scirs2_interpolate/advanced_coordinator_modules/

quantum_optimization.rs

//! Quantum-inspired optimization system for interpolation parameters
//!
//! This module provides advanced quantum-inspired algorithms for parameter optimization
//! in interpolation methods, using quantum annealing, superposition, and entanglement
//! concepts to find optimal parameter configurations.

use super::types::*;
use crate::error::InterpolateResult;
use scirs2_core::ndarray::Array2;
use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::time::Instant;

/// Quantum-inspired parameter optimizer
#[derive(Debug)]
pub struct QuantumParameterOptimizer<F: Float + Debug> {
    /// Quantum state representation
    quantum_state: QuantumState<F>,
    /// Quantum operators for optimization
    quantum_operators: Vec<QuantumOperator<F>>,
    /// Quantum annealing parameters
    annealing_params: AnnealingParameters<F>,
    /// Quantum measurement system
    measurement_system: QuantumMeasurement<F>,
    /// Optimization history
    optimization_history: VecDeque<QuantumOptimizationResult<F>>,
    /// Population of quantum states (for quantum evolution)
    state_population: Vec<QuantumState<F>>,
}

/// Quantum state for optimization
#[derive(Debug, Clone)]
pub struct QuantumState<F: Float> {
    /// State amplitudes (complex numbers represented as pairs of real values)
    pub amplitudes: Vec<(F, F)>, // (real, imaginary)
    /// State phases
    pub phases: Vec<F>,
    /// Entanglement information
    pub entanglement: EntanglementInfo,
    /// Parameter values encoded in quantum state
    pub parameter_values: HashMap<String, F>,
    /// Energy level of the state
    pub energy: F,
}

/// Entanglement information between parameters
#[derive(Debug, Clone)]
pub struct EntanglementInfo {
    /// Entangled parameter pairs
    pub entangled_pairs: Vec<(usize, usize)>,
    /// Entanglement strength (0-1)
    pub entanglement_strength: f64,
    /// Entanglement matrix for complex correlations
    pub entanglement_matrix: Option<Vec<Vec<f64>>>,
}

/// Quantum operator for parameter manipulation
#[derive(Debug, Clone)]
pub enum QuantumOperator<F: Float> {
    /// Hadamard operator (creates superposition)
    Hadamard { parameter: usize },
    /// Pauli-X operator (bit flip)
    PauliX { parameter: usize },
    /// Pauli-Y operator (complex rotation)
    PauliY { parameter: usize },
    /// Pauli-Z operator (phase flip)
    PauliZ { parameter: usize },
    /// CNOT operator (entanglement creation)
    CNOT { control: usize, target: usize },
    /// Rotation operator (parameter exploration)
    Rotation { parameter: usize, angle: F },
    /// Phase shift operator
    PhaseShift { parameter: usize, phase: F },
    /// Quantum Fourier Transform
    QFT { parameters: Vec<usize> },
    /// Custom quantum operator
    Custom {
        name: String,
        matrix: Array2<(F, F)>, // Complex matrix as (real, imaginary) pairs
    },
}

/// Quantum annealing parameters
#[derive(Debug, Clone)]
pub struct AnnealingParameters<F: Float> {
    /// Initial temperature
    pub initial_temperature: F,
    /// Final temperature
    pub final_temperature: F,
    /// Annealing schedule
    pub annealing_schedule: AnnealingSchedule<F>,
    /// Number of annealing steps
    pub num_steps: usize,
    /// Quantum tunneling strength
    pub tunneling_strength: F,
    /// Transverse field strength
    pub transverse_field: F,
}

/// Annealing schedule types
#[derive(Debug, Clone)]
pub enum AnnealingSchedule<F: Float> {
    /// Linear temperature decrease
    Linear,
    /// Exponential temperature decrease
    Exponential { decay_rate: F },
    /// Logarithmic schedule
    Logarithmic { scale_factor: F },
    /// Custom temperature schedule
    Custom { schedule: Vec<F> },
    /// Adaptive schedule based on convergence
    Adaptive {
        convergence_threshold: F,
        adaptation_rate: F,
    },
}

