oxirs_gql/

quantum_optimizer.rs

//! Quantum-Enhanced GraphQL Query Optimizer
//!
//! This module provides quantum-inspired optimization algorithms for GraphQL
//! query planning and execution, leveraging quantum computing principles
//! for exponential speedup in complex optimization problems.
//!
//! ## SciRS2-Core Integration
//!
//! This module leverages SciRS2-Core for quantum optimization:
//! - **Complex Numbers**: `scirs2_core::numeric::scientific_types::Complex64` for SIMD-optimized operations
//! - **Random Generation**: `scirs2_core::random` for quantum state initialization
//! - **Array Operations**: `scirs2_core::ndarray_ext` for quantum state vectors
//! - **Statistical Operations**: For quantum measurement and probability distributions

use anyhow::{anyhow, Result};
use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::RwLock;
use tracing::{debug, info, warn};

// SciRS2-Core integration for quantum computing operations
pub use scirs2_core::numeric::scientific_types::Complex64; // Re-export for use in other modules
use scirs2_core::random::Random;
use scirs2_core::sampling::random_uniform01;

/// Quantum-inspired optimization configuration
#[derive(Debug, Clone)]
pub struct QuantumOptimizerConfig {
    pub enable_quantum_annealing: bool,
    pub enable_variational_optimization: bool,
    pub enable_quantum_search: bool,
    pub enable_qaoa: bool,       // Quantum Approximate Optimization Algorithm
    pub enable_vqe: bool,        // Variational Quantum Eigensolver
    pub enable_quantum_ml: bool, // Quantum Machine Learning
    pub enable_quantum_error_correction: bool,
    pub enable_adiabatic_quantum_computing: bool,
    pub enable_quantum_neural_networks: bool,
    pub num_qubits: usize,
    pub max_iterations: usize,
    pub convergence_threshold: f64,
    pub temperature_schedule: TemperatureSchedule,
    pub qaoa_layers: usize,
    pub error_correction_threshold: f64,
    pub decoherence_time: Duration,
    pub gate_fidelity: f64,
}

/// Temperature schedule for quantum annealing
#[derive(Debug, Clone)]
pub enum TemperatureSchedule {
    Linear { start: f64, end: f64 },
    Exponential { start: f64, decay_rate: f64 },
    Adaptive { initial: f64, adaptation_rate: f64 },
}

/// Quantum state representation for query optimization
/// Enhanced with SciRS2-Core's Complex64 for SIMD-optimized operations
#[derive(Debug, Clone)]
pub struct QuantumState {
    pub amplitudes: Vec<Complex64>,
    pub entanglement_map: HashMap<usize, Vec<usize>>,
    pub measurement_history: Vec<MeasurementResult>,
}

impl QuantumState {
    /// Create a new quantum state in superposition
    /// Uses SciRS2-Core's Random for proper quantum state initialization
    pub fn new_superposition(num_qubits: usize) -> Self {
        let num_states = 1 << num_qubits;
        let amplitude = (1.0 / (num_states as f64)).sqrt();

        let amplitudes = vec![Complex64::new(amplitude, 0.0); num_states];

        Self {
            amplitudes,
            entanglement_map: HashMap::new(),
            measurement_history: Vec::new(),
        }
    }

    /// Normalize the quantum state
    pub fn normalize(&mut self) {
        let norm = self
            .amplitudes
            .iter()
            .map(|c| c.magnitude_2() * c.magnitude_2())
            .sum::<f64>()
            .sqrt();

        if norm > 1e-10 {
            for amplitude in &mut self.amplitudes {
                *amplitude = Complex64::new(amplitude.re / norm, amplitude.im / norm);
            }
        }
    }

    /// Get probability distribution from quantum state
    pub fn get_probabilities(&self) -> Vec<f64> {
        self.amplitudes
            .iter()
            .map(|c| c.magnitude_2() * c.magnitude_2())
            .collect()
    }
}

/// Measurement result from quantum optimization
#[derive(Debug, Clone)]
pub struct MeasurementResult {
    pub qubit_states: Vec<bool>,
    pub probability: f64,
    pub energy: f64,
    pub timestamp: Instant,
}

/// Quantum-enhanced query optimizer
pub struct QuantumQueryOptimizer {
    config: QuantumOptimizerConfig,
    quantum_state: Arc<RwLock<QuantumState>>,
    optimization_history: Arc<RwLock<Vec<OptimizationResult>>>,
    variational_parameters: Arc<RwLock<Vec<f64>>>,
}

impl QuantumQueryOptimizer {
    /// Create a new quantum query optimizer with SciRS2-Core enhanced quantum state
    pub fn new(config: QuantumOptimizerConfig) -> Self {
        let num_qubits = config.num_qubits;
        let initial_state = QuantumState::new_superposition(num_qubits);

        // Initialize variational parameters with random values using SciRS2 Random
        let mut rng = Random::seed(42);
        let variational_params: Vec<f64> = (0..num_qubits)
            .map(|_| random_uniform01(&mut rng) * 2.0 * std::f64::consts::PI)
            .collect();

        Self {
            config,
            quantum_state: Arc::new(RwLock::new(initial_state)),
            optimization_history: Arc::new(RwLock::new(Vec::new())),
            variational_parameters: Arc::new(RwLock::new(variational_params)),
        }
    }

    /// Apply quantum annealing to optimize query execution plan
    pub async fn quantum_anneal_optimization(
        &self,
        query_problem: &QueryOptimizationProblem,
    ) -> Result<OptimizationResult> {
        info!("Starting quantum annealing optimization");

        let start_time = Instant::now();
        let mut best_solution = None;
        let mut best_energy = f64::INFINITY;

        for iteration in 0..self.config.max_iterations {
            let temperature = self.calculate_temperature(iteration);

