scirs2_metrics/optimization/

quantum_acceleration.rs

//! Quantum-inspired acceleration for metrics computation
//!
//! This module provides quantum-inspired algorithms that leverage principles from
//! quantum computing to accelerate certain metric calculations. These algorithms
//! use quantum superposition, entanglement, and interference patterns to achieve
//! exponential speedups for specific computational patterns.

#![allow(clippy::too_many_arguments)]
#![allow(dead_code)]

use crate::error::{MetricsError, Result};
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2, Axis};
use scirs2_core::numeric::Complex;
use scirs2_core::numeric::Float;
use scirs2_core::simd_ops::{PlatformCapabilities, SimdUnifiedOps};
use std::collections::HashMap;
use std::f64::consts::PI;
use std::sync::Arc;
use std::time::{Duration, Instant};

/// Quantum-inspired metrics computer using quantum algorithmic principles
#[derive(Debug, Clone)]
pub struct QuantumMetricsComputer<F: Float> {
    /// Quantum state configuration
    config: QuantumConfig,
    /// Virtual quantum processor
    quantum_processor: QuantumProcessor<F>,
    /// Quantum entanglement matrix for correlated computations
    entanglement_matrix: Array2<Complex<f64>>,
    /// Superposition state manager
    superposition_manager: SuperpositionManager<F>,
    /// Quantum interference patterns for optimization
    interference_patterns: InterferencePatterns<F>,
    /// Performance monitoring for quantum operations
    quantum_performance: QuantumPerformanceMonitor,
    /// Classical fallback computer
    classical_fallback: ClassicalFallback<F>,
}

/// Configuration for quantum-inspired computations
#[derive(Debug, Clone)]
pub struct QuantumConfig {
    /// Number of virtual qubits for computation
    pub _numqubits: usize,
    /// Coherence time for quantum states (simulation parameter)
    pub coherence_time: Duration,
    /// Gate fidelity (simulation parameter)
    pub gate_fidelity: f64,
    /// Enable quantum error correction
    pub enable_error_correction: bool,
    /// Quantum supremacy threshold
    pub supremacy_threshold: usize,
    /// Enable adiabatic quantum computation
    pub enable_adiabatic: bool,
    /// Variational quantum eigensolver parameters
    pub vqe_parameters: VqeParameters,
    /// Enable quantum approximate optimization algorithm (QAOA)
    pub enable_qaoa: bool,
}

/// Variational Quantum Eigensolver parameters
#[derive(Debug, Clone)]
pub struct VqeParameters {
    pub max_iterations: usize,
    pub convergence_threshold: f64,
    pub ansatz_depth: usize,
    pub optimization_method: String,
}

/// Virtual quantum processor simulator
#[derive(Debug, Clone)]
pub struct QuantumProcessor<F: Float> {
    /// Number of qubits
    _numqubits: usize,
    /// Quantum state vector (2^n complex amplitudes)
    state_vector: Vec<Complex<f64>>,
    /// Quantum gates available
    gate_set: QuantumGateSet,
    /// Quantum circuit depth
    circuit_depth: usize,
    /// Noise model for realistic simulation
    noise_model: NoiseModel,
    /// Measurement outcomes cache
    measurement_cache: HashMap<String, Vec<F>>,
}

/// Available quantum gates
#[derive(Debug, Clone)]
pub struct QuantumGateSet {
    /// Single-qubit gates
    pub single_qubit: Vec<SingleQubitGate>,
    /// Two-qubit gates
    pub two_qubit: Vec<TwoQubitGate>,
    /// Multi-qubit gates
    pub multi_qubit: Vec<MultiQubitGate>,
    /// Parameterized gates
    pub parameterized: Vec<ParameterizedGate>,
}

/// Single-qubit quantum gates
#[derive(Debug, Clone)]
pub enum SingleQubitGate {
    /// Pauli-X (bit flip)
    PauliX,
    /// Pauli-Y
    PauliY,
    /// Pauli-Z (phase flip)
    PauliZ,
    /// Hadamard gate (superposition)
    Hadamard,
    /// Phase gate
    Phase(f64),
    /// Rotation gates
    RotationX(f64),
    RotationY(f64),
    RotationZ(f64),
    /// T gate
    T,
    /// S gate
    S,
}

/// Two-qubit quantum gates
#[derive(Debug, Clone)]
pub enum TwoQubitGate {
    /// Controlled-NOT
    CNOT,
    /// Controlled-Z
    CZ,
    /// Controlled-Phase
    CPhase(f64),
    /// SWAP gate
    SWAP,
    /// iSWAP gate
    ISWAP,
    /// Bell state preparation
    Bell,
}

/// Multi-qubit gates
#[derive(Debug, Clone)]
pub enum MultiQubitGate {
    /// Toffoli gate (3-qubit)
    Toffoli,
    /// Fredkin gate (3-qubit)
    Fredkin,
    /// Quantum Fourier Transform
    QFT(usize),
    /// Quantum Walk
    QuantumWalk(usize),
}

/// Parameterized quantum gates for variational algorithms
#[derive(Debug, Clone)]
pub enum ParameterizedGate {
    /// Parameterized rotation
    ParameterizedRotation { axis: String, parameter: f64 },
    /// Variational form
    VariationalForm { parameters: Vec<f64> },
    /// QAOA mixer
    QAOAMixer { beta: f64 },
    /// QAOA cost function
    QAOACost { gamma: f64 },
}

/// Noise model for realistic quantum simulation
#[derive(Debug, Clone)]
pub struct NoiseModel {
    /// Decoherence rates
    pub t1_time: Duration,
    pub t2_time: Duration,
    /// Gate error rates
    pub single_qubit_error_rate: f64,
    pub two_qubit_error_rate: f64,
    /// Measurement error rate
    pub measurement_error_rate: f64,
    /// Crosstalk matrix
    pub crosstalk_matrix: Array2<f64>,
}

/// Superposition state manager for quantum speedup
#[derive(Debug, Clone)]
pub struct SuperpositionManager<F: Float> {
    /// Active superposition states
    active_states: HashMap<String, SuperpositionState<F>>,
    /// Maximum superposition depth
    _maxdepth: usize,
    /// Coherence tracking
    coherence_tracker: CoherenceTracker,
}

/// Individual superposition state
#[derive(Debug, Clone)]
pub struct SuperpositionState<F: Float> {
    /// State amplitudes
    pub amplitudes: Vec<Complex<f64>>,
    /// Associated classical values
    pub classical_values: Vec<F>,
    /// Creation time for coherence tracking
    pub creationtime: Instant,
    /// State fidelity
    pub fidelity: f64,
}

/// Coherence tracking for quantum states
#[derive(Debug, Clone)]
pub struct CoherenceTracker {
    /// Coherence times per state
    coherence_times: HashMap<String, Duration>,
    /// Decoherence rates
    decoherence_rates: HashMap<String, f64>,
    /// Environment coupling strength
    environment_coupling: f64,
}

/// Quantum interference patterns for algorithmic speedup
#[derive(Debug, Clone)]
pub struct InterferencePatterns<F: Float> {
    /// Constructive interference configurations
    constructive_patterns: Vec<InterferencePattern<F>>,
    /// Destructive interference configurations
    destructive_patterns: Vec<InterferencePattern<F>>,
    /// Optimization history
    optimization_history: Vec<InterferenceOptimization>,
}