/// Quantum measurement system
#[derive(Debug)]
pub struct QuantumMeasurement<F: Float> {
    /// Measurement operators
    measurement_operators: Vec<MeasurementOperator<F>>,
    /// Measurement results history
    measurement_history: VecDeque<MeasurementResult<F>>,
    /// Measurement bases
    measurement_bases: Vec<MeasurementBasis<F>>,
}

/// Measurement operator for quantum observables
#[derive(Debug, Clone)]
pub struct MeasurementOperator<F: Float> {
    /// Operator name
    pub name: String,
    /// Operator matrix (complex as real/imaginary pairs)
    pub operator: Array2<(F, F)>,
    /// Expected value for this operator
    pub expected_value: Option<F>,
    /// Measurement precision
    pub precision: F,
}

/// Measurement result from quantum system
#[derive(Debug, Clone)]
pub struct MeasurementResult<F: Float> {
    /// Measured value
    pub value: F,
    /// Measurement uncertainty
    pub uncertainty: F,
    /// Measurement basis used
    pub basis: String,
    /// Measurement time
    pub timestamp: Instant,
    /// Probability of this measurement
    pub probability: F,
}

/// Measurement basis for quantum measurements
#[derive(Debug, Clone)]
pub struct MeasurementBasis<F: Float> {
    /// Basis name
    pub name: String,
    /// Basis vectors
    pub basis_vectors: Vec<Vec<(F, F)>>,
    /// Basis completeness (0-1)
    pub completeness: F,
}

/// Result of quantum optimization
#[derive(Debug, Clone)]
pub struct QuantumOptimizationResult<F: Float> {
    /// Optimized parameters
    pub optimized_parameters: HashMap<String, F>,
    /// Final quantum state
    pub final_state: QuantumState<F>,
    /// Energy convergence history
    pub energy_history: Vec<F>,
    /// Quantum optimization algorithm used
    pub algorithm_used: QuantumAlgorithm,
    /// Number of quantum iterations
    pub quantum_iterations: usize,
    /// Measurement statistics
    pub measurement_stats: MeasurementStatistics<F>,
    /// Optimization success
    pub success: bool,
    /// Convergence achieved
    pub converged: bool,
}

/// Types of quantum algorithms available
#[derive(Debug, Clone)]
pub enum QuantumAlgorithm {
    /// Quantum Annealing
    QuantumAnnealing,
    /// Variational Quantum Eigensolver
    VQE,
    /// Quantum Approximate Optimization Algorithm
    QAOA,
    /// Adiabatic Quantum Computation
    AdiabaticQC,
    /// Quantum Phase Estimation
    QPE,
    /// Quantum Amplitude Amplification
    QAA,
}

/// Statistics from quantum measurements
#[derive(Debug, Clone)]
pub struct MeasurementStatistics<F: Float> {
    /// Mean measurement value
    pub mean: F,
    /// Measurement variance
    pub variance: F,
    /// Standard deviation
    pub std_dev: F,
    /// Number of measurements
    pub sample_count: usize,
    /// Confidence interval
    pub confidence_interval: (F, F),
}

impl<F: Float + Debug + std::ops::MulAssign + std::ops::AddAssign + std::ops::SubAssign>
    QuantumParameterOptimizer<F>
{
    /// Create a new quantum parameter optimizer
    pub fn new() -> InterpolateResult<Self> {
        Ok(Self {
            quantum_state: QuantumState::new()?,
            quantum_operators: Vec::new(),
            annealing_params: AnnealingParameters::default(),
            measurement_system: QuantumMeasurement::new()?,
            optimization_history: VecDeque::new(),
            state_population: Vec::new(),
        })
    }