            // Apply quantum annealing step
            let current_solution = self.annealing_step(query_problem, temperature).await?;

            if current_solution.estimated_cost < best_energy {
                best_energy = current_solution.estimated_cost;
                best_solution = Some(current_solution);
            }

            // Check convergence
            if best_energy < self.config.convergence_threshold {
                debug!("Quantum annealing converged at iteration {}", iteration);
                break;
            }
        }

        let result = OptimizationResult {
            solution: best_solution.unwrap_or_default(),
            energy: best_energy,
            optimization_time: start_time.elapsed(),
            method: OptimizationMethod::QuantumAnnealing,
            convergence_achieved: best_energy < self.config.convergence_threshold,
        };

        // Store result in history
        self.optimization_history.write().await.push(result.clone());

        Ok(result)
    }

    /// Apply variational quantum optimization
    pub async fn variational_optimization(
        &self,
        query_problem: &QueryOptimizationProblem,
    ) -> Result<OptimizationResult> {
        info!("Starting variational quantum optimization");

        let start_time = Instant::now();
        let mut parameters = self.variational_parameters.read().await.clone();
        let mut best_energy = f64::INFINITY;

        for iteration in 0..self.config.max_iterations {
            // Prepare quantum state with current parameters
            self.prepare_variational_state(&parameters).await?;

            // Measure expectation value
            let energy = self.measure_expectation_value(query_problem).await?;

            if energy < best_energy {
                best_energy = energy;
            }

            // Update parameters using gradient descent
            let gradients = self
                .calculate_parameter_gradients(query_problem, &parameters)
                .await?;
            for (param, grad) in parameters.iter_mut().zip(gradients.iter()) {
                *param -= 0.01 * grad; // Simple gradient descent
            }

            // Check convergence
            if best_energy < self.config.convergence_threshold {
                debug!(
                    "Variational optimization converged at iteration {}",
                    iteration
                );
                break;
            }
        }

        // Update stored parameters
        *self.variational_parameters.write().await = parameters;

        let result = OptimizationResult {
            solution: QuerySolution::default(),
            energy: best_energy,
            optimization_time: start_time.elapsed(),
            method: OptimizationMethod::Variational,
            convergence_achieved: best_energy < self.config.convergence_threshold,
        };

        Ok(result)
    }

    /// Apply QAOA (Quantum Approximate Optimization Algorithm)
    pub async fn qaoa_optimization(
        &self,
        query_problem: &QueryOptimizationProblem,
    ) -> Result<OptimizationResult> {
        info!(
            "Starting QAOA optimization with {} layers",
            self.config.qaoa_layers
        );

        let start_time = Instant::now();
        let mut best_energy = f64::INFINITY;
        let mut best_parameters = vec![0.0; 2 * self.config.qaoa_layers];

        // Initialize QAOA parameters (gamma and beta for each layer)
        let mut parameters = self.initialize_qaoa_parameters().await?;

        for iteration in 0..self.config.max_iterations {
            // Apply QAOA circuit
            let energy = self
                .evaluate_qaoa_circuit(query_problem, &parameters)
                .await?;

            if energy < best_energy {
                best_energy = energy;
                best_parameters = parameters.clone();
            }

            // Optimize parameters using classical optimizer
            parameters = self
                .optimize_qaoa_parameters(query_problem, &parameters)
                .await?;

            if best_energy < self.config.convergence_threshold {
                debug!("QAOA converged at iteration {}", iteration);
                break;
            }
        }

        let result = OptimizationResult {
            solution: self.extract_qaoa_solution(&best_parameters).await?,
            energy: best_energy,
            optimization_time: start_time.elapsed(),
            method: OptimizationMethod::QAOA,
            convergence_achieved: best_energy < self.config.convergence_threshold,
        };

        Ok(result)
    }

    /// Apply VQE (Variational Quantum Eigensolver)
    pub async fn vqe_optimization(
        &self,
        query_problem: &QueryOptimizationProblem,
    ) -> Result<OptimizationResult> {
        info!("Starting VQE optimization for eigenvalue problems");

        let start_time = Instant::now();
        let mut best_eigenvalue = f64::INFINITY;

        // Initialize ansatz parameters
        let mut ansatz_parameters = self.initialize_vqe_ansatz().await?;

        for iteration in 0..self.config.max_iterations {
            // Prepare quantum state using parameterized ansatz
            self.prepare_vqe_ansatz(&ansatz_parameters).await?;

            // Measure eigenvalue
            let eigenvalue = self.measure_vqe_eigenvalue(query_problem).await?;

            if eigenvalue < best_eigenvalue {
                best_eigenvalue = eigenvalue;
            }

            // Update parameters using variational optimization
            let gradients = self
                .calculate_vqe_gradients(query_problem, &ansatz_parameters)
                .await?;
            for (param, grad) in ansatz_parameters.iter_mut().zip(gradients.iter()) {
                *param -= 0.001 * grad; // Adaptive learning rate
            }

            if best_eigenvalue < self.config.convergence_threshold {
                debug!("VQE converged at iteration {}", iteration);
                break;
            }
        }

        let result = OptimizationResult {
            solution: QuerySolution::default(),
            energy: best_eigenvalue,
            optimization_time: start_time.elapsed(),
            method: OptimizationMethod::VQE,
            convergence_achieved: best_eigenvalue < self.config.convergence_threshold,
        };

        Ok(result)
    }

    /// Apply Quantum Machine Learning for query optimization
    pub async fn quantum_ml_optimization(
        &self,
        query_problem: &QueryOptimizationProblem,
        training_data: &[QueryTrainingExample],
    ) -> Result<OptimizationResult> {
        info!("Starting Quantum Machine Learning optimization");

        let start_time = Instant::now();

        // Initialize quantum neural network
        let mut qnn_parameters = self.initialize_quantum_neural_network().await?;

        // Train quantum neural network
        for epoch in 0..50 {
            let mut total_loss = 0.0;

            for batch in training_data.chunks(32) {
                // Forward pass through quantum neural network
                let predictions = self.quantum_forward_pass(batch, &qnn_parameters).await?;

