oxirs-star 0.2.4

//! Quantum-Enhanced SPARQL-star Query Optimizer
//!
//! This module provides quantum-inspired optimization algorithms for SPARQL-star
//! query planning and execution, leveraging quantum computing principles for
//! exponential speedup in complex optimization problems.
//!
//! ## Features
//!
//! - **Quantum Annealing**: Optimize query plans using simulated quantum annealing
//! - **Variational Optimization**: QAOA-inspired query plan optimization
//! - **Quantum Search**: Grover-inspired search for optimal join orders
//! - **Superposition Exploration**: Explore multiple query plans simultaneously
//! - **Entanglement-based Correlation**: Identify correlated query patterns
//!
//! ## SciRS2-Core Integration
//!
//! This module can leverage SciRS2-Core's quantum optimization capabilities:
//! - **Quantum Optimization**: `scirs2_core::quantum_optimization`
//! - **Complex Operations**: `scirs2_core::types::ComplexOps` for quantum states
//! - **Profiling**: `scirs2_core::profiling::Profiler` for performance tracking
//! - **Random**: `scirs2_core::random::Random` for quantum state initialization

use crate::{StarError, StarResult};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::RwLock;
use tracing::{debug, info};

// SciRS2-Core integration
use scirs2_core::random::{rand_distributions as rand_distr, Random};

/// Quantum-inspired optimization configuration for SPARQL-star
#[derive(Debug, Clone)]
pub struct QuantumSPARQLOptimizerConfig {
    /// Enable quantum annealing optimization
    pub enable_quantum_annealing: bool,
    /// Enable variational quantum optimization (QAOA-inspired)
    pub enable_variational_optimization: bool,
    /// Enable quantum search (Grover-inspired)
    pub enable_quantum_search: bool,
    /// Enable quantum approximate optimization algorithm
    pub enable_qaoa: bool,
    /// Enable variational quantum eigensolver
    pub enable_vqe: bool,
    /// Enable quantum machine learning integration
    pub enable_quantum_ml: bool,
    /// Enable quantum error correction
    pub enable_quantum_error_correction: bool,
    /// Enable adiabatic quantum computing
    pub enable_adiabatic_quantum_computing: bool,
    /// Enable quantum neural networks
    pub enable_quantum_neural_networks: bool,
    /// Number of qubits for quantum simulation
    pub num_qubits: usize,
    /// Maximum optimization iterations
    pub max_iterations: usize,
    /// Convergence threshold
    pub convergence_threshold: f64,
    /// Temperature schedule for annealing
    pub temperature_schedule: TemperatureSchedule,
    /// Number of QAOA layers
    pub qaoa_layers: usize,
    /// Error correction threshold
    pub error_correction_threshold: f64,
    /// Simulated decoherence time
    pub decoherence_time: Duration,
    /// Gate fidelity (0.0-1.0)
    pub gate_fidelity: f64,
}

impl Default for QuantumSPARQLOptimizerConfig {
    fn default() -> Self {
        Self {
            enable_quantum_annealing: true,
            enable_variational_optimization: false,
            enable_quantum_search: true,
            enable_qaoa: false,
            enable_vqe: false,
            enable_quantum_ml: false,
            enable_quantum_error_correction: false,
            enable_adiabatic_quantum_computing: false,
            enable_quantum_neural_networks: false,
            num_qubits: 16,
            max_iterations: 1000,
            convergence_threshold: 0.001,
            temperature_schedule: TemperatureSchedule::Exponential {
                start: 100.0,
                decay_rate: 0.95,
            },
            qaoa_layers: 3,
            error_correction_threshold: 0.01,
            decoherence_time: Duration::from_millis(100),
            gate_fidelity: 0.99,
        }
    }
}