    /// Initialize quantum state from classical parameters
    pub fn initialize_quantum_state(
        &mut self,
        parameters: &HashMap<String, F>,
    ) -> InterpolateResult<()> {
        let param_count = parameters.len();

        // Initialize quantum state with superposition
        self.quantum_state = QuantumState {
            amplitudes: vec![
                (
                    F::one() / F::from(param_count as f64).unwrap().sqrt(),
                    F::zero()
                );
                param_count
            ],
            phases: vec![F::zero(); param_count],
            entanglement: EntanglementInfo::default(),
            parameter_values: parameters.clone(),
            energy: F::zero(),
        };

        // Create initial entanglements between related parameters
        self.create_parameter_entanglements(parameters)?;

        // Initialize default quantum operators
        self.initialize_quantum_operators(param_count)?;

        Ok(())
    }

    /// Optimize parameters using quantum annealing
    pub fn quantum_anneal_optimize(
        &mut self,
        objective_function: impl Fn(&HashMap<String, F>) -> F,
        parameter_bounds: &HashMap<String, (F, F)>,
    ) -> InterpolateResult<QuantumOptimizationResult<F>> {
        let start_time = Instant::now();
        let mut energy_history = Vec::new();

        // Initialize population of quantum states
        self.initialize_population(parameter_bounds, 20)?; // 20 states in population

        let mut current_temperature = self.annealing_params.initial_temperature;
        let temperature_step = (self.annealing_params.initial_temperature
            - self.annealing_params.final_temperature)
            / F::from(self.annealing_params.num_steps as f64).unwrap();

        for step in 0..self.annealing_params.num_steps {
            // Apply quantum operators to create superposition and entanglement
            self.apply_quantum_evolution()?;

            // Measure quantum states to get parameter values
            let measured_params = self.measure_parameters()?;

            // Evaluate objective function
            let energy = objective_function(&measured_params);
            energy_history.push(energy);

            // Update quantum state based on energy
            self.update_quantum_state_energy(energy)?;

            // Apply simulated annealing acceptance criteria
            let accept_probability =
                self.calculate_acceptance_probability(energy, current_temperature);

            if self.should_accept_state(accept_probability)? {
                self.quantum_state.parameter_values = measured_params;
                self.quantum_state.energy = energy;
            }

            // Cool down temperature
            current_temperature = match &self.annealing_params.annealing_schedule {
                AnnealingSchedule::Linear => {
                    self.annealing_params.initial_temperature
                        - temperature_step * F::from(step as f64).unwrap()
                }
                AnnealingSchedule::Exponential { decay_rate } => current_temperature * *decay_rate,
                AnnealingSchedule::Logarithmic { scale_factor } => {
                    self.annealing_params.initial_temperature
                        / (F::one() + *scale_factor * F::from(step as f64).unwrap().ln())
                }
                AnnealingSchedule::Custom { schedule } => {
                    if step < schedule.len() {
                        schedule[step]
                    } else {
                        self.annealing_params.final_temperature
                    }
                }
                AnnealingSchedule::Adaptive {
                    convergence_threshold,
                    adaptation_rate,
                } => {
                    if energy_history.len() > 10 {
                        let recent_variance = self.calculate_energy_variance(
                            &energy_history[energy_history.len() - 10..],
                        );
                        if recent_variance < *convergence_threshold {
                            current_temperature * *adaptation_rate
                        } else {
                            current_temperature
                        }
                    } else {
                        current_temperature
                    }
                }
            };

            // Apply quantum tunneling for escaping local minima
            if step % 100 == 0 {
                self.apply_quantum_tunneling()?;
            }
        }

        // Perform final measurement
        let final_measurement = self.perform_final_measurement()?;
        let measurement_stats = self.calculate_measurement_statistics(&energy_history);

        let result = QuantumOptimizationResult {
            optimized_parameters: self.quantum_state.parameter_values.clone(),
            final_state: self.quantum_state.clone(),
            energy_history,
            algorithm_used: QuantumAlgorithm::QuantumAnnealing,
            quantum_iterations: self.annealing_params.num_steps,
            measurement_stats,
            success: true,
            converged: self.check_convergence(&final_measurement)?,
        };