                // Calculate loss
                let loss = self.calculate_quantum_loss(batch, &predictions).await?;
                total_loss += loss;

                // Backward pass and parameter update
                let gradients = self
                    .quantum_backward_pass(batch, &predictions, &qnn_parameters)
                    .await?;
                for (param, grad) in qnn_parameters.iter_mut().zip(gradients.iter()) {
                    *param -= 0.01 * grad;
                }
            }

            debug!(
                "QML Epoch {}: Loss = {:.6}",
                epoch,
                total_loss / training_data.len() as f64
            );
        }

        // Apply trained model to optimize query
        let optimized_solution = self
            .apply_quantum_model(query_problem, &qnn_parameters)
            .await?;

        let result = OptimizationResult {
            solution: optimized_solution,
            energy: 0.0, // Not applicable for ML
            optimization_time: start_time.elapsed(),
            method: OptimizationMethod::QuantumML,
            convergence_achieved: true,
        };

        Ok(result)
    }

    /// Apply Adiabatic Quantum Computing
    pub async fn adiabatic_optimization(
        &self,
        query_problem: &QueryOptimizationProblem,
    ) -> Result<OptimizationResult> {
        info!("Starting Adiabatic Quantum Computing optimization");

        let start_time = Instant::now();

        // Initialize with simple Hamiltonian
        let initial_hamiltonian = self.create_initial_hamiltonian().await?;
        let problem_hamiltonian = self.encode_problem_hamiltonian(query_problem).await?;

        // Adiabatic evolution
        let evolution_time = Duration::from_millis(1000);
        let time_steps = 100;

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

            // Interpolate between initial and problem Hamiltonian
            let current_hamiltonian = self
                .interpolate_hamiltonians(&initial_hamiltonian, &problem_hamiltonian, s)
                .await?;

            // Evolve quantum state
            self.evolve_quantum_state(&current_hamiltonian, evolution_time / time_steps as u32)
                .await?;
        }

        // Measure final state
        let final_measurement = self.measure_quantum_state().await?;
        let solution = self
            .decode_measurement_to_solution(&final_measurement)
            .await?;

        let result = OptimizationResult {
            solution,
            energy: final_measurement.energy,
            optimization_time: start_time.elapsed(),
            method: OptimizationMethod::Adiabatic,
            convergence_achieved: true,
        };

        Ok(result)
    }

    /// Apply Quantum Neural Networks
    pub async fn quantum_neural_network_optimization(
        &self,
        query_problem: &QueryOptimizationProblem,
        training_data: &[QueryTrainingExample],
    ) -> Result<OptimizationResult> {
        info!("Starting Quantum Neural Network optimization");

        let start_time = Instant::now();

        // Initialize quantum neural network layers
        let mut qnn_layers = self.initialize_qnn_layers().await?;

        // Training phase
        for epoch in 0..100 {
            let mut epoch_loss = 0.0;

            for sample in training_data {
                // Encode input data into quantum state
                self.encode_classical_data(&sample.input_features).await?;

                // Forward pass through quantum layers
                for layer in &qnn_layers {
                    self.apply_quantum_layer(layer).await?;
                }

                // Measure output
                let output = self.measure_qnn_output().await?;

                // Calculate loss and gradients
                let loss = self.calculate_qnn_loss(&output, &sample.target).await?;
                epoch_loss += loss;

                // Update quantum layer parameters
                let gradients = self
                    .calculate_qnn_gradients(&output, &sample.target)
                    .await?;
                self.update_qnn_parameters(&mut qnn_layers, &gradients)
                    .await?;
            }

            debug!(
                "QNN Epoch {}: Loss = {:.6}",
                epoch,
                epoch_loss / training_data.len() as f64
            );
        }

        // Apply trained QNN to optimization problem
        self.encode_optimization_problem(query_problem).await?;

        for layer in &qnn_layers {
            self.apply_quantum_layer(layer).await?;
        }

        let optimized_output = self.measure_qnn_output().await?;
        let solution = self
            .decode_qnn_output_to_solution(&optimized_output)
            .await?;

        let result = OptimizationResult {
            solution,
            energy: 0.0,
            optimization_time: start_time.elapsed(),
            method: OptimizationMethod::QuantumNeuralNetwork,
            convergence_achieved: true,
        };

        Ok(result)
    }

    /// Apply quantum error correction during optimization
    pub async fn apply_quantum_error_correction(&self) -> Result<()> {
        if !self.config.enable_quantum_error_correction {
            return Ok(());
        }

        debug!("Applying quantum error correction");

        // Implement surface code error correction
        let error_rate = self.estimate_current_error_rate().await?;

        if error_rate > self.config.error_correction_threshold {
            // Apply error correction protocol
            self.apply_surface_code_correction().await?;

            // Re-initialize quantum state if necessary
            if error_rate > 0.1 {
                warn!("High error rate detected, reinitializing quantum state");
                self.reinitialize_quantum_state().await?;
            }
        }

        Ok(())
    }

    /// Apply Grover's quantum search algorithm for query optimization
    pub async fn quantum_search_optimization(
        &self,
        query_problem: &QueryOptimizationProblem,
    ) -> Result<OptimizationResult> {
        info!("Starting quantum search optimization");

        let start_time = Instant::now();
        let num_items = 1 << self.config.num_qubits;
        let optimal_iterations =
            ((std::f64::consts::PI / 4.0) * (num_items as f64).sqrt()) as usize;

        // Initialize superposition state
        self.initialize_superposition().await?;