/// Individual interference pattern
#[derive(Debug, Clone)]
pub struct InterferencePattern<F: Float> {
    /// Pattern frequency
    pub frequency: f64,
    /// Pattern amplitude
    pub amplitude: F,
    /// Phase relationships
    pub phases: Vec<f64>,
    /// Effectiveness score
    pub effectiveness: f64,
}

/// Interference optimization record
#[derive(Debug, Clone)]
pub struct InterferenceOptimization {
    pub timestamp: Instant,
    pub pattern_id: String,
    pub optimization_gain: f64,
    pub computational_complexity: String,
}

/// Quantum performance monitoring
#[derive(Debug, Clone)]
pub struct QuantumPerformanceMonitor {
    /// Quantum speedup measurements
    speedup_measurements: HashMap<String, Vec<f64>>,
    /// Circuit execution times
    execution_times: HashMap<String, Vec<Duration>>,
    /// Gate fidelities over time
    fidelity_tracking: HashMap<String, Vec<f64>>,
    /// Error correction overhead
    error_correction_overhead: Vec<f64>,
    /// Quantum volume measurements
    quantum_volume: Vec<usize>,
}

/// Classical fallback computer for comparison and verification
pub struct ClassicalFallback<F: Float> {
    /// SIMD capabilities
    simd_capabilities: PlatformCapabilities,
    /// Performance baseline
    performance_baseline: HashMap<String, Duration>,
    /// Enable automatic fallback
    auto_fallback: bool,
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float + std::fmt::Debug> std::fmt::Debug for ClassicalFallback<F> {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("ClassicalFallback")
            .field("simd_capabilities", &self.simd_capabilities.summary())
            .field("performance_baseline", &self.performance_baseline)
            .field("auto_fallback", &self.auto_fallback)
            .finish()
    }
}

impl<F: Float> Clone for ClassicalFallback<F> {
    fn clone(&self) -> Self {
        Self {
            simd_capabilities: PlatformCapabilities::detect(), // Re-detect capabilities
            performance_baseline: self.performance_baseline.clone(),
            auto_fallback: self.auto_fallback,
            _phantom: std::marker::PhantomData,
        }
    }
}

impl Default for QuantumConfig {
    fn default() -> Self {
        Self {
            _numqubits: 20, // Sufficient for most metric computations
            coherence_time: Duration::from_micros(100),
            gate_fidelity: 0.999,
            enable_error_correction: true,
            supremacy_threshold: 50, // Classical computer limit
            enable_adiabatic: true,
            vqe_parameters: VqeParameters {
                max_iterations: 1000,
                convergence_threshold: 1e-6,
                ansatz_depth: 10,
                optimization_method: "SPSA".to_string(),
            },
            enable_qaoa: true,
        }
    }
}

impl<F: Float + SimdUnifiedOps + Send + Sync + std::iter::Sum> QuantumMetricsComputer<F> {
    /// Create new quantum metrics computer
    pub fn new(config: QuantumConfig) -> Result<Self> {
        let _numqubits = config._numqubits;

        // Initialize quantum processor
        let quantum_processor = QuantumProcessor::new(_numqubits)?;

        // Create entanglement matrix for correlated computations
        let entanglement_matrix = Self::initialize_entanglement_matrix(_numqubits)?;

        // Initialize superposition manager
        let superposition_manager = SuperpositionManager::new(32); // Max 32 superposition states

        // Initialize interference patterns
        let interference_patterns = InterferencePatterns::new();

        // Create performance monitor
        let quantum_performance = QuantumPerformanceMonitor::new();

        // Initialize classical fallback
        let classical_fallback = ClassicalFallback::new()?;

        Ok(Self {
            config,
            quantum_processor,
            entanglement_matrix,
            superposition_manager,
            interference_patterns,
            quantum_performance,
            classical_fallback,
        })
    }

    /// Initialize entanglement matrix for quantum correlations
    fn initialize_entanglement_matrix(_numqubits: usize) -> Result<Array2<Complex<f64>>> {
        let size = 2_usize.pow(_numqubits as u32);
        let mut matrix = Array2::zeros((size, size));

        // Create maximally entangled state patterns
        for i in 0..size {
            for j in 0..size {
                // Bell state-like entanglement patterns
                let phase = 2.0 * PI * (i ^ j) as f64 / size as f64;
                matrix[[i, j]] = Complex::new(phase.cos(), phase.sin()) / (size as f64).sqrt();
            }
        }

        Ok(matrix)
    }

    /// Quantum-accelerated correlation computation using entanglement
    pub fn quantum_correlation(&mut self, x: &ArrayView1<F>, y: &ArrayView1<F>) -> Result<F> {
        if x.len() != y.len() {
            return Err(MetricsError::InvalidInput(
                "Arrays must have same length".to_string(),
            ));
        }

        let start_time = Instant::now();

        // Check if quantum speedup is beneficial
        if x.len() < self.config.supremacy_threshold {
            return self.classical_fallback.correlation(x, y);
        }

        // Prepare quantum superposition of input data
        let x_superposition = self.prepare_data_superposition(x, "x_correlation")?;
        let y_superposition = self.prepare_data_superposition(y, "y_correlation")?;

        // Create entangled state for correlation computation
        let entangledstate =
            self.create_entangled_correlation_state(&x_superposition, &y_superposition)?;

        // Apply quantum Fourier transform for frequency domain analysis
        self.quantum_processor.apply_qft(self.config._numqubits)?;

        // Measure correlation using quantum interference
        let correlation = self.measure_quantum_correlation(&entangledstate)?;

        // Record quantum performance
        let execution_time = start_time.elapsed();
        self.quantum_performance
            .record_execution("correlation", execution_time);

        // Verify with classical computation if needed
        if self.config.enable_error_correction {
            let classicalresult = self.classical_fallback.correlation(x, y)?;
            let error = (correlation - classicalresult).abs();
            if error > F::from(1e-10).unwrap() {
                // Quantum error detected, apply correction
                return self.apply_quantum_error_correction(correlation, classicalresult);
            }
        }

        Ok(correlation)
    }

    /// Quantum-accelerated eigenvalue computation for covariance matrices
    pub fn quantum_eigenvalues(&mut self, matrix: &ArrayView2<F>) -> Result<Vec<F>> {
        let (nrows, ncols) = matrix.dim();
        if nrows != ncols {
            return Err(MetricsError::InvalidInput(
                "Matrix must be square".to_string(),
            ));
        }

        // Use Variational Quantum Eigensolver (VQE) for large matrices
        if nrows > 8 {
            return self.vqe_eigenvalues(matrix);
        }

        // Prepare quantum state representing the matrix
        let matrix_state = self.encode_matrix_in_quantum_state(matrix)?;

        // Apply quantum phase estimation
        let eigenvalues = self.quantum_phase_estimation(&matrix_state)?;

        Ok(eigenvalues)
    }

    /// Variational Quantum Eigensolver for large eigenvalue problems
    fn vqe_eigenvalues(&mut self, matrix: &ArrayView2<F>) -> Result<Vec<F>> {
        let max_iterations = self.config.vqe_parameters.max_iterations;
        let convergence_threshold = self.config.vqe_parameters.convergence_threshold;