        // Store optimization result
        self.optimization_history.push_back(result.clone());
        if self.optimization_history.len() > 50 {
            self.optimization_history.pop_front();
        }

        Ok(result)
    }

    /// Optimize using Variational Quantum Eigensolver (VQE)
    pub fn vqe_optimize(
        &mut self,
        hamiltonian: impl Fn(&HashMap<String, F>) -> F,
        parameter_bounds: &HashMap<String, (F, F)>,
        max_iterations: usize,
    ) -> InterpolateResult<QuantumOptimizationResult<F>> {
        let mut energy_history = Vec::new();
        let mut current_best_energy = F::infinity();
        let mut current_best_params = self.quantum_state.parameter_values.clone();

        for iteration in 0..max_iterations {
            // Prepare ansatz state
            self.prepare_ansatz_state()?;

            // Measure expectation value of Hamiltonian
            let measured_params = self.measure_parameters()?;
            let energy = hamiltonian(&measured_params);
            energy_history.push(energy);

            // Update best solution
            if energy < current_best_energy {
                current_best_energy = energy;
                current_best_params = measured_params.clone();
                self.quantum_state.parameter_values = measured_params;
                self.quantum_state.energy = energy;
            }

            // Apply variational updates to quantum state
            self.apply_variational_updates(energy)?;

            // Check for convergence
            if iteration > 10
                && self.check_vqe_convergence(&energy_history[iteration - 10..iteration])
            {
                break;
            }
        }

        let measurement_stats = self.calculate_measurement_statistics(&energy_history);

        Ok(QuantumOptimizationResult {
            optimized_parameters: current_best_params,
            final_state: self.quantum_state.clone(),
            energy_history,
            algorithm_used: QuantumAlgorithm::VQE,
            quantum_iterations: max_iterations,
            measurement_stats,
            success: true,
            converged: true,
        })
    }

    /// Create entanglements between related parameters
    fn create_parameter_entanglements(
        &mut self,
        parameters: &HashMap<String, F>,
    ) -> InterpolateResult<()> {
        let param_names: Vec<_> = parameters.keys().collect();
        let mut entangled_pairs = Vec::new();

        // Create entanglements between parameters that might be correlated
        for (i, &param1) in param_names.iter().enumerate() {
            for (j, &param2) in param_names.iter().enumerate().skip(i + 1) {
                // Heuristic: entangle parameters with similar names or related functions
                if self.should_entangle_parameters(param1, param2) {
                    entangled_pairs.push((i, j));
                }
            }
        }

        self.quantum_state.entanglement = EntanglementInfo {
            entangled_pairs,
            entanglement_strength: 0.5, // Medium entanglement
            entanglement_matrix: None,
        };

        Ok(())
    }

    /// Determine if two parameters should be entangled
    fn should_entangle_parameters(&self, param1: &str, param2: &str) -> bool {
        // Heuristic rules for parameter entanglement
        let related_pairs = [
            ("tolerance", "max_iterations"),
            ("degree", "smoothing"),
            ("kernel_width", "regularization"),
            ("learning_rate", "momentum"),
        ];

        related_pairs.iter().any(|(p1, p2)| {
            (param1.contains(p1) && param2.contains(p2))
                || (param1.contains(p2) && param2.contains(p1))
        })
    }

    /// Initialize quantum operators for optimization
    fn initialize_quantum_operators(&mut self, param_count: usize) -> InterpolateResult<()> {
        self.quantum_operators.clear();

        // Add Hadamard operators for superposition
        for i in 0..param_count {
            self.quantum_operators
                .push(QuantumOperator::Hadamard { parameter: i });
        }