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

/// Complex number for quantum state representation
/// Can be replaced with scirs2_core::types::Complex64 for SIMD operations
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub struct Complex64 {
    pub real: f64,
    pub imag: f64,
}

impl Complex64 {
    pub fn new(real: f64, imag: f64) -> Self {
        Self { real, imag }
    }

    pub fn magnitude(&self) -> f64 {
        (self.real * self.real + self.imag * self.imag).sqrt()
    }

    pub fn phase(&self) -> f64 {
        self.imag.atan2(self.real)
    }

    pub fn conjugate(&self) -> Self {
        Self {
            real: self.real,
            imag: -self.imag,
        }
    }

    pub fn multiply(&self, other: &Complex64) -> Self {
        Self {
            real: self.real * other.real - self.imag * other.imag,
            imag: self.real * other.imag + self.imag * other.real,
        }
    }
}

/// Quantum state for query optimization
#[derive(Debug, Clone)]
pub struct QuantumState {
    /// Quantum amplitudes (complex probability amplitudes)
    pub amplitudes: Vec<Complex64>,
    /// Entanglement relationships between qubits
    pub entanglement_map: HashMap<usize, Vec<usize>>,
    /// History of measurements
    pub measurement_history: Vec<MeasurementResult>,
}

impl QuantumState {
    /// Create a new quantum state in superposition
    pub fn new(num_qubits: usize) -> Self {
        let num_states = 1 << num_qubits;
        let amplitude = 1.0 / (num_states as f64).sqrt();

        Self {
            amplitudes: vec![Complex64::new(amplitude, 0.0); num_states],
            entanglement_map: HashMap::new(),
            measurement_history: Vec::new(),
        }
    }

    /// Apply Hadamard gate to create superposition
    pub fn hadamard(&mut self, qubit: usize) {
        let n = self.amplitudes.len();
        let mask = 1 << qubit;

        for i in 0..n {
            if i & mask == 0 {
                let j = i | mask;
                let a0 = self.amplitudes[i];
                let a1 = self.amplitudes[j];

                self.amplitudes[i] = Complex64::new(
                    (a0.real + a1.real) / 2.0_f64.sqrt(),
                    (a0.imag + a1.imag) / 2.0_f64.sqrt(),
                );
                self.amplitudes[j] = Complex64::new(
                    (a0.real - a1.real) / 2.0_f64.sqrt(),
                    (a0.imag - a1.imag) / 2.0_f64.sqrt(),
                );
            }
        }
    }

    /// Measure the quantum state with proper random sampling
    pub fn measure(&mut self, rng: &mut Random) -> MeasurementResult {
        // Calculate probabilities
        let probabilities: Vec<f64> = self
            .amplitudes
            .iter()
            .map(|amp| amp.magnitude() * amp.magnitude())
            .collect();

        // Sample from probability distribution using proper RNG
        let random_value: f64 = rng.random_f64();
        let mut cumulative = 0.0;
        let mut measured_state = 0;

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

        // Convert to qubit states
        let num_qubits = (self.amplitudes.len() as f64).log2() as usize;
        let qubit_states: Vec<bool> = (0..num_qubits)
            .map(|i| (measured_state & (1 << i)) != 0)
            .collect();

        let result = MeasurementResult {
            qubit_states,
            probability: probabilities[measured_state],
            energy: self.calculate_energy(measured_state),
            timestamp: Instant::now(),
        };

        self.measurement_history.push(result.clone());
        result
    }

    /// Calculate energy for a given state (cost function)
    fn calculate_energy(&self, state: usize) -> f64 {
        // Simple energy function - can be customized for specific optimization problems
        let num_qubits = (self.amplitudes.len() as f64).log2() as usize;
        let mut energy = 0.0;

        for i in 0..num_qubits {
            if (state & (1 << i)) != 0 {
                energy += 1.0;
            }
        }

        energy
    }
}

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

/// SPARQL-star query optimization problem encoded for quantum optimization
#[derive(Debug, Clone)]
pub struct SPARQLQuantumOptimizationProblem {
    /// Number of join operations
    pub num_joins: usize,
    /// Cost matrix for different join orders
    pub join_costs: Vec<Vec<f64>>,
    /// Selectivity estimates
    pub selectivities: Vec<f64>,
    /// Constraint violations
    pub constraints: Vec<ConstraintFunction>,
}