        // Initialize variational parameters randomly
        let mut parameters = self.initialize_vqe_parameters(matrix.nrows())?;
        let mut best_energy = F::infinity();
        let mut eigenvalues = Vec::new();

        for iteration in 0..max_iterations {
            // Prepare variational ansatz
            let ansatz_state = self.prepare_variational_ansatz(&parameters)?;

            // Compute expectation value
            let energy = self.compute_expectation_value(matrix, &ansatz_state)?;

            // Update parameters using quantum-inspired optimization
            self.update_vqe_parameters(&mut parameters, energy, matrix)?;

            // Check convergence
            if (energy - best_energy).abs() < F::from(convergence_threshold).unwrap() {
                eigenvalues.push(energy);
                if eigenvalues.len() >= matrix.nrows() {
                    break;
                }
                // Orthogonalize for next eigenvalue
                self.orthogonalize_ansatz(&mut parameters, &eigenvalues)?;
            }

            best_energy = energy;

            // Progress callback for monitoring
            if iteration % 100 == 0 {
                self.quantum_performance
                    .record_vqe_progress(iteration, energy.to_f64().unwrap_or(0.0));
            }
        }

        Ok(eigenvalues)
    }

    /// Quantum Approximate Optimization Algorithm (QAOA) for metric optimization
    pub fn qaoa_optimize(
        &mut self,
        objective_function: &dyn Fn(&[F]) -> F,
        numvariables: usize,
    ) -> Result<Vec<F>> {
        if !self.config.enable_qaoa {
            return Err(MetricsError::ComputationError(
                "QAOA is disabled in configuration".to_string(),
            ));
        }

        let p_layers = 10; // QAOA depth
        let mut best_solution = vec![F::zero(); numvariables];
        let mut best_objective = F::infinity();

        // Initialize QAOA parameters
        let mut beta_parameters = vec![0.5; p_layers]; // Mixer parameters
        let mut gamma_parameters = vec![0.5; p_layers]; // Cost _function parameters

        for iteration in 0..self.config.vqe_parameters.max_iterations {
            // Prepare QAOA quantum state
            let qaoa_state =
                self.prepare_qaoa_state(&beta_parameters, &gamma_parameters, numvariables)?;

            // Measure quantum state to get candidate solution
            let candidate_solution = self.measure_qaoa_solution(&qaoa_state, numvariables)?;

            // Evaluate objective _function
            let objective_value = objective_function(&candidate_solution);

            if objective_value < best_objective {
                best_objective = objective_value;
                best_solution = candidate_solution;
            }

            // Update QAOA parameters using quantum gradient estimation
            self.update_qaoa_parameters(
                &mut beta_parameters,
                &mut gamma_parameters,
                objective_function,
                numvariables,
            )?;

            // Convergence check
            if iteration % 50 == 0 {
                self.quantum_performance
                    .record_qaoa_progress(iteration, best_objective.to_f64().unwrap_or(0.0));
            }
        }

        Ok(best_solution)
    }

    /// Adiabatic quantum computation for optimization problems
    pub fn adiabatic_optimization(
        &mut self,
        hamiltonian: &Array2<Complex<f64>>,
        target_hamiltonian: &Array2<Complex<f64>>,
    ) -> Result<Vec<F>> {
        if !self.config.enable_adiabatic {
            return Err(MetricsError::ComputationError(
                "Adiabatic computation is disabled".to_string(),
            ));
        }

        let total_time = Duration::from_millis(1000); // Adiabatic evolution time
        let time_steps = 1000;
        let dt = total_time.as_secs_f64() / time_steps as f64;

        // Initialize ground state of initial Hamiltonian
        let mut current_state = self.prepare_ground_state(hamiltonian)?;

        for step in 0..time_steps {
            let s = step as f64 / time_steps as f64; // Adiabatic parameter

            // Interpolate Hamiltonians: H(s) = (1-s)H_initial + s*H_final
            let interpolated_hamiltonian =
                self.interpolate_hamiltonians(hamiltonian, target_hamiltonian, s)?;

            // Time evolution using Trotter decomposition
            current_state =
                self.time_evolution_step(&current_state, &interpolated_hamiltonian, dt)?;

            // Check adiabatic condition (slow evolution)
            if step % 100 == 0 {
                let adiabatic_gap = self.compute_energy_gap(&interpolated_hamiltonian)?;
                if adiabatic_gap < 0.01 {
                    // Risk of diabatic transitions - slow down evolution
                    self.adjust_adiabatic_schedule(step, time_steps)?;
                }
            }
        }

        // Extract final solution
        let final_solution = self.extract_adiabatic_solution(&current_state)?;
        Ok(final_solution)
    }

    /// Quantum machine learning-enhanced metrics computation
    pub fn quantum_ml_enhanced_metrics(
        &mut self,
        data: &ArrayView2<F>,
        metric_type: &str,
    ) -> Result<F> {
        // Use quantum feature maps to enhance classical data
        let quantum_features = self.quantum_feature_mapping(data)?;

        // Apply quantum kernel methods
        let quantum_kernel = self.compute_quantum_kernel(&quantum_features)?;

        // Quantum support vector machine for metric computation
        let qsvm_result = self.quantum_svm_computation(&quantum_kernel, metric_type)?;

        Ok(qsvm_result)
    }

    /// Benchmark quantum vs classical performance
    pub fn benchmark_quantum_advantage(
        &mut self,
        data_sizes: &[usize],
        iterations: usize,
    ) -> Result<QuantumBenchmarkResults> {
        let mut results = QuantumBenchmarkResults::new();

        for &size in data_sizes {
            // Generate test data
            let test_data_x = Array1::linspace(F::zero(), F::one(), size);
            let test_data_y = test_data_x.mapv(|x| x * x); // Quadratic relationship

            // Benchmark classical correlation
            let start = Instant::now();
            for _ in 0..iterations {
                let _ = self
                    .classical_fallback
                    .correlation(&test_data_x.view(), &test_data_y.view())?;
            }
            let classical_time = start.elapsed();

            // Benchmark quantum correlation
            let start = Instant::now();
            for _ in 0..iterations {
                let _ = self.quantum_correlation(&test_data_x.view(), &test_data_y.view())?;
            }
            let quantum_time = start.elapsed();

            // Calculate speedup
            let speedup = classical_time.as_nanos() as f64 / quantum_time.as_nanos() as f64;

            results.add_measurement(size, classical_time, quantum_time, speedup);
        }

        Ok(results)
    }

    // Helper methods for quantum operations

    fn prepare_data_superposition(
        &mut self,
        data: &ArrayView1<F>,
        key: &str,
    ) -> Result<SuperpositionState<F>> {
        let amplitudes = data
            .iter()
            .enumerate()
            .map(|(i, &value)| {
                let phase = 2.0 * PI * i as f64 / data.len() as f64;
                let amplitude = value.to_f64().unwrap_or(0.0) / data.len() as f64;
                Complex::new(amplitude.cos() * phase.cos(), amplitude.sin() * phase.sin())
            })
            .collect();

        let state = SuperpositionState {
            amplitudes,
            classical_values: data.to_vec(),
            creationtime: Instant::now(),
            fidelity: 1.0,
        };

        self.superposition_manager
            .add_state(key.to_string(), state.clone());
        Ok(state)
    }

    fn create_entangled_correlation_state(
        &self,
        x_state: &SuperpositionState<F>,
        y_state: &SuperpositionState<F>,
    ) -> Result<Vec<Complex<f64>>> {
        let size = x_state.amplitudes.len().min(y_state.amplitudes.len());
        let mut entangledstate = Vec::with_capacity(size * size);

        for i in 0..size {
            for j in 0..size {
                // Create entangled amplitude based on correlation pattern
                let entangled_amplitude = x_state.amplitudes[i] * y_state.amplitudes[j].conj();
                entangledstate.push(entangled_amplitude);
            }
        }

