOptimizer;
pub use quantum_adiabatic_path_optimization::QuantumAdiabaticSampler;
pub use sampler::ArminSampler;
pub use sampler::DWaveSampler;
pub use sampler::GASampler;
pub use sampler::MIKASAmpler;
pub use sampler::SASampler;
pub use scirs_stub::SCIRS2_AVAILABLE;
pub use tensor_network_sampler::create_mera_sampler;
pub use tensor_network_sampler::create_mps_sampler;
pub use tensor_network_sampler::create_peps_sampler;
pub use tensor_network_sampler::TensorNetworkSampler;
pub use visual_problem_builder::BuilderConfig;
pub use visual_problem_builder::ConstraintType;
pub use visual_problem_builder::ExportFormat;
pub use visual_problem_builder::ObjectiveExpression;
pub use visual_problem_builder::VariableType;
pub use visual_problem_builder::VisualProblem;
pub use visual_problem_builder::VisualProblemBuilder;

Modules§

adaptive_noise_calibration: Adaptive Noise Models and Calibration
adaptive_optimization: Adaptive optimization framework that automatically adjusts algorithms and parameters.
advanced_error_mitigation: Advanced Error Mitigation and Calibration
advanced_performance_analysis: Advanced Performance Analysis and Benchmarking
advanced_problem_decomposition: Advanced Problem Decomposition for QUBO Optimization
advanced_visualization: Advanced Visualization and Analysis
ai_assisted_optimization: AI-Assisted Quantum Optimization
analysis: Analysis utilities for quantum annealing results.
applications: Industry-specific application modules for quantum optimization.
auto_array: Automatic conversion of sample results to multi-dimensional arrays.
benchmark: Hardware benchmarking suite with SciRS2 analysis
coherent_ising_machine: Coherent Ising Machine (CIM) simulation for quantum-inspired optimization.
compile: Compilation of symbolic expressions to QUBO/HOBO models.
constraints: Constraint programming enhancements for optimization problems.
encoding: Variable encoding schemes for optimization problems.
gpu: GPU acceleration for QUBO/HOBO optimization.
gpu_benchmark: GPU benchmarking framework for performance testing and analysis.
gpu_kernels: CUDA and OpenCL kernels for GPU-accelerated optimization.
gpu_memory_pool: GPU memory pooling for efficient allocation and reuse.
gpu_performance: GPU performance optimization and profiling.
gpu_samplers: GPU-accelerated samplers with SciRS2 integration.
grover_amplitude_amplification: Grover-Inspired Amplitude Amplification for QUBO Optimization
hybrid_algorithms: Hybrid quantum-classical algorithms for optimization.
ml_guided_sampling: Machine learning guided sampling algorithms
multi_objective_optimization: Multi-Objective QUBO Optimization
optimization: Optimization strategies for quantum annealing
optimize: Optimization utilities for QUBO/HOBO problems.
parallel_tempering: Parallel tempering implementation for enhanced sampling.
parallel_tempering_advanced: Advanced parallel tempering implementation
performance_optimization: Performance optimization module for critical paths.
performance_profiler: Performance profiler for quantum optimization.
problem_decomposition: Problem decomposition methods for large-scale optimization.
problem_dsl: Problem modeling DSL for quantum optimization.
quantum_adiabatic_path_optimization: Quantum Adiabatic Optimization with Dynamic Path Optimization
quantum_advantage_analysis: Quantum Advantage Analysis Suite
quantum_advantage_prediction: Quantum Advantage Prediction System
quantum_annealing: Simulated Quantum Annealing implementation with SciRS2
quantum_circuit_annealing_compiler: Quantum Circuit to Annealing Compiler
quantum_classical_hybrid: Quantum-Classical Hybrid Refinement
quantum_error_correction: Advanced Quantum Error Correction for Optimization
quantum_inspired_ml: Quantum-inspired machine learning algorithms.
quantum_ml_integration: Quantum Machine Learning integration for optimization.
quantum_neural_networks: Advanced Quantum Neural Networks for Optimization
quantum_optimization_extensions: Extended quantum optimization algorithms.
quantum_state_tomography: Quantum State Tomography and Characterization
realtime_performance_dashboard: Real-time Performance Dashboard
realtime_quantum_integration: Real-time Quantum Computing Integration
sampler: Samplers for solving QUBO/HOBO problems.
sampler_framework: Sampler framework extensions for advanced optimization strategies.
scirs_stub: Stub for SciRS2 integration
sensitivity_analysis: Sensitivity analysis for optimization parameters.
solution_clustering: Solution clustering algorithms
solution_debugger: Solution debugger for quantum optimization.
solution_statistics: Solution statistics and analysis
symbol: Symbolic representation of variables for quantum annealing problems.
tensor_network_sampler: Tensor Network-Based Quantum Annealing Sampler
testing_framework: Automated testing framework for quantum optimization.
topological_optimization: Topological optimization for quantum computing.
variable_correlation: Variable correlation analysis
variational_quantum_factoring: Variational Quantum Factoring (VQF) implementation.
visual_problem_builder: Visual problem builder for interactive QUBO construction.
visualization: Advanced visualization for quantum annealing results

Macros§

profile: Profiling macros
time_it

Structs§

IsingModel: Represents an Ising model for quantum annealing
QuboBuilder: A builder for creating QUBO problems
QuboModel: Represents a Quadratic Unconstrained Binary Optimization (QUBO) problem

Enums§

IsingError: Errors that can occur when working with Ising models
QuboError: Errors that can occur when formulating QUBO problems

Traits§

QuboFormulation: Trait for problems that can be formulated as QUBO

Functions§

is_available: Check if the module is available
is_gpu_available
version: Print version information

Type Aliases§

IsingResult: Result type for Ising model operations
QuboResult: Result type for QUBO problem operations

Crate quantrs2_tytan

Crate quantrs2_tytan Copy item path

§Features

§Recent Updates (v0.1.0-rc.1)

§Example

Re-exports§

Modules§

Macros§

Structs§

Enums§

Traits§

Functions§

Type Aliases§

Crate quantrs2_tytan