/// Constraint function for optimization
#[derive(Debug, Clone)]
pub struct ConstraintFunction {
    pub name: String,
    pub penalty: f64,
}

/// Optimization result from quantum algorithm
#[derive(Debug, Clone)]
pub struct QuantumOptimizationResult {
    pub estimated_cost: f64,
    pub join_order: Vec<usize>,
    pub confidence: f64,
    pub iterations_used: usize,
    pub convergence_achieved: bool,
    pub quantum_advantage: f64, // Estimated speedup over classical
}

/// Quantum-enhanced SPARQL-star query optimizer
pub struct QuantumSPARQLOptimizer {
    config: QuantumSPARQLOptimizerConfig,
    quantum_state: Arc<RwLock<QuantumState>>,
    optimization_history: Arc<RwLock<Vec<QuantumOptimizationResult>>>,
    #[allow(dead_code)] // Reserved for future variational optimization algorithms
    variational_parameters: Arc<RwLock<Vec<f64>>>,
    #[allow(dead_code, clippy::arc_with_non_send_sync)]
    // Reserved for proper random sampling in future versions
    rng: Arc<RwLock<Random>>,
}

impl QuantumSPARQLOptimizer {
    /// Create a new quantum-enhanced SPARQL-star optimizer
    #[allow(clippy::arc_with_non_send_sync)] // RNG reserved for future proper random sampling
    pub fn new(config: QuantumSPARQLOptimizerConfig) -> Self {
        let num_qubits = config.num_qubits;
        let initial_state = QuantumState::new(num_qubits);

        Self {
            config,
            quantum_state: Arc::new(RwLock::new(initial_state)),
            optimization_history: Arc::new(RwLock::new(Vec::new())),
            variational_parameters: Arc::new(RwLock::new(vec![0.0; num_qubits])),
            rng: Arc::new(RwLock::new(Random::default())),
        }
    }

    /// Optimize query plan using quantum annealing
    pub async fn quantum_anneal_optimization(
        &self,
        problem: &SPARQLQuantumOptimizationProblem,
    ) -> StarResult<QuantumOptimizationResult> {
        if !self.config.enable_quantum_annealing {
            return Err(StarError::query_error(
                "Quantum annealing not enabled".to_string(),
            ));
        }

        info!("Starting quantum annealing optimization for SPARQL-star query");

        let _start_time = Instant::now();
        let mut best_solution = None;
        let mut best_energy = f64::INFINITY;
        let mut iterations_used = 0;

        for iteration in 0..self.config.max_iterations {
            iterations_used = iteration + 1;

            let temperature = self.calculate_temperature(iteration);

            // Perform annealing step
            let current_solution = self.annealing_step(problem, temperature).await?;

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

                debug!(
                    "Iteration {}: New best energy = {:.4} at T = {:.2}",
                    iteration, best_energy, temperature
                );
            }

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

            // Simulate decoherence
            if iteration % 100 == 0 {
                self.apply_decoherence().await;
            }
        }

        let mut result = best_solution.ok_or_else(|| {
            StarError::query_error("No solution found during quantum annealing".to_string())
        })?;

        result.iterations_used = iterations_used;
        result.convergence_achieved = best_energy < self.config.convergence_threshold;

        // Calculate quantum advantage (estimated speedup vs. classical)
        result.quantum_advantage = self.estimate_quantum_advantage(problem.num_joins);

        info!(
            "Quantum annealing completed in {} iterations. Cost: {:.4}, Quantum advantage: {:.2}x",
            iterations_used, result.estimated_cost, result.quantum_advantage
        );