        // Add rotation operators for exploration
        for i in 0..param_count {
            self.quantum_operators.push(QuantumOperator::Rotation {
                parameter: i,
                angle: F::from(std::f64::consts::PI / 4.0).unwrap(),
            });
        }

        // Add CNOT operators for entanglement
        for &(control, target) in &self.quantum_state.entanglement.entangled_pairs {
            self.quantum_operators
                .push(QuantumOperator::CNOT { control, target });
        }

        Ok(())
    }

    /// Initialize population of quantum states
    fn initialize_population(
        &mut self,
        parameter_bounds: &HashMap<String, (F, F)>,
        population_size: usize,
    ) -> InterpolateResult<()> {
        self.state_population.clear();

        for _ in 0..population_size {
            let mut state = QuantumState::new()?;

            // Initialize with random parameter values within bounds
            for (param_name, &(min_val, max_val)) in parameter_bounds {
                let random_val = min_val
                    + (max_val - min_val) * F::from(scirs2_core::random::random::<f64>()).unwrap();
                state
                    .parameter_values
                    .insert(param_name.clone(), random_val);
            }

            // Initialize quantum amplitudes
            let param_count = parameter_bounds.len();
            state.amplitudes = vec![
                (
                    F::one() / F::from(param_count as f64).unwrap().sqrt(),
                    F::zero()
                );
                param_count
            ];
            state.phases =
                vec![
                    F::from(scirs2_core::random::random::<f64>() * 2.0 * std::f64::consts::PI)
                        .unwrap();
                    param_count
                ];

            self.state_population.push(state);
        }

        Ok(())
    }

    /// Apply quantum evolution to the state
    fn apply_quantum_evolution(&mut self) -> InterpolateResult<()> {
        // Apply a subset of quantum operators
        let operators_to_apply = std::cmp::min(3, self.quantum_operators.len());

        for i in 0..operators_to_apply {
            let operator = self.quantum_operators[i].clone();
            self.apply_quantum_operator(&operator)?;
        }

        // Add quantum decoherence
        self.apply_quantum_decoherence(F::from(0.01).unwrap())?;

        Ok(())
    }

    /// Apply a specific quantum operator
    fn apply_quantum_operator(&mut self, operator: &QuantumOperator<F>) -> InterpolateResult<()> {
        match operator {
            QuantumOperator::Hadamard { parameter } => {
                if *parameter < self.quantum_state.amplitudes.len() {
                    // Apply Hadamard transformation (creates superposition)
                    let (real, imag) = self.quantum_state.amplitudes[*parameter];
                    let sqrt2_inv = F::one() / F::from(2.0_f64.sqrt()).unwrap();
                    self.quantum_state.amplitudes[*parameter] =
                        ((real + imag) * sqrt2_inv, (real - imag) * sqrt2_inv);
                }
            }
            QuantumOperator::Rotation { parameter, angle } => {
                if *parameter < self.quantum_state.phases.len() {
                    self.quantum_state.phases[*parameter] += *angle;
                }
            }
            QuantumOperator::CNOT { control, target } => {
                // Simplified CNOT operation on amplitudes
                if *control < self.quantum_state.amplitudes.len()
                    && *target < self.quantum_state.amplitudes.len()
                {
                    let control_amp = self.quantum_state.amplitudes[*control];
                    let target_amp = self.quantum_state.amplitudes[*target];

                    // CNOT logic: if control is |1⟩, flip target
                    if control_amp.0.abs() > F::from(0.5).unwrap() {
                        self.quantum_state.amplitudes[*target] = (target_amp.1, target_amp.0);
                    }
                }
            }
            _ => {
                // Other operators can be implemented similarly
            }
        }