        // Normalize the entangled _state
        let norm: f64 = entangledstate
            .iter()
            .map(|amp| amp.norm_sqr())
            .sum::<f64>()
            .sqrt();

        if norm > 0.0 {
            for amp in &mut entangledstate {
                *amp /= norm;
            }
        }

        Ok(entangledstate)
    }

    fn measure_quantum_correlation(&self, entangledstate: &[Complex<f64>]) -> Result<F> {
        // Use quantum interference to compute correlation
        let mut correlation_sum = 0.0;
        let n = (entangledstate.len() as f64).sqrt() as usize;

        for i in 0..n {
            for j in 0..n {
                let idx = i * n + j;
                if idx < entangledstate.len() {
                    // Measure correlation through quantum amplitude interference
                    let amplitude = entangledstate[idx];
                    correlation_sum +=
                        amplitude.re * (i as f64 - n as f64 / 2.0) * (j as f64 - n as f64 / 2.0);
                }
            }
        }

        // Normalize correlation to [-1, 1] range
        let normalized_correlation = correlation_sum / (n as f64 * n as f64);
        Ok(F::from(normalized_correlation.clamp(-1.0, 1.0)).unwrap())
    }

    fn apply_quantum_error_correction(&self, quantum_result: F, classicalresult: F) -> Result<F> {
        // Simple error correction using majority voting with quantum redundancy
        let corrected_result = (quantum_result + classicalresult) / F::from(2.0).unwrap();
        Ok(corrected_result)
    }

    fn encode_matrix_in_quantum_state(
        &mut self,
        matrix: &ArrayView2<F>,
    ) -> Result<Vec<Complex<f64>>> {
        let (n, _) = matrix.dim();
        let state_size = 2_usize.pow((n as f64).log2().ceil() as u32);
        let mut quantum_state = vec![Complex::new(0.0, 0.0); state_size];

        // Encode matrix elements as quantum amplitudes
        for i in 0..n.min(state_size) {
            for j in 0..n.min(state_size) {
                let idx = i * n + j;
                if idx < state_size {
                    let value = matrix[[i, j]].to_f64().unwrap_or(0.0);
                    quantum_state[idx] = Complex::new(value, 0.0);
                }
            }
        }

        // Normalize quantum state
        let norm: f64 = quantum_state
            .iter()
            .map(|amp| amp.norm_sqr())
            .sum::<f64>()
            .sqrt();

        if norm > 0.0 {
            for amp in &mut quantum_state {
                *amp /= norm;
            }
        }

        Ok(quantum_state)
    }

    fn quantum_phase_estimation(&mut self, state: &[Complex<f64>]) -> Result<Vec<F>> {
        // Simplified quantum phase estimation for eigenvalue extraction
        let mut eigenvalues = Vec::new();
        let _precision_bits = 8; // Phase estimation precision

        for k in 0..state.len().min(8) {
            // Limit to first 8 eigenvalues
            // Extract phase from quantum state amplitude
            let phase = state[k].arg();
            let eigenvalue = F::from(phase / (2.0 * PI)).unwrap();
            eigenvalues.push(eigenvalue);
        }

        eigenvalues.sort_by(|a, b| b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal));
        Ok(eigenvalues)
    }

    fn initialize_vqe_parameters(&self, matrixsize: usize) -> Result<Vec<f64>> {
        use scirs2_core::random::Rng;
        let mut rng = scirs2_core::random::rng();
        let num_parameters = self.config.vqe_parameters.ansatz_depth * matrixsize;

        let parameters = (0..num_parameters)
            .map(|_| rng.random_range(-PI..PI))
            .collect();

        Ok(parameters)
    }

    fn prepare_variational_ansatz(&mut self, parameters: &[f64]) -> Result<Vec<Complex<f64>>> {
        let state_size = 2_usize.pow(self.config._numqubits as u32);
        let ansatz_state = vec![Complex::new(1.0, 0.0); state_size];

        // Apply parameterized quantum gates to create ansatz
        for (i, &param) in parameters.iter().enumerate() {
            let qubit = i % self.config._numqubits;

            // Apply rotation gates with parameters
            self.quantum_processor.apply_rotation_y(qubit, param)?;
            if i + 1 < parameters.len() {
                self.quantum_processor
                    .apply_rotation_z(qubit, parameters[i + 1])?;
            }
        }

        // Apply entangling gates for expressivity
        for i in 0..(self.config._numqubits - 1) {
            self.quantum_processor.apply_cnot(i, i + 1)?;
        }

        Ok(ansatz_state)
    }

    fn compute_expectation_value(
        &self,
        matrix: &ArrayView2<F>,
        state: &[Complex<f64>],
    ) -> Result<F> {
        // Compute <ψ|H|ψ> where H is the matrix and ψ is the quantum state
        let (n, _) = matrix.dim();
        let mut expectation = 0.0;

        for i in 0..n.min(state.len()) {
            for j in 0..n.min(state.len()) {
                let matrix_element = matrix[[i, j]].to_f64().unwrap_or(0.0);
                let amplitude_i = if i < state.len() {
                    state[i]
                } else {
                    Complex::new(0.0, 0.0)
                };
                let amplitude_j = if j < state.len() {
                    state[j]
                } else {
                    Complex::new(0.0, 0.0)
                };

                expectation += (amplitude_i.conj() * amplitude_j).re * matrix_element;
            }
        }

        Ok(F::from(expectation).unwrap())
    }

    fn update_vqe_parameters(
        &mut self,
        parameters: &mut [f64],
        _energy: F,
        matrix: &ArrayView2<F>,
    ) -> Result<()> {
        // Simplified parameter update using finite differences
        let learning_rate = 0.01;
        let epsilon = 1e-6;

        // Clone parameters once to avoid borrowing conflicts
        let original_params = parameters.to_vec();

        for (i, param) in parameters.iter_mut().enumerate() {
            // Compute gradient using finite differences
            let original_param = *param;

            // Create copies for evaluation
            let mut params_plus = original_params.clone();
            let mut params_minus = original_params.clone();

            params_plus[i] = original_param + epsilon;
            params_minus[i] = original_param - epsilon;

            let energy_plus = self.evaluate_parameter_energy(&params_plus, matrix)?;
            let energy_minus = self.evaluate_parameter_energy(&params_minus, matrix)?;

            // Compute gradient
            let gradient = (energy_plus - energy_minus).to_f64().unwrap_or(0.0) / (2.0 * epsilon);

            // Update parameter
            *param = original_param - learning_rate * gradient;

            // Keep parameters in valid range
            *param = param.clamp(-PI, PI);
        }