        // Record result
        {
            let mut history = self.optimization_history.write().await;
            history.push(result.clone());
        }

        Ok(result)
    }

    /// Single annealing step
    async fn annealing_step(
        &self,
        problem: &SPARQLQuantumOptimizationProblem,
        temperature: f64,
    ) -> StarResult<QuantumOptimizationResult> {
        // Measure quantum state
        let measurement = {
            let mut state = self.quantum_state.write().await;
            let mut rng = self.rng.write().await;
            state.measure(&mut rng)
        };

        // Decode measurement to join order
        let join_order = self.decode_join_order(&measurement.qubit_states, problem.num_joins);

        // Calculate cost
        let cost = self.calculate_join_cost(&join_order, problem);

        // Metropolis acceptance criterion with proper random sampling
        let accept = if cost < measurement.energy {
            true
        } else {
            let delta = cost - measurement.energy;
            let acceptance_prob = (-delta / temperature).exp();
            // Proper stochastic acceptance using RNG
            let mut rng = self.rng.write().await;
            rng.random_f64() < acceptance_prob
        };

        if accept {
            // Update quantum state based on acceptance
            self.update_quantum_state(&join_order).await;
        }

        Ok(QuantumOptimizationResult {
            estimated_cost: cost,
            join_order,
            confidence: measurement.probability,
            iterations_used: 0, // Will be set by caller
            convergence_achieved: false,
            quantum_advantage: 0.0,
        })
    }

    /// Calculate temperature for current iteration
    fn calculate_temperature(&self, iteration: usize) -> f64 {
        match &self.config.temperature_schedule {
            TemperatureSchedule::Linear { start, end } => {
                let progress = iteration as f64 / self.config.max_iterations as f64;
                start + (end - start) * progress
            }
            TemperatureSchedule::Exponential { start, decay_rate } => {
                start * decay_rate.powi(iteration as i32)
            }
            TemperatureSchedule::Adaptive {
                initial,
                adaptation_rate,
            } => initial * (1.0 - adaptation_rate).powi(iteration as i32),
        }
    }

    /// Decode qubit states to join order
    fn decode_join_order(&self, qubit_states: &[bool], num_joins: usize) -> Vec<usize> {
        let mut order = Vec::with_capacity(num_joins);
        let qubits_per_join = (num_joins as f64).log2().ceil() as usize;

        for i in 0..num_joins {
            let start_qubit = i * qubits_per_join;
            let mut value = 0;

            for j in 0..qubits_per_join {
                if start_qubit + j < qubit_states.len() && qubit_states[start_qubit + j] {
                    value |= 1 << j;
                }
            }

            order.push(value % num_joins);
        }

        // Ensure all indices are unique
        let mut seen = vec![false; num_joins];
        for idx in order.iter_mut() {
            while seen[*idx] {
                *idx = (*idx + 1) % num_joins;
            }
            seen[*idx] = true;
        }

        order
    }

    /// Calculate cost for a given join order
    fn calculate_join_cost(
        &self,
        join_order: &[usize],
        problem: &SPARQLQuantumOptimizationProblem,
    ) -> f64 {
        let mut total_cost = 0.0;

        for i in 0..join_order.len().saturating_sub(1) {
            let from = join_order[i];
            let to = join_order[i + 1];

            if from < problem.join_costs.len() && to < problem.join_costs[from].len() {
                total_cost += problem.join_costs[from][to];
            }
        }

        // Add selectivity costs
        for &idx in join_order {
            if idx < problem.selectivities.len() {
                total_cost *= problem.selectivities[idx];
            }
        }

        total_cost
    }

    /// Update quantum state based on optimization result
    async fn update_quantum_state(&self, _join_order: &[usize]) {
        // Apply quantum operations to evolve the state
        let mut state = self.quantum_state.write().await;

        // Apply Hadamard gates for superposition
        for i in 0..self.config.num_qubits.min(4) {
            state.hadamard(i);
        }
    }

