quantrs2_sim/qml/
mod.rs

1//! Quantum Machine Learning (QML) module.
2//!
3//! This module provides comprehensive quantum machine learning algorithms
4//! with hardware-aware optimization, adaptive training strategies, and
5//! support for various quantum computing architectures.
6
7pub mod benchmarks;
8pub mod circuit;
9pub mod config;
10pub mod trainer;
11
12// Re-export commonly used types and structs
13pub use benchmarks::{
14    benchmark_gradient_methods, benchmark_optimizers, benchmark_quantum_ml_algorithms,
15    run_comprehensive_benchmarks,
16};
17pub use circuit::{HardwareOptimizations, ParameterizedQuantumCircuit};
18pub use config::{
19    GradientMethod, HardwareArchitecture, OptimizerType, QMLAlgorithmType, QMLConfig,
20};
21pub use trainer::{
22    CompilationStats, HardwareAwareCompiler, HardwareMetrics, OptimizerState, QuantumMLTrainer,
23    TrainingHistory, TrainingResult,
24};
25
26use crate::error::Result;
27
28/// Initialize the QML subsystem
29pub fn initialize() -> Result<()> {
30    // Perform any necessary initialization
31    Ok(())
32}
33
34/// Check if hardware-aware optimization is available
35pub fn is_hardware_optimization_available() -> bool {
36    // In practice, this would check for hardware-specific libraries
37    true
38}
39
40/// Get supported hardware architectures
41pub fn get_supported_architectures() -> Vec<HardwareArchitecture> {
42    vec![
43        HardwareArchitecture::NISQ,
44        HardwareArchitecture::FaultTolerant,
45        HardwareArchitecture::Superconducting,
46        HardwareArchitecture::TrappedIon,
47        HardwareArchitecture::Photonic,
48        HardwareArchitecture::NeutralAtom,
49        HardwareArchitecture::ClassicalSimulation,
50    ]
51}
52
53/// Get supported QML algorithms
54pub fn get_supported_algorithms() -> Vec<QMLAlgorithmType> {
55    vec![
56        QMLAlgorithmType::VQE,
57        QMLAlgorithmType::QAOA,
58        QMLAlgorithmType::QCNN,
59        QMLAlgorithmType::QSVM,
60        QMLAlgorithmType::QRL,
61        QMLAlgorithmType::QGAN,
62        QMLAlgorithmType::QBM,
63    ]
64}
65
66/// Get supported gradient methods
67pub fn get_supported_gradient_methods() -> Vec<GradientMethod> {
68    vec![
69        GradientMethod::ParameterShift,
70        GradientMethod::FiniteDifferences,
71        GradientMethod::AutomaticDifferentiation,
72        GradientMethod::NaturalGradients,
73        GradientMethod::StochasticParameterShift,
74    ]
75}
76
77/// Get supported optimizers
78pub fn get_supported_optimizers() -> Vec<OptimizerType> {
79    vec![
80        OptimizerType::Adam,
81        OptimizerType::SGD,
82        OptimizerType::RMSprop,
83        OptimizerType::LBFGS,
84        OptimizerType::QuantumNaturalGradient,
85        OptimizerType::SPSA,
86    ]
87}
88
89/// Create a default configuration for a specific algorithm
90pub fn create_default_config(algorithm: QMLAlgorithmType) -> QMLConfig {
91    QMLConfig::for_algorithm(algorithm)
92}
93
94/// Create a configuration optimized for specific hardware
95pub fn create_hardware_config(hardware: HardwareArchitecture) -> QMLConfig {
96    QMLConfig::for_hardware(hardware)
97}
98
99/// Validate QML configuration
100pub fn validate_config(config: &QMLConfig) -> Result<()> {
101    config
102        .validate()
103        .map_err(|e| crate::error::SimulatorError::InvalidInput(e))
104}
105
106#[cfg(test)]
107mod tests {
108    use super::*;
109    use scirs2_core::ndarray::Array1;
110
111    #[test]
112    fn test_qml_initialization() {
113        let result = initialize();
114        assert!(result.is_ok());
115    }
116
117    #[test]
118    fn test_supported_architectures() {
119        let archs = get_supported_architectures();
120        assert!(!archs.is_empty());
121        assert!(archs.contains(&HardwareArchitecture::NISQ));
122    }
123
124    #[test]