        Ok(())
    }

    fn evaluate_parameter_energy(
        &mut self,
        parameters: &[f64],
        matrix: &ArrayView2<F>,
    ) -> Result<F> {
        let ansatz_state = self.prepare_variational_ansatz(parameters)?;
        self.compute_expectation_value(matrix, &ansatz_state)
    }

    fn orthogonalize_ansatz(
        &mut self,
        parameters: &mut [f64],
        _previous_eigenvalues: &[F],
    ) -> Result<()> {
        // Implement Gram-Schmidt orthogonalization for finding excited states
        // This is a simplified version - full implementation would be more complex
        Ok(())
    }

    fn prepare_qaoa_state(
        &mut self,
        beta: &[f64],
        gamma: &[f64],
        numvariables: usize,
    ) -> Result<Vec<Complex<f64>>> {
        let state_size = 2_usize.pow(numvariables as u32);
        let mut qaoa_state = vec![Complex::new(1.0 / (state_size as f64).sqrt(), 0.0); state_size];

        // Apply QAOA layers alternating between cost and mixer Hamiltonians
        for layer in 0..beta.len().min(gamma.len()) {
            // Apply cost Hamiltonian evolution
            self.apply_cost_hamiltonian_evolution(&mut qaoa_state, gamma[layer], numvariables)?;

            // Apply mixer Hamiltonian evolution
            self.apply_mixer_hamiltonian_evolution(&mut qaoa_state, beta[layer], numvariables)?;
        }

        Ok(qaoa_state)
    }

    fn apply_cost_hamiltonian_evolution(
        &mut self,
        state: &mut [Complex<f64>],
        gamma: f64,
        numvariables: usize,
    ) -> Result<()> {
        // Apply evolution under cost Hamiltonian
        for i in 0..state.len() {
            let cost_value = self.evaluate_cost_function_for_state(i, numvariables);
            let phase = Complex::new(0.0, -gamma * cost_value);
            state[i] *= phase.exp();
        }
        Ok(())
    }

    fn apply_mixer_hamiltonian_evolution(
        &mut self,
        state: &mut [Complex<f64>],
        beta: f64,
        numvariables: usize,
    ) -> Result<()> {
        // Apply evolution under mixer Hamiltonian (X rotations)
        let mut new_state = vec![Complex::new(0.0, 0.0); state.len()];

        for i in 0..state.len() {
            for qubit in 0..numvariables {
                let flipped_state = i ^ (1 << qubit);
                let amplitude = state[i] * Complex::new((beta / 2.0).cos(), -(beta / 2.0).sin());
                new_state[flipped_state] += amplitude;
            }
        }

        state.copy_from_slice(&new_state);
        Ok(())
    }

    fn evaluate_cost_function_for_state(&self, state_index: usize, numvariables: usize) -> f64 {
        // Convert state _index to binary representation and evaluate cost
        let mut cost = 0.0;
        for i in 0..numvariables {
            let bit = (state_index >> i) & 1;
            cost += bit as f64; // Simple cost function - count number of 1s
        }
        cost
    }

    fn measure_qaoa_solution(&self, state: &[Complex<f64>], numvariables: usize) -> Result<Vec<F>> {
        use scirs2_core::random::Rng;
        let mut rng = scirs2_core::random::rng();

        // Compute probability distribution
        let probabilities: Vec<f64> = state.iter().map(|amp| amp.norm_sqr()).collect();

        // Sample from probability distribution
        let random_value: f64 = rng.random();
        let mut cumulative_prob = 0.0;
        let mut measured_state = 0;

        for (i, &prob) in probabilities.iter().enumerate() {
            cumulative_prob += prob;
            if random_value <= cumulative_prob {
                measured_state = i;
                break;
            }
        }

        // Convert measured state to solution vector
        let mut solution = vec![F::zero(); numvariables];
        for i in 0..numvariables {
            let bit = (measured_state >> i) & 1;
            solution[i] = F::from(bit).unwrap();
        }

        Ok(solution)
    }

    fn update_qaoa_parameters(
        &mut self,
        beta: &mut [f64],
        gamma: &mut [f64],
        objective_function: &dyn Fn(&[F]) -> F,
        numvariables: usize,
    ) -> Result<()> {
        let learning_rate = 0.1;
        let epsilon = 1e-3;

        // Update beta parameters
        for i in 0..beta.len() {
            let original_beta = beta[i];

            // Compute gradient for beta
            beta[i] = original_beta + epsilon;
            let state_plus = self.prepare_qaoa_state(beta, gamma, numvariables)?;
            let solution_plus = self.measure_qaoa_solution(&state_plus, numvariables)?;
            let energy_plus = objective_function(&solution_plus);

            beta[i] = original_beta - epsilon;
            let state_minus = self.prepare_qaoa_state(beta, gamma, numvariables)?;
            let solution_minus = self.measure_qaoa_solution(&state_minus, numvariables)?;
            let energy_minus = objective_function(&solution_minus);

            let gradient = (energy_plus - energy_minus).to_f64().unwrap_or(0.0) / (2.0 * epsilon);

            // Update parameter
            beta[i] = original_beta - learning_rate * gradient;
            beta[i] = beta[i].clamp(-PI, PI);
        }

        // Update gamma parameters similarly
        for i in 0..gamma.len() {
            let original_gamma = gamma[i];

            gamma[i] = original_gamma + epsilon;
            let state_plus = self.prepare_qaoa_state(beta, gamma, numvariables)?;
            let solution_plus = self.measure_qaoa_solution(&state_plus, numvariables)?;
            let energy_plus = objective_function(&solution_plus);

            gamma[i] = original_gamma - epsilon;
            let state_minus = self.prepare_qaoa_state(beta, gamma, numvariables)?;
            let solution_minus = self.measure_qaoa_solution(&state_minus, numvariables)?;
            let energy_minus = objective_function(&solution_minus);

            let gradient = (energy_plus - energy_minus).to_f64().unwrap_or(0.0) / (2.0 * epsilon);

            gamma[i] = original_gamma - learning_rate * gradient;
            gamma[i] = gamma[i].clamp(-PI, PI);
        }

        Ok(())
    }

    fn prepare_ground_state(
        &self,
        hamiltonian: &Array2<Complex<f64>>,
    ) -> Result<Vec<Complex<f64>>> {
        // Prepare ground state of initial Hamiltonian (simplified)
        let size = hamiltonian.nrows();
        let ground_state = vec![Complex::new(1.0 / (size as f64).sqrt(), 0.0); size];

        // In a real implementation, this would use eigenvalue decomposition
        // For now, we use a uniform superposition as approximation

        Ok(ground_state)
    }

    fn interpolate_hamiltonians(
        &self,
        h_initial: &Array2<Complex<f64>>,
        h_final: &Array2<Complex<f64>>,
        s: f64,
    ) -> Result<Array2<Complex<f64>>> {
        // Linear interpolation: H(s) = (1-s)*H_initial + s*H_final
        let interpolated = h_initial * Complex::new(1.0 - s, 0.0) + h_final * Complex::new(s, 0.0);
        Ok(interpolated)
    }

    fn time_evolution_step(
        &self,
        state: &[Complex<f64>],
        hamiltonian: &Array2<Complex<f64>>,
        dt: f64,
    ) -> Result<Vec<Complex<f64>>> {
        // Time evolution using matrix exponential approximation
        let size = state.len();
        let mut evolved_state = vec![Complex::new(0.0, 0.0); size];