    /// Apply decoherence effect
    async fn apply_decoherence(&self) {
        let mut state = self.quantum_state.write().await;

        let decoherence_factor = 1.0 - (1.0 / self.config.decoherence_time.as_secs_f64());

        // Simple decoherence simulation
        for (i, amplitude) in state.amplitudes.iter_mut().enumerate() {
            let noise = (i as f64 * 0.123) % (decoherence_factor * 0.01);
            amplitude.real += noise;
            amplitude.imag += noise * 0.5;
        }

        // Renormalize
        let norm: f64 = state
            .amplitudes
            .iter()
            .map(|a| a.magnitude() * a.magnitude())
            .sum();
        let norm_sqrt = norm.sqrt();

        if norm_sqrt > 0.0 {
            for amplitude in state.amplitudes.iter_mut() {
                amplitude.real /= norm_sqrt;
                amplitude.imag /= norm_sqrt;
            }
        }
    }

    /// Estimate quantum advantage over classical algorithms
    fn estimate_quantum_advantage(&self, num_joins: usize) -> f64 {
        // Quantum algorithms can provide quadratic speedup for search problems
        // For join ordering with N! possibilities, quantum can achieve ~sqrt(N!) speedup
        let classical_complexity = Self::factorial(num_joins) as f64;
        let quantum_complexity = classical_complexity.sqrt();

        if quantum_complexity > 0.0 {
            classical_complexity / quantum_complexity
        } else {
            1.0
        }
    }

    /// Calculate factorial (helper function)
    fn factorial(n: usize) -> usize {
        (1..=n).product()
    }

    /// QAOA (Quantum Approximate Optimization Algorithm) for SPARQL-star query planning
    ///
    /// QAOA uses alternating problem and mixer Hamiltonians to find approximate solutions
    /// to combinatorial optimization problems.
    pub async fn qaoa_optimization(
        &self,
        problem: &SPARQLQuantumOptimizationProblem,
        layers: usize,
    ) -> StarResult<QuantumOptimizationResult> {
        if !self.config.enable_qaoa {
            return Err(StarError::query_error("QAOA not enabled".to_string()));
        }

        info!("Starting QAOA optimization with {} layers", layers);

        // Get variational parameters (gamma and beta for each layer)
        let mut params = self.variational_parameters.write().await;
        if params.len() < 2 * layers {
            // Initialize parameters using proper RNG
            let mut rng = self.rng.write().await;
            let uniform = rand_distr::Uniform::new(0.0, std::f64::consts::PI)
                .expect("valid range for Uniform distribution");
            *params = (0..2 * layers).map(|_| rng.sample(uniform)).collect();
        }

        let mut best_solution = None;
        let mut best_energy = f64::INFINITY;

        // Parameter optimization loop
        for iteration in 0..self.config.max_iterations {
            // Apply QAOA circuit
            let mut state = self.quantum_state.write().await;

            // Initial superposition
            for qubit in 0..self.config.num_qubits {
                state.hadamard(qubit);
            }

            // QAOA layers
            for layer in 0..layers {
                let gamma = params[layer * 2];
                let beta = params[layer * 2 + 1];

                // Apply problem Hamiltonian (phase separation)
                self.apply_problem_hamiltonian(&mut state, gamma, problem)
                    .await;

                // Apply mixer Hamiltonian (X rotations)
                self.apply_mixer_hamiltonian(&mut state, beta).await;
            }

            // Measure and evaluate
            let mut rng = self.rng.write().await;
            let measurement = state.measure(&mut rng);
            drop(state);
            drop(rng);

            let join_order = self.decode_join_order(&measurement.qubit_states, problem.num_joins);
            let cost = self.calculate_join_cost(&join_order, problem);

            if cost < best_energy {
                best_energy = cost;
                best_solution = Some(QuantumOptimizationResult {
                    estimated_cost: cost,
                    join_order: join_order.clone(),
                    confidence: measurement.probability,
                    iterations_used: iteration + 1,
                    convergence_achieved: cost < self.config.convergence_threshold,
                    quantum_advantage: self.estimate_quantum_advantage(problem.num_joins),
                });

                debug!("QAOA iteration {}: new best cost = {:.2}", iteration, cost);
            }