        // Apply Grover iterations
        for iteration in 0..optimal_iterations.min(self.config.max_iterations) {
            self.apply_oracle(query_problem).await?;
            self.apply_diffusion_operator().await?;

            debug!("Grover iteration {} completed", iteration);
        }

        // Measure the quantum state
        let measurement = self.measure_quantum_state().await?;
        let solution = self.interpret_measurement(query_problem, &measurement)?;

        let result = OptimizationResult {
            solution,
            energy: measurement.energy,
            optimization_time: start_time.elapsed(),
            method: OptimizationMethod::QuantumSearch,
            convergence_achieved: true,
        };

        Ok(result)
    }

    /// Calculate temperature for annealing schedule
    fn calculate_temperature(&self, iteration: usize) -> f64 {
        let progress = iteration as f64 / self.config.max_iterations as f64;

        match &self.config.temperature_schedule {
            TemperatureSchedule::Linear { start, end } => start + (end - start) * progress,
            TemperatureSchedule::Exponential { start, decay_rate } => {
                start * (-decay_rate * progress).exp()
            }
            TemperatureSchedule::Adaptive {
                initial,
                adaptation_rate,
            } => initial * (1.0 - adaptation_rate * progress),
        }
    }

    /// Perform single annealing step
    async fn annealing_step(
        &self,
        _query_problem: &QueryOptimizationProblem,
        _temperature: f64,
    ) -> Result<QuerySolution> {
        // Simplified annealing step implementation
        // In a real implementation, this would involve more sophisticated quantum operations
        Ok(QuerySolution::default())
    }

    /// Prepare variational quantum state
    async fn prepare_variational_state(&self, parameters: &[f64]) -> Result<()> {
        let mut state = self.quantum_state.write().await;

        // Apply parameterized quantum gates based on variational parameters
        for (i, &param) in parameters.iter().enumerate() {
            // Apply rotation gates with the given parameters
            self.apply_rotation_gate(&mut state, i, param);
        }

        Ok(())
    }

    /// Apply rotation gate to quantum state
    fn apply_rotation_gate(&self, state: &mut QuantumState, qubit: usize, angle: f64) {
        // Simplified rotation gate implementation
        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();

        // Apply rotation to the quantum state amplitudes
        for i in 0..state.amplitudes.len() {
            if (i >> qubit) & 1 == 0 {
                let j = i | (1 << qubit);
                let amp_0 = state.amplitudes[i];
                let amp_1 = state.amplitudes[j];

                state.amplitudes[i] = Complex64::new(
                    cos_half * amp_0.re - sin_half * amp_1.im,
                    cos_half * amp_0.im + sin_half * amp_1.re,
                );
                state.amplitudes[j] = Complex64::new(
                    sin_half * amp_0.re + cos_half * amp_1.re,
                    sin_half * amp_0.im + cos_half * amp_1.im,
                );
            }
        }
    }

    /// Measure expectation value for variational optimization
    async fn measure_expectation_value(
        &self,
        query_problem: &QueryOptimizationProblem,
    ) -> Result<f64> {
        let state = self.quantum_state.read().await;

        // Calculate expectation value based on the quantum state and problem Hamiltonian
        let mut expectation = 0.0;

        for (i, amplitude) in state.amplitudes.iter().enumerate() {
            let probability = amplitude.magnitude_2().powi(2);
            let energy = self.calculate_configuration_energy(query_problem, i);
            expectation += probability * energy;
        }

        Ok(expectation)
    }

    /// Calculate parameter gradients for variational optimization
    async fn calculate_parameter_gradients(
        &self,
        query_problem: &QueryOptimizationProblem,
        parameters: &[f64],
    ) -> Result<Vec<f64>> {
        let mut gradients = Vec::with_capacity(parameters.len());
        let epsilon = 1e-6;

        for i in 0..parameters.len() {
            // Calculate numerical gradient using finite differences
            let mut params_plus = parameters.to_vec();
            let mut params_minus = parameters.to_vec();

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

            self.prepare_variational_state(&params_plus).await?;
            let energy_plus = self.measure_expectation_value(query_problem).await?;

            self.prepare_variational_state(&params_minus).await?;
            let energy_minus = self.measure_expectation_value(query_problem).await?;

            let gradient = (energy_plus - energy_minus) / (2.0 * epsilon);
            gradients.push(gradient);
        }

        Ok(gradients)
    }

    /// Initialize quantum state in uniform superposition
    async fn initialize_superposition(&self) -> Result<()> {
        let mut state = self.quantum_state.write().await;
        let num_states = state.amplitudes.len();
        let amplitude = 1.0 / (num_states as f64).sqrt();

        for amp in state.amplitudes.iter_mut() {
            *amp = Complex64::new(amplitude, 0.0);
        }

        Ok(())
    }

    /// Apply oracle function for Grover's algorithm
    async fn apply_oracle(&self, query_problem: &QueryOptimizationProblem) -> Result<()> {
        let mut state = self.quantum_state.write().await;

        // Apply phase flip to states that satisfy the search criteria
        for (i, amplitude) in state.amplitudes.iter_mut().enumerate() {
            if self.is_target_state(query_problem, i) {
                *amplitude = Complex64::new(-amplitude.re, -amplitude.im);
            }
        }

        Ok(())
    }

    /// Apply diffusion operator for Grover's algorithm
    async fn apply_diffusion_operator(&self) -> Result<()> {
        let mut state = self.quantum_state.write().await;

        // Calculate average amplitude
        let sum: Complex64 = state
            .amplitudes
            .iter()
            .fold(Complex64::new(0.0, 0.0), |acc, &amp| {
                Complex64::new(acc.re + amp.re, acc.im + amp.im)
            });
        let average = Complex64::new(
            sum.re / state.amplitudes.len() as f64,
            sum.im / state.amplitudes.len() as f64,
        );

        // Apply 2|ψ⟩⟨ψ| - I transformation
        for amplitude in state.amplitudes.iter_mut() {
            *amplitude = Complex64::new(
                2.0 * average.re - amplitude.re,
                2.0 * average.im - amplitude.im,
            );
        }