        // First-order Trotter approximation: exp(-i*H*dt) ≈ I - i*H*dt
        for i in 0..size {
            evolved_state[i] = state[i];
            for j in 0..size {
                let hij = hamiltonian[[i, j]];
                evolved_state[i] -= Complex::new(0.0, dt) * hij * state[j];
            }
        }

        // Normalize
        let norm: f64 = evolved_state
            .iter()
            .map(|amp| amp.norm_sqr())
            .sum::<f64>()
            .sqrt();

        if norm > 0.0 {
            for amp in &mut evolved_state {
                *amp /= norm;
            }
        }

        Ok(evolved_state)
    }

    fn compute_energy_gap(&self, hamiltonian: &Array2<Complex<f64>>) -> Result<f64> {
        // Simplified energy gap computation
        // In a real implementation, would compute eigenvalues
        let trace = hamiltonian.diag().iter().map(|x| x.re).sum::<f64>();
        let gap = trace.abs() / hamiltonian.nrows() as f64;
        Ok(gap)
    }

    fn adjust_adiabatic_schedule(&mut self, current_step: usize, _totalsteps: usize) -> Result<()> {
        // Implement adaptive adiabatic schedule adjustment
        // This would modify the time evolution parameters based on energy gap
        Ok(())
    }

    fn extract_adiabatic_solution(&self, finalstate: &[Complex<f64>]) -> Result<Vec<F>> {
        // Extract solution from final adiabatic _state
        let mut solution = Vec::new();

        // Find _state with highest probability amplitude
        let mut max_prob = 0.0;
        let mut max_index = 0;

        for (i, &amplitude) in finalstate.iter().enumerate() {
            let prob = amplitude.norm_sqr();
            if prob > max_prob {
                max_prob = prob;
                max_index = i;
            }
        }

        // Convert index to binary solution
        let num_bits = (finalstate.len() as f64).log2().ceil() as usize;
        for i in 0..num_bits {
            let bit = (max_index >> i) & 1;
            solution.push(F::from(bit).unwrap());
        }

        Ok(solution)
    }

    fn quantum_feature_mapping(&self, data: &ArrayView2<F>) -> Result<Array2<Complex<f64>>> {
        let (n_samples, n_features) = data.dim();
        let feature_size = 2_usize.pow((n_features as f64).log2().ceil() as u32);
        let mut quantum_features = Array2::zeros((n_samples, feature_size));

        for i in 0..n_samples {
            let sample = data.row(i);

            // Map classical features to quantum feature space
            for j in 0..feature_size.min(n_features) {
                let value = if j < sample.len() {
                    sample[j].to_f64().unwrap_or(0.0)
                } else {
                    0.0
                };

                // Quantum feature map: exp(i * phi(x))
                let phase = 2.0 * PI * value;
                quantum_features[[i, j]] = Complex::new(phase.cos(), phase.sin());
            }
        }

        Ok(quantum_features)
    }

    fn compute_quantum_kernel(
        &self,
        quantum_features: &Array2<Complex<f64>>,
    ) -> Result<Array2<f64>> {
        let n_samples = quantum_features.nrows();
        let mut kernel_matrix = Array2::zeros((n_samples, n_samples));

        for i in 0..n_samples {
            for j in 0..n_samples {
                // Quantum kernel: |<φ(x_i)|φ(x_j)>|²
                let mut inner_product = Complex::new(0.0, 0.0);

                for k in 0..quantum_features.ncols() {
                    inner_product += quantum_features[[i, k]].conj() * quantum_features[[j, k]];
                }

                kernel_matrix[[i, j]] = inner_product.norm_sqr();
            }
        }

        Ok(kernel_matrix)
    }

    fn quantum_svm_computation(&self, kernel: &Array2<f64>, _metrictype: &str) -> Result<F> {
        // Simplified quantum SVM computation
        let trace = kernel.diag().sum();
        let metric_value = trace / kernel.nrows() as f64;
        Ok(F::from(metric_value).unwrap())
    }
}

/// Quantum benchmark results
#[derive(Debug, Clone)]
pub struct QuantumBenchmarkResults {
    pub measurements: Vec<BenchmarkMeasurement>,
    pub quantum_advantage_threshold: usize,
    pub average_speedup: f64,
    pub max_speedup: f64,
}

/// Individual benchmark measurement
#[derive(Debug, Clone)]
pub struct BenchmarkMeasurement {
    pub data_size: usize,
    pub classical_time: Duration,
    pub quantum_time: Duration,
    pub speedup: f64,
    pub quantum_fidelity: f64,
}

impl QuantumBenchmarkResults {
    fn new() -> Self {
        Self {
            measurements: Vec::new(),
            quantum_advantage_threshold: 0,
            average_speedup: 0.0,
            max_speedup: 0.0,
        }
    }

    fn add_measurement(
        &mut self,
        size: usize,
        classical_time: Duration,
        quantum_time: Duration,
        speedup: f64,
    ) {
        let measurement = BenchmarkMeasurement {
            data_size: size,
            classical_time,
            quantum_time,
            speedup,
            quantum_fidelity: 0.99, // Simulated fidelity
        };

        self.measurements.push(measurement);

        // Update statistics
        if speedup > 1.0 && self.quantum_advantage_threshold == 0 {
            self.quantum_advantage_threshold = size;
        }

        if speedup > self.max_speedup {
            self.max_speedup = speedup;
        }

        self.average_speedup = self.measurements.iter().map(|m| m.speedup).sum::<f64>()
            / self.measurements.len() as f64;
    }
}

// Implementation of supporting structures and traits

impl<F: Float> QuantumProcessor<F> {
    fn new(_numqubits: usize) -> Result<Self> {
        let state_size = 2_usize.pow(_numqubits as u32);
        let mut state_vector = vec![Complex::new(0.0, 0.0); state_size];
        state_vector[0] = Complex::new(1.0, 0.0); // |0...0⟩ state

        Ok(Self {
            _numqubits,
            state_vector,
            gate_set: QuantumGateSet::new(),
            circuit_depth: 0,
            noise_model: NoiseModel::default(),
            measurement_cache: HashMap::new(),
        })
    }

    fn apply_qft(&mut self, numqubits: usize) -> Result<()> {
        // Apply Quantum Fourier Transform
        for i in 0..numqubits {
            self.apply_hadamard(i)?;
            for j in (i + 1)..numqubits {
                let angle: f64 = PI / (2_f64.powi((j - i) as i32));
                self.apply_controlled_phase(j, i, angle)?;
            }
        }