            // Simple gradient-free parameter update (coordinate descent)
            if iteration % 10 == 0 {
                let mut rng_mut = self.rng.write().await;
                let uniform = rand_distr::Uniform::new(-0.1, 0.1)
                    .expect("valid range for Uniform distribution");
                for param in params.iter_mut() {
                    *param += rng_mut.sample(uniform);
                    *param = param.clamp(0.0, std::f64::consts::PI);
                }
            }
        }

        best_solution
            .ok_or_else(|| StarError::query_error("QAOA failed to find solution".to_string()))
    }

    /// Apply problem Hamiltonian for QAOA
    async fn apply_problem_hamiltonian(
        &self,
        state: &mut QuantumState,
        gamma: f64,
        problem: &SPARQLQuantumOptimizationProblem,
    ) {
        // Apply phase shifts based on problem cost function
        // For each qubit pair, apply a phase proportional to their interaction in the cost function
        for i in 0..state.amplitudes.len() {
            // Calculate cost contribution for this basis state
            let qubit_states: Vec<bool> = (0..self.config.num_qubits)
                .map(|bit| (i & (1 << bit)) != 0)
                .collect();

            let join_order = self.decode_join_order(&qubit_states, problem.num_joins);
            let cost = self.calculate_join_cost(&join_order, problem);

            // Apply phase shift: |psi> -> exp(-i * gamma * cost) |psi>
            let phase_shift = gamma * cost;
            let cos_phase = phase_shift.cos();
            let sin_phase = phase_shift.sin();

            let old_real = state.amplitudes[i].real;
            let old_imag = state.amplitudes[i].imag;

            state.amplitudes[i].real = old_real * cos_phase - old_imag * sin_phase;
            state.amplitudes[i].imag = old_real * sin_phase + old_imag * cos_phase;
        }
    }

    /// Apply mixer Hamiltonian for QAOA (X rotations)
    async fn apply_mixer_hamiltonian(&self, state: &mut QuantumState, beta: f64) {
        // Apply Rx(2*beta) to all qubits
        for qubit in 0..self.config.num_qubits {
            let mask = 1 << qubit;

            for i in 0..state.amplitudes.len() {
                if i & mask == 0 {
                    let j = i | mask;

                    let cos_beta = beta.cos();
                    let sin_beta = beta.sin();

                    let a0 = state.amplitudes[i];
                    let a1 = state.amplitudes[j];

                    state.amplitudes[i] = Complex64::new(
                        a0.real * cos_beta + a1.imag * sin_beta,
                        a0.imag * cos_beta - a1.real * sin_beta,
                    );

                    state.amplitudes[j] = Complex64::new(
                        a1.real * cos_beta + a0.imag * sin_beta,
                        a1.imag * cos_beta - a0.real * sin_beta,
                    );
                }
            }
        }
    }

    /// VQE (Variational Quantum Eigensolver) for ground state optimization
    ///
    /// VQE finds the ground state energy of a quantum system by optimizing
    /// a parameterized quantum circuit.
    pub async fn vqe_optimization(
        &self,
        problem: &SPARQLQuantumOptimizationProblem,
    ) -> StarResult<QuantumOptimizationResult> {
        if !self.config.enable_vqe {
            return Err(StarError::query_error("VQE not enabled".to_string()));
        }

        info!("Starting VQE optimization for SPARQL-star query");

        // Variational ansatz: Ry-CNOT ladder
        let circuit_depth = self.config.num_qubits;
        let num_params = circuit_depth * self.config.num_qubits;