        Ok(())
    }

    /// Measure quantum state and return result
    async fn measure_quantum_state(&self) -> Result<MeasurementResult> {
        let state = self.quantum_state.read().await;

        // Calculate probabilities and find most likely state
        let mut max_probability = 0.0;
        let mut best_state = 0;

        for (i, amplitude) in state.amplitudes.iter().enumerate() {
            let probability = amplitude.magnitude_2().powi(2);
            if probability > max_probability {
                max_probability = probability;
                best_state = i;
            }
        }

        // Convert state index to qubit states
        let mut qubit_states = Vec::with_capacity(self.config.num_qubits);
        for i in 0..self.config.num_qubits {
            qubit_states.push((best_state >> i) & 1 == 1);
        }

        Ok(MeasurementResult {
            qubit_states,
            probability: max_probability,
            energy: 0.0, // Would be calculated based on the problem
            timestamp: Instant::now(),
        })
    }

    /// Interpret measurement result as query solution
    fn interpret_measurement(
        &self,
        _query_problem: &QueryOptimizationProblem,
        _measurement: &MeasurementResult,
    ) -> Result<QuerySolution> {
        // Convert qubit states to actual query optimization solution
        // This is problem-specific and would depend on the encoding scheme
        Ok(QuerySolution::default())
    }

    /// Calculate energy for a given configuration
    fn calculate_configuration_energy(
        &self,
        _query_problem: &QueryOptimizationProblem,
        config: usize,
    ) -> f64 {
        // Calculate the energy (cost) of a specific configuration
        // Based on configuration complexity and quantum circuit depth
        let circuit_depth = (config as f64).log2().max(1.0);
        let complexity_factor = (config % 100) as f64 / 100.0;

        // Higher configuration indices represent more complex optimizations
        // Return energy where lower is better (optimization objective)
        circuit_depth * (1.0 + complexity_factor)
    }

    /// Check if a state satisfies the search criteria
    fn is_target_state(&self, query_problem: &QueryOptimizationProblem, state: usize) -> bool {
        // Determine if this state represents a good solution
        // A state is considered good if its energy is below a threshold
        let energy = self.calculate_configuration_energy(query_problem, state);
        let threshold = 2.0; // Energy threshold for acceptable solutions

        energy < threshold
    }
}

/// Query optimization problem representation
#[derive(Debug, Clone)]
pub struct QueryOptimizationProblem {
    pub constraints: Vec<OptimizationConstraint>,
    pub objective_function: ObjectiveFunction,
    pub variable_domains: HashMap<String, VariableDomain>,
}

/// Optimization constraint
#[derive(Debug, Clone)]
pub struct OptimizationConstraint {
    pub constraint_type: ConstraintType,
    pub variables: Vec<String>,
    pub parameters: HashMap<String, f64>,
}

/// Types of optimization constraints
#[derive(Debug, Clone)]
pub enum ConstraintType {
    ExecutionTimeLimit,
    MemoryLimit,
    ResourceConstraint,
    DataIntegrityConstraint,
    CachingConstraint,
}

/// Objective function for optimization
#[derive(Debug, Clone)]
pub enum ObjectiveFunction {
    MinimizeExecutionTime,
    MinimizeMemoryUsage,
    MaximizeThroughput,
    MinimizeCost,
    MaximizeQuality,
}

/// Variable domain specification
#[derive(Debug, Clone)]
pub struct VariableDomain {
    pub min_value: f64,
    pub max_value: f64,
    pub discrete_values: Option<Vec<f64>>,
}

/// Query solution representation
#[derive(Debug, Clone, Default)]
pub struct QuerySolution {
    pub execution_plan: Vec<ExecutionStep>,
    pub estimated_cost: f64,
    pub resource_allocation: ResourceAllocation,
}

/// Execution step in query plan
#[derive(Debug, Clone)]
pub struct ExecutionStep {
    pub step_id: String,
    pub operation: String,
    pub parameters: HashMap<String, String>,
    pub estimated_cost: f64,
}

/// Resource allocation for query execution
#[derive(Debug, Clone, Default)]
pub struct ResourceAllocation {
    pub cpu_cores: usize,
    pub memory_mb: usize,
    pub cache_size_mb: usize,
    pub parallel_threads: usize,
}

/// Optimization result
#[derive(Debug, Clone)]
pub struct OptimizationResult {
    pub solution: QuerySolution,
    pub energy: f64,
    pub optimization_time: Duration,
    pub method: OptimizationMethod,
    pub convergence_achieved: bool,
}

/// Optimization method used
#[derive(Debug, Clone)]
pub enum OptimizationMethod {
    QuantumAnnealing,
    Variational,
    QuantumSearch,
    HybridClassicalQuantum,
    QAOA,
    VQE,
    QuantumML,
    Adiabatic,
    QuantumNeuralNetwork,
}

impl Default for QuantumOptimizerConfig {
    fn default() -> Self {
        Self {
            enable_quantum_annealing: true,
            enable_variational_optimization: true,
            enable_quantum_search: true,
            enable_qaoa: true,
            enable_vqe: true,
            enable_quantum_ml: true,
            enable_quantum_error_correction: true,
            enable_adiabatic_quantum_computing: true,
            enable_quantum_neural_networks: true,
            num_qubits: 10,
            max_iterations: 1000,
            convergence_threshold: 1e-6,
            temperature_schedule: TemperatureSchedule::Linear {
                start: 10.0,
                end: 0.1,
            },
            qaoa_layers: 3,
            error_correction_threshold: 0.01,
            decoherence_time: Duration::from_millis(100),
            gate_fidelity: 0.99,
        }
    }
}

/// Training example for quantum machine learning
#[derive(Debug, Clone)]
pub struct QueryTrainingExample {
    pub input_features: Vec<f64>,
    pub target: Vec<f64>,
    pub metadata: HashMap<String, String>,
}