        Ok(())
    }

    /// Apply quantum decoherence
    fn apply_quantum_decoherence(&mut self, decoherence_rate: F) -> InterpolateResult<()> {
        for (real, imag) in &mut self.quantum_state.amplitudes {
            *real *= F::one() - decoherence_rate;
            *imag *= F::one() - decoherence_rate;
        }

        for phase in &mut self.quantum_state.phases {
            *phase *= F::one() - decoherence_rate;
        }

        Ok(())
    }

    /// Measure quantum state to extract classical parameter values
    fn measure_parameters(&self) -> InterpolateResult<HashMap<String, F>> {
        let mut measured_params = HashMap::new();

        for (param_name, &current_value) in &self.quantum_state.parameter_values {
            // Quantum measurement introduces uncertainty
            let measurement_uncertainty = F::from(0.01).unwrap(); // 1% uncertainty
            let random_factor = F::from(scirs2_core::random::random::<f64>() - 0.5).unwrap()
                * measurement_uncertainty;
            let measured_value = current_value * (F::one() + random_factor);

            measured_params.insert(param_name.clone(), measured_value);
        }

        Ok(measured_params)
    }

    /// Update quantum state energy
    fn update_quantum_state_energy(&mut self, energy: F) -> InterpolateResult<()> {
        self.quantum_state.energy = energy;

        // Normalize amplitudes based on energy (lower energy = higher amplitude)
        let energy_factor = (-energy / F::from(10.0).unwrap()).exp();
        for (real, imag) in &mut self.quantum_state.amplitudes {
            *real *= energy_factor;
            *imag *= energy_factor;
        }

        Ok(())
    }

    /// Calculate acceptance probability for simulated annealing
    fn calculate_acceptance_probability(&self, energy: F, temperature: F) -> F {
        if energy < self.quantum_state.energy {
            F::one() // Always accept better solutions
        } else {
            let delta_energy = energy - self.quantum_state.energy;
            (-delta_energy / temperature).exp()
        }
    }

    /// Determine if state should be accepted
    fn should_accept_state(&self, acceptance_probability: F) -> InterpolateResult<bool> {
        let random_value = F::from(scirs2_core::random::random::<f64>()).unwrap();
        Ok(random_value < acceptance_probability)
    }

    /// Apply quantum tunneling for escaping local minima
    fn apply_quantum_tunneling(&mut self) -> InterpolateResult<()> {
        let tunneling_strength = self.annealing_params.tunneling_strength;

        // Add quantum fluctuations to parameter values
        for value in self.quantum_state.parameter_values.values_mut() {
            let tunneling_offset =
                F::from(scirs2_core::random::random::<f64>() - 0.5).unwrap() * tunneling_strength;
            *value += tunneling_offset;
        }

        Ok(())
    }

    /// Calculate energy variance for convergence checking
    fn calculate_energy_variance(&self, energies: &[F]) -> F {
        if energies.len() < 2 {
            return F::infinity();
        }

        let mean = energies.iter().fold(F::zero(), |acc, &x| acc + x)
            / F::from(energies.len() as f64).unwrap();
        let variance = energies
            .iter()
            .map(|&x| (x - mean) * (x - mean))
            .fold(F::zero(), |acc, x| acc + x)
            / F::from(energies.len() as f64).unwrap();

        variance
    }

    /// Perform final quantum measurement
    fn perform_final_measurement(&self) -> InterpolateResult<MeasurementResult<F>> {
        let final_energy = self.quantum_state.energy;
        let uncertainty = F::from(0.001).unwrap(); // Final measurement has low uncertainty

        Ok(MeasurementResult {
            value: final_energy,
            uncertainty,
            basis: "energy".to_string(),
            timestamp: Instant::now(),
            probability: F::one(), // Definite measurement
        })
    }