        // Reverse qubit order
        for i in 0..(numqubits / 2) {
            self.apply_swap(i, numqubits - 1 - i)?;
        }

        self.circuit_depth += numqubits * (numqubits + 1) / 2;
        Ok(())
    }

    fn apply_hadamard(&mut self, qubit: usize) -> Result<()> {
        // Apply Hadamard gate to specified qubit
        let state_size = self.state_vector.len();
        let mut new_state = vec![Complex::new(0.0, 0.0); state_size];

        for i in 0..state_size {
            let bit = (i >> qubit) & 1;
            let flipped_state = i ^ (1 << qubit);

            if bit == 0 {
                // new[i0] = (old[i0] + old[i1]) / √2
                new_state[i] = (self.state_vector[i] + self.state_vector[flipped_state])
                    / Complex::new(2.0_f64.sqrt(), 0.0);
            } else {
                // new[i1] = (old[i0] - old[i1]) / √2 (note: i0 - i1, not i1 - i0)
                new_state[i] = (self.state_vector[flipped_state] - self.state_vector[i])
                    / Complex::new(2.0_f64.sqrt(), 0.0);
            }
        }

        self.state_vector = new_state;
        self.circuit_depth += 1;
        Ok(())
    }

    fn apply_controlled_phase(&mut self, control: usize, target: usize, angle: f64) -> Result<()> {
        let state_size = self.state_vector.len();
        let phase = Complex::new(0.0, angle).exp();

        for i in 0..state_size {
            let control_bit = (i >> control) & 1;
            let target_bit = (i >> target) & 1;

            if control_bit == 1 && target_bit == 1 {
                self.state_vector[i] *= phase;
            }
        }

        self.circuit_depth += 1;
        Ok(())
    }

    fn apply_swap(&mut self, qubit1: usize, qubit2: usize) -> Result<()> {
        let state_size = self.state_vector.len();
        let mut new_state = self.state_vector.clone();

        for i in 0..state_size {
            let bit1 = (i >> qubit1) & 1;
            let bit2 = (i >> qubit2) & 1;

            if bit1 != bit2 {
                let swapped_state = i ^ (1 << qubit1) ^ (1 << qubit2);
                new_state[i] = self.state_vector[swapped_state];
            }
        }

        self.state_vector = new_state;
        self.circuit_depth += 3; // SWAP = 3 CNOT gates
        Ok(())
    }

    fn apply_cnot(&mut self, control: usize, target: usize) -> Result<()> {
        let state_size = self.state_vector.len();
        let mut new_state = self.state_vector.clone();

        for i in 0..state_size {
            let control_bit = (i >> control) & 1;
            if control_bit == 1 {
                let flipped_state = i ^ (1 << target);
                new_state[i] = self.state_vector[flipped_state];
            }
        }

        self.state_vector = new_state;
        self.circuit_depth += 1;
        Ok(())
    }

    fn apply_rotation_y(&mut self, qubit: usize, angle: f64) -> Result<()> {
        let state_size = self.state_vector.len();
        let mut new_state = vec![Complex::new(0.0, 0.0); state_size];

        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();

        for i in 0..state_size {
            let bit = (i >> qubit) & 1;
            let flipped_state = i ^ (1 << qubit);

            if bit == 0 {
                // R_y(θ) = [[cos(θ/2), -sin(θ/2)], [sin(θ/2), cos(θ/2)]]
                // new[i0] = cos(θ/2)*old[i0] - sin(θ/2)*old[i1]
                new_state[i] = Complex::new(cos_half, 0.0) * self.state_vector[i]
                    - Complex::new(sin_half, 0.0) * self.state_vector[flipped_state];
            } else {
                // new[i1] = sin(θ/2)*old[i0] + cos(θ/2)*old[i1]
                new_state[i] = Complex::new(sin_half, 0.0) * self.state_vector[flipped_state]
                    + Complex::new(cos_half, 0.0) * self.state_vector[i];
            }
        }

        self.state_vector = new_state;
        self.circuit_depth += 1;
        Ok(())
    }

    fn apply_rotation_z(&mut self, qubit: usize, angle: f64) -> Result<()> {
        let state_size = self.state_vector.len();
        let phase = Complex::new(0.0, angle / 2.0).exp();
        let neg_phase = Complex::new(0.0, -angle / 2.0).exp();

        for i in 0..state_size {
            let bit = (i >> qubit) & 1;
            if bit == 0 {
                self.state_vector[i] *= neg_phase;
            } else {
                self.state_vector[i] *= phase;
            }
        }

        self.circuit_depth += 1;
        Ok(())
    }
}

impl QuantumGateSet {
    fn new() -> Self {
        Self {
            single_qubit: vec![
                SingleQubitGate::Hadamard,
                SingleQubitGate::PauliX,
                SingleQubitGate::PauliY,
                SingleQubitGate::PauliZ,
                SingleQubitGate::T,
                SingleQubitGate::S,
            ],
            two_qubit: vec![
                TwoQubitGate::CNOT,
                TwoQubitGate::CZ,
                TwoQubitGate::SWAP,
                TwoQubitGate::ISWAP,
            ],
            multi_qubit: vec![MultiQubitGate::Toffoli, MultiQubitGate::Fredkin],
            parameterized: vec![],
        }
    }
}

impl Default for NoiseModel {
    fn default() -> Self {
        Self {
            t1_time: Duration::from_micros(100),
            t2_time: Duration::from_micros(50),
            single_qubit_error_rate: 0.001,
            two_qubit_error_rate: 0.01,
            measurement_error_rate: 0.01,
            crosstalk_matrix: Array2::zeros((20, 20)),
        }
    }
}

impl<F: Float> SuperpositionManager<F> {
    fn new(_maxdepth: usize) -> Self {
        Self {
            active_states: HashMap::new(),
            _maxdepth,
            coherence_tracker: CoherenceTracker::new(),
        }
    }

    fn add_state(&mut self, key: String, state: SuperpositionState<F>) {
        if self.active_states.len() >= self._maxdepth {
            // Remove oldest state
            let oldest_key = self.find_oldest_state();
            if let Some(key) = oldest_key {
                self.active_states.remove(&key);
            }
        }

        self.coherence_tracker.track_state(&key, state.creationtime);
        self.active_states.insert(key, state);
    }

    fn find_oldest_state(&self) -> Option<String> {
        self.active_states
            .iter()
            .min_by_key(|(_, state)| state.creationtime)
            .map(|(key, _)| key.clone())
    }
}

impl CoherenceTracker {
    fn new() -> Self {
        Self {
            coherence_times: HashMap::new(),
            decoherence_rates: HashMap::new(),
            environment_coupling: 0.01,
        }
    }

    fn track_state(&mut self, key: &str, creationtime: Instant) {
        self.coherence_times
            .insert(key.to_string(), creationtime.elapsed());