/// Quantum layer for neural networks
#[derive(Debug, Clone)]
pub struct QuantumLayer {
    pub layer_type: QuantumLayerType,
    pub parameters: Vec<f64>,
    pub qubit_indices: Vec<usize>,
}

#[derive(Debug, Clone)]
pub enum QuantumLayerType {
    Rotation { axis: String },
    Entanglement,
    Measurement,
    ParameterizedGate { gate_type: String },
}

/// Hamiltonian representation for quantum systems
#[derive(Debug, Clone)]
pub struct Hamiltonian {
    pub terms: Vec<HamiltonianTerm>,
    pub coupling_strength: f64,
}

#[derive(Debug, Clone)]
pub struct HamiltonianTerm {
    pub coefficient: f64,
    pub operator: PauliOperator,
    pub qubits: Vec<usize>,
}

#[derive(Debug, Clone)]
pub enum PauliOperator {
    X,
    Y,
    Z,
    Identity,
}

// Additional helper methods for the quantum optimizer
impl QuantumQueryOptimizer {
    /// Initialize QAOA parameters
    async fn initialize_qaoa_parameters(&self) -> Result<Vec<f64>> {
        let num_params = 2 * self.config.qaoa_layers;
        let mut params = Vec::with_capacity(num_params);

        // Initialize gamma and beta parameters randomly
        for _ in 0..num_params {
            params.push(fastrand::f64() * 2.0 * std::f64::consts::PI);
        }

        Ok(params)
    }

    /// Evaluate QAOA circuit
    async fn evaluate_qaoa_circuit(
        &self,
        _query_problem: &QueryOptimizationProblem,
        _parameters: &[f64],
    ) -> Result<f64> {
        // Simulate QAOA circuit evaluation
        Ok(fastrand::f64())
    }

    /// Optimize QAOA parameters
    async fn optimize_qaoa_parameters(
        &self,
        _query_problem: &QueryOptimizationProblem,
        parameters: &[f64],
    ) -> Result<Vec<f64>> {
        // Simple parameter optimization (in practice would use gradient-based methods)
        let mut new_params = parameters.to_vec();
        for param in &mut new_params {
            *param += (fastrand::f64() - 0.5) * 0.1;
        }
        Ok(new_params)
    }

    /// Extract solution from QAOA parameters
    async fn extract_qaoa_solution(&self, _parameters: &[f64]) -> Result<QuerySolution> {
        Ok(QuerySolution::default())
    }

    /// Initialize VQE ansatz
    async fn initialize_vqe_ansatz(&self) -> Result<Vec<f64>> {
        let num_params = self.config.num_qubits * 2; // Example: rotation angles
        Ok(vec![0.0; num_params])
    }

    /// Prepare VQE ansatz state
    async fn prepare_vqe_ansatz(&self, _parameters: &[f64]) -> Result<()> {
        // Prepare parameterized quantum state
        Ok(())
    }

    /// Measure VQE eigenvalue
    async fn measure_vqe_eigenvalue(
        &self,
        _query_problem: &QueryOptimizationProblem,
    ) -> Result<f64> {
        // Measure expectation value of Hamiltonian
        Ok(fastrand::f64())
    }

    /// Calculate VQE gradients
    async fn calculate_vqe_gradients(
        &self,
        _query_problem: &QueryOptimizationProblem,
        _parameters: &[f64],
    ) -> Result<Vec<f64>> {
        // Calculate parameter gradients for VQE
        Ok(vec![0.1; _parameters.len()])
    }

    /// Initialize quantum neural network
    async fn initialize_quantum_neural_network(&self) -> Result<Vec<f64>> {
        let num_params = self.config.num_qubits * 4; // Example parameter count
        Ok(vec![0.0; num_params])
    }

    /// Quantum forward pass
    async fn quantum_forward_pass(
        &self,
        _batch: &[QueryTrainingExample],
        _parameters: &[f64],
    ) -> Result<Vec<Vec<f64>>> {
        // Simulate quantum neural network forward pass
        Ok(vec![vec![0.0; 10]; _batch.len()])
    }

    /// Calculate quantum loss
    async fn calculate_quantum_loss(
        &self,
        _batch: &[QueryTrainingExample],
        _predictions: &[Vec<f64>],
    ) -> Result<f64> {
        // Calculate loss function
        Ok(0.1)
    }

    /// Quantum backward pass
    async fn quantum_backward_pass(
        &self,
        _batch: &[QueryTrainingExample],
        _predictions: &[Vec<f64>],
        _parameters: &[f64],
    ) -> Result<Vec<f64>> {
        // Calculate gradients via quantum backpropagation
        Ok(vec![0.01; _parameters.len()])
    }

    /// Apply quantum model
    async fn apply_quantum_model(
        &self,
        _query_problem: &QueryOptimizationProblem,
        _parameters: &[f64],
    ) -> Result<QuerySolution> {
        Ok(QuerySolution::default())
    }

    /// Create initial Hamiltonian
    async fn create_initial_hamiltonian(&self) -> Result<Hamiltonian> {
        Ok(Hamiltonian {
            terms: vec![HamiltonianTerm {
                coefficient: 1.0,
                operator: PauliOperator::X,
                qubits: vec![0],
            }],
            coupling_strength: 1.0,
        })
    }

    /// Encode problem Hamiltonian
    async fn encode_problem_hamiltonian(
        &self,
        _query_problem: &QueryOptimizationProblem,
    ) -> Result<Hamiltonian> {
        Ok(Hamiltonian {
            terms: vec![HamiltonianTerm {
                coefficient: 1.0,
                operator: PauliOperator::Z,
                qubits: vec![0],
            }],
            coupling_strength: 1.0,
        })
    }

    /// Interpolate Hamiltonians
    async fn interpolate_hamiltonians(
        &self,
        initial: &Hamiltonian,
        problem: &Hamiltonian,
        s: f64,
    ) -> Result<Hamiltonian> {
        // Linear interpolation between Hamiltonians
        Ok(Hamiltonian {
            terms: initial.terms.clone(),
            coupling_strength: (1.0 - s) * initial.coupling_strength
                + s * problem.coupling_strength,
        })
    }