    /// Calculate measurement statistics
    fn calculate_measurement_statistics(&self, energy_history: &[F]) -> MeasurementStatistics<F> {
        if energy_history.is_empty() {
            return MeasurementStatistics {
                mean: F::zero(),
                variance: F::zero(),
                std_dev: F::zero(),
                sample_count: 0,
                confidence_interval: (F::zero(), F::zero()),
            };
        }

        let mean = energy_history.iter().fold(F::zero(), |acc, &x| acc + x)
            / F::from(energy_history.len() as f64).unwrap();
        let variance = energy_history
            .iter()
            .map(|&x| (x - mean) * (x - mean))
            .fold(F::zero(), |acc, x| acc + x)
            / F::from(energy_history.len() as f64).unwrap();
        let std_dev = variance.sqrt();

        // 95% confidence interval
        let confidence_margin =
            std_dev * F::from(1.96).unwrap() / F::from(energy_history.len() as f64).unwrap().sqrt();

        MeasurementStatistics {
            mean,
            variance,
            std_dev,
            sample_count: energy_history.len(),
            confidence_interval: (mean - confidence_margin, mean + confidence_margin),
        }
    }

    /// Check convergence of final measurement
    fn check_convergence(&self, _measurement: &MeasurementResult<F>) -> InterpolateResult<bool> {
        // Simple convergence check based on energy stability
        if self.optimization_history.len() < 3 {
            return Ok(false);
        }

        let recent_energies: Vec<F> = self
            .optimization_history
            .iter()
            .rev()
            .take(3)
            .map(|result| result.final_state.energy)
            .collect();

        let energy_variance = self.calculate_energy_variance(&recent_energies);
        Ok(energy_variance < F::from(1e-6).unwrap())
    }

    /// Prepare ansatz state for VQE
    fn prepare_ansatz_state(&mut self) -> InterpolateResult<()> {
        // Apply parameterized quantum circuit
        for i in 0..self.quantum_state.amplitudes.len() {
            self.apply_quantum_operator(&QuantumOperator::Rotation {
                parameter: i,
                angle: self.quantum_state.phases[i],
            })?;
        }

        Ok(())
    }

    /// Apply variational updates for VQE
    fn apply_variational_updates(&mut self, energy: F) -> InterpolateResult<()> {
        let learning_rate = F::from(0.01).unwrap();

        // Simple gradient-based update (simplified)
        for phase in &mut self.quantum_state.phases {
            let gradient = if energy > self.quantum_state.energy {
                -learning_rate
            } else {
                learning_rate
            };
            *phase += gradient;
        }

        Ok(())
    }

    /// Check VQE convergence
    fn check_vqe_convergence(&self, recent_energies: &[F]) -> bool {
        if recent_energies.len() < 5 {
            return false;
        }

        let variance = self.calculate_energy_variance(recent_energies);
        variance < F::from(1e-8).unwrap()
    }

    /// Get optimization history
    pub fn get_optimization_history(&self) -> &VecDeque<QuantumOptimizationResult<F>> {
        &self.optimization_history
    }

    /// Get current quantum state
    pub fn get_quantum_state(&self) -> &QuantumState<F> {
        &self.quantum_state
    }
}

impl<F: Float + std::ops::SubAssign> QuantumState<F> {
    /// Create a new quantum state
    pub fn new() -> InterpolateResult<Self> {
        Ok(Self {
            amplitudes: Vec::new(),
            phases: Vec::new(),
            entanglement: EntanglementInfo::default(),
            parameter_values: HashMap::new(),
            energy: F::zero(),
        })
    }

    /// Get the probability of measuring a specific parameter value
    pub fn get_measurement_probability(&self, parameter_index: usize) -> F {
        if parameter_index < self.amplitudes.len() {
            let (real, imag) = self.amplitudes[parameter_index];
            real * real + imag * imag
        } else {
            F::zero()
        }
    }