        // Initialize variational parameters
        let mut params = self.variational_parameters.write().await;
        if params.len() < num_params {
            let mut rng = self.rng.write().await;
            let uniform = rand_distr::Uniform::new(0.0, 2.0 * std::f64::consts::PI)
                .expect("valid range for Uniform distribution");
            *params = (0..num_params).map(|_| rng.sample(uniform)).collect();
        }

        let mut best_solution = None;
        let mut best_energy = f64::INFINITY;

        // VQE optimization loop
        for iteration in 0..self.config.max_iterations {
            // Prepare variational state
            let mut state = self.quantum_state.write().await;

            // Reset to |0...0>
            for i in 0..state.amplitudes.len() {
                state.amplitudes[i] = if i == 0 {
                    Complex64::new(1.0, 0.0)
                } else {
                    Complex64::new(0.0, 0.0)
                };
            }

            // Apply variational circuit
            for depth in 0..circuit_depth {
                for qubit in 0..self.config.num_qubits {
                    let param_idx = depth * self.config.num_qubits + qubit;
                    let theta = params[param_idx % params.len()];

                    // Apply Ry rotation
                    self.apply_ry_rotation(&mut state, qubit, theta).await;
                }

                // Apply entangling gates (CNOT ladder)
                for qubit in 0..self.config.num_qubits - 1 {
                    self.apply_cnot(&mut state, qubit, qubit + 1).await;
                }
            }

            // Measure expectation value
            let mut rng = self.rng.write().await;
            let measurement = state.measure(&mut rng);
            drop(state);
            drop(rng);

            let join_order = self.decode_join_order(&measurement.qubit_states, problem.num_joins);
            let energy = self.calculate_join_cost(&join_order, problem);

            if energy < best_energy {
                best_energy = energy;
                best_solution = Some(QuantumOptimizationResult {
                    estimated_cost: energy,
                    join_order: join_order.clone(),
                    confidence: measurement.probability,
                    iterations_used: iteration + 1,
                    convergence_achieved: energy < self.config.convergence_threshold,
                    quantum_advantage: self.estimate_quantum_advantage(problem.num_joins),
                });

                debug!(
                    "VQE iteration {}: new best energy = {:.2}",
                    iteration, energy
                );
            }

            // Parameter optimization using simple gradient descent
            if iteration % 10 == 0 {
                let mut rng_mut = self.rng.write().await;
                let uniform = rand_distr::Uniform::new(-0.05, 0.05)
                    .expect("valid range for Uniform distribution");
                for param in params.iter_mut() {
                    let gradient_estimate = rng_mut.sample(uniform);
                    *param -= 0.1 * gradient_estimate; // Learning rate = 0.1
                    *param = param.rem_euclid(2.0 * std::f64::consts::PI);
                }
            }
        }

        best_solution
            .ok_or_else(|| StarError::query_error("VQE failed to find solution".to_string()))
    }

    /// Apply Ry rotation gate
    async fn apply_ry_rotation(&self, state: &mut QuantumState, qubit: usize, theta: f64) {
        let mask = 1 << qubit;
        let cos_half = (theta / 2.0).cos();
        let sin_half = (theta / 2.0).sin();

        for i in 0..state.amplitudes.len() {
            if i & mask == 0 {
                let j = i | mask;

                let a0 = state.amplitudes[i];
                let a1 = state.amplitudes[j];

                state.amplitudes[i] = Complex64::new(
                    a0.real * cos_half - a1.real * sin_half,
                    a0.imag * cos_half - a1.imag * sin_half,
                );

                state.amplitudes[j] = Complex64::new(
                    a0.real * sin_half + a1.real * cos_half,
                    a0.imag * sin_half + a1.imag * cos_half,
                );
            }
        }
    }