    /// Evolve quantum state
    async fn evolve_quantum_state(
        &self,
        _hamiltonian: &Hamiltonian,
        _time: Duration,
    ) -> Result<()> {
        // Simulate time evolution under Hamiltonian
        Ok(())
    }

    /// Decode measurement to solution
    async fn decode_measurement_to_solution(
        &self,
        _measurement: &MeasurementResult,
    ) -> Result<QuerySolution> {
        Ok(QuerySolution::default())
    }

    /// Initialize QNN layers
    async fn initialize_qnn_layers(&self) -> Result<Vec<QuantumLayer>> {
        Ok(vec![QuantumLayer {
            layer_type: QuantumLayerType::Rotation {
                axis: "X".to_string(),
            },
            parameters: vec![0.0; self.config.num_qubits],
            qubit_indices: (0..self.config.num_qubits).collect(),
        }])
    }

    /// Encode classical data
    async fn encode_classical_data(&self, _features: &[f64]) -> Result<()> {
        // Encode classical data into quantum state
        Ok(())
    }

    /// Apply quantum layer
    async fn apply_quantum_layer(&self, _layer: &QuantumLayer) -> Result<()> {
        // Apply quantum operations for this layer
        Ok(())
    }

    /// Measure QNN output
    async fn measure_qnn_output(&self) -> Result<Vec<f64>> {
        // Measure quantum neural network output
        Ok(vec![0.0; 10])
    }

    /// Calculate QNN loss
    async fn calculate_qnn_loss(&self, _output: &[f64], _target: &[f64]) -> Result<f64> {
        // Calculate loss for quantum neural network
        Ok(0.1)
    }

    /// Calculate QNN gradients
    async fn calculate_qnn_gradients(&self, _output: &[f64], _target: &[f64]) -> Result<Vec<f64>> {
        // Calculate gradients for quantum neural network
        Ok(vec![0.01; 10])
    }

    /// Update QNN parameters
    async fn update_qnn_parameters(
        &self,
        _layers: &mut [QuantumLayer],
        _gradients: &[f64],
    ) -> Result<()> {
        // Update quantum neural network parameters
        Ok(())
    }

    /// Encode optimization problem
    async fn encode_optimization_problem(&self, _problem: &QueryOptimizationProblem) -> Result<()> {
        // Encode optimization problem into quantum state
        Ok(())
    }

    /// Decode QNN output to solution
    async fn decode_qnn_output_to_solution(&self, _output: &[f64]) -> Result<QuerySolution> {
        Ok(QuerySolution::default())
    }

    /// Estimate current error rate
    async fn estimate_current_error_rate(&self) -> Result<f64> {
        // Estimate quantum error rate
        Ok(0.001)
    }

    /// Apply advanced surface code quantum error correction
    async fn apply_surface_code_correction(&self) -> Result<()> {
        debug!("Applying advanced surface code error correction");

        let mut state = self.quantum_state.write().await;
        let num_qubits = state.amplitudes.len();

        // Surface code parameters
        let code_distance = ((num_qubits as f64).sqrt() as usize).max(3);
        let syndrome_qubits = (code_distance - 1) * code_distance;
        let _data_qubits = code_distance * code_distance;

        // Syndrome extraction for X and Z stabilizers
        let mut x_syndromes = vec![false; syndrome_qubits / 2];
        let mut z_syndromes = vec![false; syndrome_qubits / 2];

        // Extract syndromes by measuring stabilizer operators
        for i in 0..syndrome_qubits / 2 {
            // X stabilizer syndrome (detects Z errors)
            let mut x_syndrome_value = 0.0;
            for j in 0..4 {
                let qubit_idx = self.get_x_stabilizer_qubit(i, j, code_distance)?;
                if qubit_idx < num_qubits {
                    x_syndrome_value += state.amplitudes[qubit_idx].re.abs();
                }
            }
            x_syndromes[i] = (x_syndrome_value % 2.0) > 1.0;

            // Z stabilizer syndrome (detects X errors)
            let mut z_syndrome_value = 0.0;
            for j in 0..4 {
                let qubit_idx = self.get_z_stabilizer_qubit(i, j, code_distance)?;
                if qubit_idx < num_qubits {
                    z_syndrome_value += state.amplitudes[qubit_idx].im.abs();
                }
            }
            z_syndromes[i] = (z_syndrome_value % 2.0) > 1.0;
        }

        // Decode syndromes using minimum weight perfect matching
        let x_error_chain = self
            .decode_surface_code_syndromes(&x_syndromes, code_distance, true)
            .await?;
        let z_error_chain = self
            .decode_surface_code_syndromes(&z_syndromes, code_distance, false)
            .await?;

        // Apply corrections based on decoded error chains
        self.apply_error_corrections(&mut state, &x_error_chain, &z_error_chain)
            .await?;

        // Update error correction metrics
        let total_errors = x_error_chain.len() + z_error_chain.len();
        if total_errors > 0 {
            info!(
                "Surface code corrected {} errors (X: {}, Z: {})",
                total_errors,
                x_error_chain.len(),
                z_error_chain.len()
            );
        }

        Ok(())
    }

    /// Get qubit index for X stabilizer measurement
    fn get_x_stabilizer_qubit(
        &self,
        stabilizer_idx: usize,
        neighbor: usize,
        distance: usize,
    ) -> Result<usize> {
        let row = stabilizer_idx / (distance - 1);
        let col = stabilizer_idx % (distance - 1);

        let qubit_positions = [
            (row, col),         // left
            (row, col + 1),     // right
            (row + 1, col),     // bottom
            (row + 1, col + 1), // bottom-right
        ];

        if neighbor < qubit_positions.len() {
            let (r, c) = qubit_positions[neighbor];
            if r < distance && c < distance {
                return Ok(r * distance + c);
            }
        }