    /// Calculate quantum entropy
    pub fn calculate_entropy(&self) -> F {
        let mut entropy = F::zero();

        for (real, imag) in &self.amplitudes {
            let probability = *real * *real + *imag * *imag;
            if probability > F::zero() {
                entropy -= probability * probability.ln();
            }
        }

        entropy
    }
}

impl Default for EntanglementInfo {
    fn default() -> Self {
        Self {
            entangled_pairs: Vec::new(),
            entanglement_strength: 0.0,
            entanglement_matrix: None,
        }
    }
}

impl<F: Float> Default for AnnealingParameters<F> {
    fn default() -> Self {
        Self {
            initial_temperature: F::from(1.0).unwrap(),
            final_temperature: F::from(0.01).unwrap(),
            annealing_schedule: AnnealingSchedule::Linear,
            num_steps: 1000,
            tunneling_strength: F::from(0.1).unwrap(),
            transverse_field: F::from(0.5).unwrap(),
        }
    }
}

impl<F: Float> QuantumMeasurement<F> {
    /// Create a new quantum measurement system
    pub fn new() -> InterpolateResult<Self> {
        Ok(Self {
            measurement_operators: Vec::new(),
            measurement_history: VecDeque::new(),
            measurement_bases: Vec::new(),
        })
    }

    /// Add a measurement operator
    pub fn add_measurement_operator(&mut self, operator: MeasurementOperator<F>) {
        self.measurement_operators.push(operator);
    }

    /// Perform measurement with specific operator
    pub fn measure_with_operator(
        &mut self,
        state: &QuantumState<F>,
        operator_name: &str,
    ) -> InterpolateResult<MeasurementResult<F>> {
        // Find the measurement operator
        let operator = self
            .measurement_operators
            .iter()
            .find(|op| op.name == operator_name)
            .ok_or_else(|| {
                crate::error::InterpolateError::invalid_input(format!(
                    "Measurement operator '{}' not found",
                    operator_name
                ))
            })?;

        // Simplified measurement (in practice would involve matrix operations)
        let measurement_value = state.energy; // Simplified
        let uncertainty = operator.precision;

        let result = MeasurementResult {
            value: measurement_value,
            uncertainty,
            basis: operator_name.to_string(),
            timestamp: Instant::now(),
            probability: F::one(),
        };

        self.measurement_history.push_back(result.clone());
        if self.measurement_history.len() > 100 {
            self.measurement_history.pop_front();
        }

        Ok(result)
    }

    /// Get measurement history
    pub fn get_measurement_history(&self) -> &VecDeque<MeasurementResult<F>> {
        &self.measurement_history
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_quantum_optimizer_creation() {
        let optimizer: QuantumParameterOptimizer<f64> = QuantumParameterOptimizer::new().unwrap();
        assert!(optimizer.quantum_operators.is_empty());
        assert!(optimizer.optimization_history.is_empty());
    }

    #[test]
    fn test_quantum_state_creation() {
        let state: QuantumState<f64> = QuantumState::new().unwrap();
        assert!(state.amplitudes.is_empty());
        assert!(state.parameter_values.is_empty());
        assert_eq!(state.energy, 0.0);
    }

    #[test]
    fn test_annealing_parameters_default() {
        let params: AnnealingParameters<f64> = AnnealingParameters::default();
        assert_eq!(params.initial_temperature, 1.0);
        assert_eq!(params.final_temperature, 0.01);
        assert_eq!(params.num_steps, 1000);
    }

    #[test]
    fn test_measurement_probability() {
        let mut state: QuantumState<f64> = QuantumState::new().unwrap();
        state.amplitudes = vec![(0.8, 0.6)]; // |amplitude|² = 0.64 + 0.36 = 1.0

        let prob = state.get_measurement_probability(0);
        assert!((prob - 1.0).abs() < 1e-10);
    }

    #[test]
    fn test_quantum_entropy_calculation() {
        let mut state: QuantumState<f64> = QuantumState::new().unwrap();
        state.amplitudes = vec![(0.707, 0.0), (0.707, 0.0)]; // Equal superposition

        let entropy = state.calculate_entropy();
        assert!(entropy > 0.0); // Should have positive entropy for superposition
    }
}