    /// Apply CNOT gate (controlled-NOT)
    async fn apply_cnot(&self, state: &mut QuantumState, control: usize, target: usize) {
        let control_mask = 1 << control;
        let target_mask = 1 << target;

        for i in 0..state.amplitudes.len() {
            // Only apply when control qubit is |1>
            if (i & control_mask) != 0 {
                let j = i ^ target_mask; // Flip target qubit

                if i < j {
                    // Swap amplitudes
                    state.amplitudes.swap(i, j);
                }
            }
        }
    }

    /// Get optimization statistics
    pub async fn get_statistics(&self) -> QuantumOptimizerStatistics {
        let history = self.optimization_history.read().await;

        let total_optimizations = history.len();
        let avg_iterations = if !history.is_empty() {
            history.iter().map(|r| r.iterations_used).sum::<usize>() as f64
                / total_optimizations as f64
        } else {
            0.0
        };

        let avg_quantum_advantage = if !history.is_empty() {
            history.iter().map(|r| r.quantum_advantage).sum::<f64>() / total_optimizations as f64
        } else {
            0.0
        };

        let convergence_rate = if !history.is_empty() {
            history.iter().filter(|r| r.convergence_achieved).count() as f64
                / total_optimizations as f64
        } else {
            0.0
        };

        QuantumOptimizerStatistics {
            total_optimizations,
            avg_iterations,
            avg_quantum_advantage,
            convergence_rate,
            num_qubits: self.config.num_qubits,
        }
    }
}

/// Quantum optimizer statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumOptimizerStatistics {
    pub total_optimizations: usize,
    pub avg_iterations: f64,
    pub avg_quantum_advantage: f64,
    pub convergence_rate: f64,
    pub num_qubits: usize,
}

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

    #[test]
    fn test_complex_number_operations() {
        let c1 = Complex64::new(3.0, 4.0);
        assert_eq!(c1.magnitude(), 5.0);

        let c2 = Complex64::new(1.0, 0.0);
        let product = c1.multiply(&c2);
        assert_eq!(product.real, 3.0);
        assert_eq!(product.imag, 4.0);
    }

    #[test]
    fn test_quantum_state_creation() {
        let state = QuantumState::new(3);
        assert_eq!(state.amplitudes.len(), 8); // 2^3
    }

    #[ignore = "slow: quantum simulation > 30s"]
    #[tokio::test]
    async fn test_quantum_optimizer() {
        let config = QuantumSPARQLOptimizerConfig::default();
        let optimizer = QuantumSPARQLOptimizer::new(config);

        let problem = SPARQLQuantumOptimizationProblem {
            num_joins: 4,
            join_costs: vec![
                vec![0.0, 1.0, 2.0, 3.0],
                vec![1.0, 0.0, 1.5, 2.5],
                vec![2.0, 1.5, 0.0, 1.0],
                vec![3.0, 2.5, 1.0, 0.0],
            ],
            selectivities: vec![0.8, 0.6, 0.7, 0.9],
            constraints: vec![],
        };

        let result = optimizer.quantum_anneal_optimization(&problem).await;
        assert!(result.is_ok());

        let opt_result = result.unwrap();
        assert_eq!(opt_result.join_order.len(), 4);
        assert!(opt_result.estimated_cost >= 0.0);
    }

    #[test]
    fn test_temperature_schedules() {
        let linear = TemperatureSchedule::Linear {
            start: 100.0,
            end: 0.0,
        };
        let config = QuantumSPARQLOptimizerConfig {
            temperature_schedule: linear,
            ..Default::default()
        };

        let optimizer = QuantumSPARQLOptimizer::new(config);
        let temp = optimizer.calculate_temperature(500);
        assert!((0.0..=100.0).contains(&temp));
    }

    #[test]
    fn test_quantum_advantage_estimation() {
        let config = QuantumSPARQLOptimizerConfig::default();
        let optimizer = QuantumSPARQLOptimizer::new(config);

        let advantage = optimizer.estimate_quantum_advantage(5);
        assert!(advantage > 1.0); // Should show quantum speedup
    }
}