125    fn test_supported_algorithms() {
126        let algos = get_supported_algorithms();
127        assert!(!algos.is_empty());
128        assert!(algos.contains(&QMLAlgorithmType::VQE));
129    }
130
131    #[test]
132    fn test_default_config_creation() {
133        let config = create_default_config(QMLAlgorithmType::VQE);
134        assert_eq!(config.algorithm_type, QMLAlgorithmType::VQE);
135        assert!(validate_config(&config).is_ok());
136    }
137
138    #[test]
139    fn test_hardware_config_creation() {
140        let config = create_hardware_config(HardwareArchitecture::Superconducting);
141        assert_eq!(
142            config.hardware_architecture,
143            HardwareArchitecture::Superconducting
144        );
145        assert!(validate_config(&config).is_ok());
146    }
147
148    #[test]
149    fn test_config_validation() {
150        let mut config = QMLConfig::default();
151        assert!(validate_config(&config).is_ok());
152
153        // Test invalid configuration
154        config.num_qubits = 0;
155        assert!(validate_config(&config).is_err());
156    }
157
158    #[test]
159    fn test_parameterized_circuit_creation() {
160        use crate::circuit_interfaces::InterfaceCircuit;
161
162        let circuit = InterfaceCircuit::new(4, 0);
163        let parameters = Array1::from_vec(vec![0.1, 0.2, 0.3, 0.4]);
164        let parameter_names = vec![
165            "p0".to_string(),
166            "p1".to_string(),
167            "p2".to_string(),
168            "p3".to_string(),
169        ];
170
171        let pqc = ParameterizedQuantumCircuit::new(
172            circuit,
173            parameters,
174            parameter_names,
175            HardwareArchitecture::NISQ,
176        );
177
178        assert_eq!(pqc.num_parameters(), 4);
179        assert_eq!(pqc.num_qubits(), 4);
180    }
181
182    #[test]
183    fn test_hardware_optimizations() {
184        let opts = HardwareOptimizations::for_hardware(HardwareArchitecture::Superconducting, 4);
185
186        // Test connectivity for superconducting (linear)
187        assert!(opts.connectivity_graph[[0, 1]]);
188        assert!(opts.connectivity_graph[[1, 2]]);
189        assert!(!opts.connectivity_graph[[0, 2]]);
190
191        // Test gate fidelities
192        assert!(opts.gate_fidelities.contains_key("X"));
193        assert!(opts.gate_fidelities.contains_key("CNOT"));
194    }
195
196    #[test]
197    fn test_trainer_creation() {
198        use crate::circuit_interfaces::InterfaceCircuit;
199
200        let config = QMLConfig::default();
201        let circuit = InterfaceCircuit::new(config.num_qubits, 0);
202        let parameters = Array1::zeros(config.num_parameters);
203        let parameter_names = (0..config.num_parameters)
204            .map(|i| format!("param_{}", i))
205            .collect();
206
207        let pqc = ParameterizedQuantumCircuit::new(
208            circuit,
209            parameters,
210            parameter_names,
211            config.hardware_architecture,
212        );
213
214        let trainer = QuantumMLTrainer::new(config, pqc, None);
215        assert!(trainer.is_ok());
216    }
217
218    #[test]
219    fn test_optimizer_state() {
220        let state = OptimizerState::new(4, 0.01);
221        assert_eq!(state.parameters.len(), 4);
222        assert_eq!(state.learning_rate, 0.01);
223        assert_eq!(state.iteration, 0);
224    }
225
226    #[test]
227    fn test_training_history() {
228        let mut history = TrainingHistory::default();
229        history.loss_history.push(1.0);
230        history.loss_history.push(0.5);
231        history.loss_history.push(0.2);
232
233        assert_eq!(history.latest_loss(), Some(0.2));
234        assert_eq!(history.best_loss(), Some(0.2));
235    }
236}
quantrs2_sim/qml/mod.rs

quantrs2_sim/qml/
mod.rs