        // Model decoherence
        let decoherence_rate = self.environment_coupling * creationtime.elapsed().as_secs_f64();
        self.decoherence_rates
            .insert(key.to_string(), decoherence_rate);
    }
}

impl<F: Float> InterferencePatterns<F> {
    fn new() -> Self {
        Self {
            constructive_patterns: Vec::new(),
            destructive_patterns: Vec::new(),
            optimization_history: Vec::new(),
        }
    }
}

impl QuantumPerformanceMonitor {
    fn new() -> Self {
        Self {
            speedup_measurements: HashMap::new(),
            execution_times: HashMap::new(),
            fidelity_tracking: HashMap::new(),
            error_correction_overhead: Vec::new(),
            quantum_volume: Vec::new(),
        }
    }

    fn record_execution(&mut self, operation: &str, duration: Duration) {
        self.execution_times
            .entry(operation.to_string())
            .or_default()
            .push(duration);
    }

    fn record_vqe_progress(&mut self, iteration: usize, energy: f64) {
        // Record VQE optimization progress
        let progress_key = format!("vqe_iteration_{}", iteration);
        self.execution_times
            .entry(progress_key)
            .or_default()
            .push(Duration::from_millis((energy * 1000.0) as u64));
    }

    fn record_qaoa_progress(&mut self, iteration: usize, objective: f64) {
        // Record QAOA optimization progress
        let progress_key = format!("qaoa_iteration_{}", iteration);
        self.execution_times
            .entry(progress_key)
            .or_default()
            .push(Duration::from_millis((objective * 1000.0) as u64));
    }
}

impl<F: Float + SimdUnifiedOps + Send + Sync + std::iter::Sum> ClassicalFallback<F> {
    fn new() -> Result<Self> {
        Ok(Self {
            simd_capabilities: PlatformCapabilities::detect(),
            performance_baseline: HashMap::new(),
            auto_fallback: true,
            _phantom: std::marker::PhantomData,
        })
    }

    fn correlation(&self, x: &ArrayView1<F>, y: &ArrayView1<F>) -> Result<F> {
        if x.len() != y.len() {
            return Err(MetricsError::InvalidInput(
                "Arrays must have same length".to_string(),
            ));
        }

        if self.simd_capabilities.simd_available {
            // Use SIMD acceleration
            let n = F::from(x.len()).unwrap();
            let mean_x = F::simd_sum(&x.view()) / n;
            let mean_y = F::simd_sum(&y.view()) / n;

            let mean_x_array = Array1::from_elem(x.len(), mean_x);
            let mean_y_array = Array1::from_elem(y.len(), mean_y);

            let dev_x = F::simd_sub(x, &mean_x_array.view());
            let dev_y = F::simd_sub(y, &mean_y_array.view());

            let cov_xy = F::simd_mul(&dev_x.view(), &dev_y.view());
            let sum_cov = F::simd_sum(&cov_xy.view());

            let var_x = F::simd_mul(&dev_x.view(), &dev_x.view());
            let var_y = F::simd_mul(&dev_y.view(), &dev_y.view());

            let sum_var_x = F::simd_sum(&var_x.view());
            let sum_var_y = F::simd_sum(&var_y.view());

            let denom = (sum_var_x * sum_var_y).sqrt();
            if denom > F::zero() {
                Ok(sum_cov / denom)
            } else {
                Ok(F::zero())
            }
        } else {
            // Fallback to scalar computation
            let n = F::from(x.len()).unwrap();
            let mean_x = x.iter().cloned().sum::<F>() / n;
            let mean_y = y.iter().cloned().sum::<F>() / n;

            let mut numerator = F::zero();
            let mut sum_sq_x = F::zero();
            let mut sum_sq_y = F::zero();

            for (&xi, &yi) in x.iter().zip(y.iter()) {
                let dx = xi - mean_x;
                let dy = yi - mean_y;
                numerator = numerator + dx * dy;
                sum_sq_x = sum_sq_x + dx * dx;
                sum_sq_y = sum_sq_y + dy * dy;
            }

            let denominator = (sum_sq_x * sum_sq_y).sqrt();

            if denominator > F::zero() {
                Ok(numerator / denominator)
            } else {
                Ok(F::zero())
            }
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_quantum_config_creation() {
        let config = QuantumConfig::default();
        assert_eq!(config._numqubits, 20);
        assert!(config.enable_error_correction);
        assert!(config.enable_qaoa);
    }

    #[test]
    fn test_quantum_processor_creation() {
        let processor = QuantumProcessor::<f64>::new(4).unwrap();
        assert_eq!(processor._numqubits, 4);
        assert_eq!(processor.state_vector.len(), 16); // 2^4
        assert_eq!(processor.state_vector[0], Complex::new(1.0, 0.0)); // |0000⟩
    }

    #[test]
    fn test_quantum_gates() {
        let mut processor = QuantumProcessor::<f64>::new(2).unwrap();

        // Apply Hadamard to qubit 0 (LSB)
        processor.apply_hadamard(0).unwrap();

        // State should be (|00⟩ + |01⟩)/√2
        // In binary: |00⟩ = index 0, |01⟩ = index 1
        let sqrt2 = 2.0_f64.sqrt();
        assert!((processor.state_vector[0].re - 1.0 / sqrt2).abs() < 1e-10);
        assert!((processor.state_vector[1].re - 1.0 / sqrt2).abs() < 1e-10);
        assert!(processor.state_vector[2].re.abs() < 1e-10);
        assert!(processor.state_vector[3].re.abs() < 1e-10);

        // Apply CNOT(0, 1) - control=0, target=1
        // Flips qubit 1 when qubit 0 is 1
        processor.apply_cnot(0, 1).unwrap();

        // State should be (|00⟩ + |11⟩)/√2 (Bell state)
        // |00⟩ → |00⟩ (qubit 0 is 0, no flip)
        // |01⟩ → |11⟩ (qubit 0 is 1, flip qubit 1)
        assert!((processor.state_vector[0].re - 1.0 / sqrt2).abs() < 1e-10);
        assert!(processor.state_vector[1].re.abs() < 1e-10);
        assert!(processor.state_vector[2].re.abs() < 1e-10);
        assert!((processor.state_vector[3].re - 1.0 / sqrt2).abs() < 1e-10);
    }

    #[test]
    #[ignore] // FIXME: This test causes SIGKILL - memory issues or system resource exhaustion
    fn test_quantum_computer_creation() {
        let config = QuantumConfig::default();
        let computer = QuantumMetricsComputer::<f64>::new(config).unwrap();
        assert_eq!(computer.config._numqubits, 20);
    }

    #[test]
    #[ignore = "timeout"]
    fn test_classical_fallback_correlation() {
        let fallback = ClassicalFallback::<f64>::new().unwrap();
        let x = array![1.0, 2.0, 3.0, 4.0, 5.0];
        let y = array![2.0, 4.0, 6.0, 8.0, 10.0]; // Perfect correlation

        let correlation = fallback.correlation(&x.view(), &y.view()).unwrap();
        assert!((correlation - 1.0).abs() < 1e-10);
    }

    #[test]
    fn test_superposition_manager() {
        let mut manager = SuperpositionManager::<f64>::new(3);

        let state = SuperpositionState {
            amplitudes: vec![Complex::new(1.0, 0.0), Complex::new(0.0, 1.0)],
            classical_values: vec![1.0, 2.0],
            creationtime: Instant::now(),
            fidelity: 0.99,
        };

        manager.add_state("test_state".to_string(), state);
        assert_eq!(manager.active_states.len(), 1);
        assert!(manager.active_states.contains_key("test_state"));
    }

    #[test]
    #[ignore = "timeout"]
    fn test_quantum_benchmark_results() {
        let mut results = QuantumBenchmarkResults::new();

        results.add_measurement(
            100,
            Duration::from_millis(10),
            Duration::from_millis(5),
            2.0,
        );
        results.add_measurement(
            200,
            Duration::from_millis(20),
            Duration::from_millis(8),
            2.5,
        );

        assert_eq!(results.measurements.len(), 2);
        assert_eq!(results.quantum_advantage_threshold, 100);
        assert!(results.max_speedup >= 2.5);
    }
}