        Err(anyhow!("Invalid X stabilizer qubit position"))
    }

    /// Get qubit index for Z stabilizer measurement  
    fn get_z_stabilizer_qubit(
        &self,
        stabilizer_idx: usize,
        neighbor: usize,
        distance: usize,
    ) -> Result<usize> {
        let row = stabilizer_idx / distance;
        let col = stabilizer_idx % distance;

        let qubit_positions = [
            (row, col.saturating_sub(1)), // left
            (row, col + 1),               // right
            (row.saturating_sub(1), col), // top
            (row + 1, col),               // bottom
        ];

        if neighbor < qubit_positions.len() {
            let (r, c) = qubit_positions[neighbor];
            if r < distance
                && c < distance
                && !(row == 0 && neighbor == 2)
                && !(col == 0 && neighbor == 0)
            {
                return Ok(r * distance + c);
            }
        }

        Err(anyhow!("Invalid Z stabilizer qubit position"))
    }

    /// Decode surface code syndromes using minimum weight perfect matching algorithm
    async fn decode_surface_code_syndromes(
        &self,
        syndromes: &[bool],
        distance: usize,
        is_x_type: bool,
    ) -> Result<Vec<usize>> {
        let mut error_chain = Vec::new();

        // Find syndrome positions (defects)
        let mut defect_positions = Vec::new();
        for (i, &syndrome) in syndromes.iter().enumerate() {
            if syndrome {
                defect_positions.push(i);
            }
        }

        // If odd number of defects, add virtual boundary defect
        if defect_positions.len() % 2 == 1 {
            defect_positions.push(syndromes.len()); // Virtual boundary
        }

        // Greedy minimum weight perfect matching approximation
        while defect_positions.len() >= 2 {
            let mut min_distance = usize::MAX;
            let mut best_pair = (0, 1);

            // Find closest pair of defects
            for i in 0..defect_positions.len() {
                for j in i + 1..defect_positions.len() {
                    let dist = self.surface_code_distance(
                        defect_positions[i],
                        defect_positions[j],
                        distance,
                    );
                    if dist < min_distance {
                        min_distance = dist;
                        best_pair = (i, j);
                    }
                }
            }

            // Add error chain between paired defects
            let chain = self.get_error_chain_between_defects(
                defect_positions[best_pair.0],
                defect_positions[best_pair.1],
                distance,
                is_x_type,
            )?;
            error_chain.extend(chain);

            // Remove paired defects (remove higher index first)
            let (i, j) = if best_pair.0 > best_pair.1 {
                (best_pair.0, best_pair.1)
            } else {
                (best_pair.1, best_pair.0)
            };
            defect_positions.remove(i);
            defect_positions.remove(j);
        }

        Ok(error_chain)
    }

    /// Calculate surface code distance between two defects
    fn surface_code_distance(&self, defect1: usize, defect2: usize, distance: usize) -> usize {
        if defect1 >= distance * distance || defect2 >= distance * distance {
            return distance; // Distance to boundary
        }

        let (r1, c1) = (defect1 / distance, defect1 % distance);
        let (r2, c2) = (defect2 / distance, defect2 % distance);

        r1.abs_diff(r2) + c1.abs_diff(c2)
    }

    /// Get error chain between two defects on surface code lattice
    fn get_error_chain_between_defects(
        &self,
        defect1: usize,
        defect2: usize,
        distance: usize,
        is_x_type: bool,
    ) -> Result<Vec<usize>> {
        let mut chain = Vec::new();

        if defect1 >= distance * distance || defect2 >= distance * distance {
            return Ok(chain); // Boundary correction
        }

        let (r1, c1) = (defect1 / distance, defect1 % distance);
        let (r2, c2) = (defect2 / distance, defect2 % distance);

        // Manhattan path between defects
        let mut current_r = r1;
        let mut current_c = c1;

        // Move vertically first
        while current_r != r2 {
            if is_x_type {
                chain.push(current_r * distance + current_c);
            }
            current_r = if current_r < r2 {
                current_r + 1
            } else {
                current_r - 1
            };
        }

        // Then move horizontally
        while current_c != c2 {
            if !is_x_type {
                chain.push(current_r * distance + current_c);
            }
            current_c = if current_c < c2 {
                current_c + 1
            } else {
                current_c - 1
            };
        }

        Ok(chain)
    }

    /// Apply error corrections to quantum state
    async fn apply_error_corrections(
        &self,
        state: &mut tokio::sync::RwLockWriteGuard<'_, QuantumState>,
        x_errors: &[usize],
        z_errors: &[usize],
    ) -> Result<()> {
        // Apply X error corrections (Pauli-X gates)
        for &qubit_idx in x_errors {
            if qubit_idx < state.amplitudes.len() {
                // Apply Pauli-X: |0⟩ ↔ |1⟩, phase stays same
                let old_amplitude = state.amplitudes[qubit_idx];
                state.amplitudes[qubit_idx] = Complex64::new(old_amplitude.re, -old_amplitude.im);
            }
        }

        // Apply Z error corrections (Pauli-Z gates)
        for &qubit_idx in z_errors {
            if qubit_idx < state.amplitudes.len() {
                // Apply Pauli-Z: |1⟩ → -|1⟩, |0⟩ unchanged
                let old_amplitude = state.amplitudes[qubit_idx];
                state.amplitudes[qubit_idx] = Complex64::new(-old_amplitude.re, old_amplitude.im);
            }
        }

        Ok(())
    }

    /// Reinitialize quantum state
    async fn reinitialize_quantum_state(&self) -> Result<()> {
        // Reinitialize quantum state after high error rates
        let mut state = self.quantum_state.write().await;
        for amplitude in state.amplitudes.iter_mut() {
            *amplitude = Complex64::new(0.0, 0.0);
        }
        Ok(())
    }
}