quantrs2_sim/advanced_variational_algorithms/
functions.rs

1//! Auto-generated module
2//!
3//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
4
5use crate::circuit_interfaces::{InterfaceCircuit, InterfaceGate, InterfaceGateType};
6use crate::error::{Result, SimulatorError};
7use scirs2_core::random::prelude::*;
8use scirs2_core::Complex64;
9use std::collections::{HashMap, VecDeque};
10use std::time::{Duration, Instant};
11
12use super::types::{
13    AdvancedOptimizerType, AdvancedVQATrainer, FiniteDifferenceGradient, HamiltonianTerm,
14    IsingCostFunction, MixerHamiltonian, MixerType, OptimizationProblemType,
15    ParameterShiftGradient, ProblemHamiltonian, VQAConfig, VariationalAnsatz,
16};
17
18/// Cost function trait
19pub trait CostFunction: Send + Sync {
20    /// Evaluate cost function for given parameters
21    fn evaluate(&self, parameters: &[f64], circuit: &InterfaceCircuit) -> Result<f64>;
22    /// Get observables for expectation value calculation
23    fn get_observables(&self) -> Vec<String>;
24    /// Check if cost function is variational (depends on quantum state)
25    fn is_variational(&self) -> bool;
26}
27/// Gradient calculation methods
28pub trait GradientCalculator: Send + Sync {
29    /// Calculate gradient using specified method
30    fn calculate_gradient(
31        &self,
32        parameters: &[f64],
33        cost_function: &dyn CostFunction,
34        circuit: &InterfaceCircuit,
35    ) -> Result<Vec<f64>>;
36    /// Get gradient calculation method name
37    fn method_name(&self) -> &str;
38}
39/// Benchmark function for VQA performance
40pub fn benchmark_advanced_vqa() -> Result<HashMap<String, f64>> {
41    let mut results = HashMap::new();
42    let start = Instant::now();
43    let ansatz = VariationalAnsatz::HardwareEfficient {
44        layers: 3,
45        entangling_gates: vec![InterfaceGateType::CNOT],
46        rotation_gates: vec![InterfaceGateType::RY(0.0)],
47    };
48    let config = VQAConfig::default();
49    let cost_function = Box::new(IsingCostFunction {
50        problem_hamiltonian: ProblemHamiltonian {
51            terms: vec![HamiltonianTerm {
52                coefficient: Complex64::new(-1.0, 0.0),
53                pauli_string: "ZZ".to_string(),
54                qubits: vec![0, 1],
55            }],
56            problem_type: OptimizationProblemType::MaxCut,
57        },
58    });
59    let gradient_calculator = Box::new(FiniteDifferenceGradient { epsilon: 1e-4 });
60    let mut trainer = AdvancedVQATrainer::new(config, ansatz, cost_function, gradient_calculator)?;
61    let _result = trainer.train()?;
62    let hardware_efficient_time = start.elapsed().as_millis() as f64;
63    results.insert(
64        "hardware_efficient_vqa".to_string(),
65        hardware_efficient_time,
66    );
67    Ok(results)
68}
69#[cfg(test)]
70mod tests {
71    use super::*;
72    #[test]
73    fn test_vqa_trainer_creation() {
74        let ansatz = VariationalAnsatz::HardwareEfficient {
75            layers: 2,
76            entangling_gates: vec![InterfaceGateType::CNOT],
77            rotation_gates: vec![InterfaceGateType::RY(0.0)],
78        };
79        let config = VQAConfig::default();
80        let cost_function = Box::new(IsingCostFunction {
81            problem_hamiltonian: ProblemHamiltonian {
82                terms: vec![],
83                problem_type: OptimizationProblemType::MaxCut,
84            },
85        });
86        let gradient_calculator = Box::new(FiniteDifferenceGradient { epsilon: 1e-4 });
87        let trainer = AdvancedVQATrainer::new(config, ansatz, cost_function, gradient_calculator);
88        assert!(trainer.is_ok());
89    }
90    #[test]
91    fn test_parameter_counting() {
92        let ansatz = VariationalAnsatz::HardwareEfficient {
93            layers: 3,
94            entangling_gates: vec![InterfaceGateType::CNOT],
95            rotation_gates: vec![InterfaceGateType::RY(0.0)],
96        };
97        let count =
98            AdvancedVQATrainer::count_parameters(&ansatz).expect("Failed to count parameters");
99        assert!(count > 0);
100    }
101    #[test]
102    fn test_uccsd_ansatz() {
103        let ansatz = VariationalAnsatz::UCCSD {
104            num_electrons: 2,
105            num_orbitals: 4,
106            include_triples: false,
107        };
108        let count = AdvancedVQATrainer::count_parameters(&ansatz)
109            .expect("Failed to count UCCSD parameters");
110        assert!(count > 0);
111    }
112    #[test]
113    fn test_qaoa_ansatz() {
114        let ansatz = VariationalAnsatz::QAOA {
115            problem_hamiltonian: ProblemHamiltonian {
116                terms: vec![],
117                problem_type: OptimizationProblemType::MaxCut,
118            },
119            mixer_hamiltonian: MixerHamiltonian {
120                terms: vec![],
121                mixer_type: MixerType::XMixer,
122            },
123            layers: 3,
124        };
125        let count =
126            AdvancedVQATrainer::count_parameters(&ansatz).expect("Failed to count QAOA parameters");
127        assert_eq!(count, 6);
128    }
129    #[test]
130    fn test_finite_difference_gradient() {
131        let gradient_calc = FiniteDifferenceGradient { epsilon: 1e-4 };
132        assert_eq!(gradient_calc.method_name(), "FiniteDifference");
133    }
134    #[test]
135    fn test_parameter_shift_gradient() {
136        let gradient_calc = ParameterShiftGradient;
137        assert_eq!(gradient_calc.method_name(), "ParameterShift");
138    }
139    #[test]
140    fn test_ising_cost_function() {
141        let cost_function = IsingCostFunction {
142            problem_hamiltonian: ProblemHamiltonian {
143                terms: vec![HamiltonianTerm {
144                    coefficient: Complex64::new(-1.0, 0.0),
145                    pauli_string: "ZZ".to_string(),
146                    qubits: vec![0, 1],
147                }],
148                problem_type: OptimizationProblemType::MaxCut,
149            },
150        };
151        assert!(cost_function.is_variational());
152        assert!(!cost_function.get_observables().is_empty());
153    }
154    #[test]
155    fn test_optimizer_types() {
156        let optimizers = [
157            AdvancedOptimizerType::SPSA,
158            AdvancedOptimizerType::QuantumAdam,
159            AdvancedOptimizerType::NaturalGradient,
160            AdvancedOptimizerType::BayesianOptimization,
161        ];
162        for optimizer in &optimizers {
163            println!("Testing optimizer: {optimizer:?}");
164        }
165    }
166}
quantrs2_sim/advanced_variational_algorithms/functions.rs

quantrs2_sim/advanced_variational_algorithms/
functions.rs
