quantrs2-sim 0.1.3

//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use crate::circuit_interfaces::{InterfaceCircuit, InterfaceGate, InterfaceGateType};
use crate::error::{Result, SimulatorError};
use scirs2_core::random::prelude::*;
use scirs2_core::Complex64;
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

use super::types::{
    AdvancedOptimizerType, AdvancedVQATrainer, FiniteDifferenceGradient, HamiltonianTerm,
    IsingCostFunction, MixerHamiltonian, MixerType, OptimizationProblemType,
    ParameterShiftGradient, ProblemHamiltonian, VQAConfig, VariationalAnsatz,
};

/// Cost function trait
pub trait CostFunction: Send + Sync {
    /// Evaluate cost function for given parameters
    fn evaluate(&self, parameters: &[f64], circuit: &InterfaceCircuit) -> Result<f64>;
    /// Get observables for expectation value calculation
    fn get_observables(&self) -> Vec<String>;
    /// Check if cost function is variational (depends on quantum state)
    fn is_variational(&self) -> bool;
}
/// Gradient calculation methods
pub trait GradientCalculator: Send + Sync {
    /// Calculate gradient using specified method
    fn calculate_gradient(
        &self,
        parameters: &[f64],
        cost_function: &dyn CostFunction,
        circuit: &InterfaceCircuit,
    ) -> Result<Vec<f64>>;
    /// Get gradient calculation method name
    fn method_name(&self) -> &str;
}
/// Benchmark function for VQA performance
pub fn benchmark_advanced_vqa() -> Result<HashMap<String, f64>> {
    let mut results = HashMap::new();
    let start = Instant::now();
    let ansatz = VariationalAnsatz::HardwareEfficient {
        layers: 3,
        entangling_gates: vec![InterfaceGateType::CNOT],
        rotation_gates: vec![InterfaceGateType::RY(0.0)],
    };
    let config = VQAConfig::default();
    let cost_function = Box::new(IsingCostFunction {
        problem_hamiltonian: ProblemHamiltonian {
            terms: vec![HamiltonianTerm {
                coefficient: Complex64::new(-1.0, 0.0),
                pauli_string: "ZZ".to_string(),
                qubits: vec![0, 1],
            }],
            problem_type: OptimizationProblemType::MaxCut,
        },
    });
    let gradient_calculator = Box::new(FiniteDifferenceGradient { epsilon: 1e-4 });
    let mut trainer = AdvancedVQATrainer::new(config, ansatz, cost_function, gradient_calculator)?;
    let _result = trainer.train()?;
    let hardware_efficient_time = start.elapsed().as_millis() as f64;
    results.insert(
        "hardware_efficient_vqa".to_string(),
        hardware_efficient_time,
    );
    Ok(results)
}
#[cfg(test)]
mod tests {
    use super::*;
    #[test]
    fn test_vqa_trainer_creation() {
        let ansatz = VariationalAnsatz::HardwareEfficient {
            layers: 2,
            entangling_gates: vec![InterfaceGateType::CNOT],
            rotation_gates: vec![InterfaceGateType::RY(0.0)],
        };
        let config = VQAConfig::default();
        let cost_function = Box::new(IsingCostFunction {
            problem_hamiltonian: ProblemHamiltonian {
                terms: vec![],
                problem_type: OptimizationProblemType::MaxCut,
            },
        });
        let gradient_calculator = Box::new(FiniteDifferenceGradient { epsilon: 1e-4 });
        let trainer = AdvancedVQATrainer::new(config, ansatz, cost_function, gradient_calculator);
        assert!(trainer.is_ok());
    }
    #[test]
    fn test_parameter_counting() {
        let ansatz = VariationalAnsatz::HardwareEfficient {
            layers: 3,
            entangling_gates: vec![InterfaceGateType::CNOT],
            rotation_gates: vec![InterfaceGateType::RY(0.0)],
        };
        let count =
            AdvancedVQATrainer::count_parameters(&ansatz).expect("Failed to count parameters");
        assert!(count > 0);
    }
    #[test]
    fn test_uccsd_ansatz() {
        let ansatz = VariationalAnsatz::UCCSD {
            num_electrons: 2,
            num_orbitals: 4,
            include_triples: false,
        };
        let count = AdvancedVQATrainer::count_parameters(&ansatz)
            .expect("Failed to count UCCSD parameters");
        assert!(count > 0);
    }
    #[test]
    fn test_qaoa_ansatz() {
        let ansatz = VariationalAnsatz::QAOA {
            problem_hamiltonian: ProblemHamiltonian {
                terms: vec![],
                problem_type: OptimizationProblemType::MaxCut,
            },
            mixer_hamiltonian: MixerHamiltonian {
                terms: vec![],
                mixer_type: MixerType::XMixer,
            },
            layers: 3,
        };
        let count =
            AdvancedVQATrainer::count_parameters(&ansatz).expect("Failed to count QAOA parameters");
        assert_eq!(count, 6);
    }
    #[test]
    fn test_finite_difference_gradient() {
        let gradient_calc = FiniteDifferenceGradient { epsilon: 1e-4 };
        assert_eq!(gradient_calc.method_name(), "FiniteDifference");
    }
    #[test]
    fn test_parameter_shift_gradient() {
        let gradient_calc = ParameterShiftGradient;
        assert_eq!(gradient_calc.method_name(), "ParameterShift");
    }
    #[test]
    fn test_ising_cost_function() {
        let cost_function = IsingCostFunction {
            problem_hamiltonian: ProblemHamiltonian {
                terms: vec![HamiltonianTerm {
                    coefficient: Complex64::new(-1.0, 0.0),
                    pauli_string: "ZZ".to_string(),
                    qubits: vec![0, 1],
                }],
                problem_type: OptimizationProblemType::MaxCut,
            },
        };
        assert!(cost_function.is_variational());
        assert!(!cost_function.get_observables().is_empty());
    }
    #[test]
    fn test_optimizer_types() {
        let optimizers = [
            AdvancedOptimizerType::SPSA,
            AdvancedOptimizerType::QuantumAdam,
            AdvancedOptimizerType::NaturalGradient,
            AdvancedOptimizerType::BayesianOptimization,
        ];
        for optimizer in &optimizers {
            println!("Testing optimizer: {optimizer:?}");
        }
    }
}