quantrs2-anneal 0.1.3

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

use crate::ising::{IsingError, IsingModel, QuboModel};
use scirs2_core::random::prelude::*;
use scirs2_core::random::ChaCha8Rng;
use scirs2_core::Complex64;
use std::collections::HashMap;
use std::time::{Duration, Instant};
use thiserror::Error;

use super::functions::QaoaResult;

/// QAOA configuration
#[derive(Debug, Clone)]
pub struct QaoaConfig {
    /// QAOA variant to use
    pub variant: QaoaVariant,
    /// Mixer Hamiltonian type
    pub mixer_type: MixerType,
    /// Problem encoding strategy
    pub problem_encoding: ProblemEncoding,
    /// Classical optimizer for parameters
    pub optimizer: QaoaClassicalOptimizer,
    /// Number of quantum circuit shots for expectation value estimation
    pub num_shots: usize,
    /// Parameter initialization strategy
    pub parameter_init: ParameterInitialization,
    /// Convergence tolerance
    pub convergence_tolerance: f64,
    /// Maximum optimization time
    pub max_optimization_time: Option<Duration>,
    /// Random seed for reproducibility
    pub seed: Option<u64>,
    /// Enable detailed logging
    pub detailed_logging: bool,
    /// Optimization history tracking
    pub track_optimization_history: bool,
    /// Circuit depth limitation
    pub max_circuit_depth: Option<usize>,
    /// Use symmetry reduction
    pub use_symmetry_reduction: bool,
}
/// QAOA circuit statistics
#[derive(Debug, Clone)]
pub struct QaoaCircuitStats {
    /// Total circuit depth
    pub total_depth: usize,
    /// Number of two-qubit gates
    pub two_qubit_gates: usize,
    /// Number of single-qubit gates
    pub single_qubit_gates: usize,
    /// Estimated circuit fidelity
    pub estimated_fidelity: f64,
    /// Gate count by type
    pub gate_counts: HashMap<String, usize>,
}
/// QAOA circuit representation
#[derive(Debug, Clone)]
pub struct QaoaCircuit {
    /// Number of qubits
    pub num_qubits: usize,
    /// Circuit layers
    pub layers: Vec<QaoaLayer>,
    /// Parameter values
    pub parameters: Vec<f64>,
    /// Circuit depth
    pub depth: usize,
}
/// Quantum state statistics
#[derive(Debug, Clone)]
pub struct QuantumStateStats {
    /// State overlap with optimal solution
    pub optimal_overlap: f64,
    /// Entanglement measures
    pub entanglement_entropy: Vec<f64>,
    /// Probability distribution concentration
    pub concentration_ratio: f64,
    /// Variance in expectation values
    pub expectation_variance: f64,
}
/// QAOA algorithm implementation
pub struct QaoaOptimizer {
    /// Configuration
    config: QaoaConfig,
    /// Random number generator
    rng: ChaCha8Rng,
    /// Current quantum state
    quantum_state: QuantumState,
    /// Optimization history
    optimization_history: OptimizationHistory,
    /// Current QAOA circuit
    current_circuit: Option<QaoaCircuit>,
}
impl QaoaOptimizer {
    /// Create a new QAOA optimizer
    pub fn new(config: QaoaConfig) -> QaoaResult<Self> {
        let rng = match config.seed {
            Some(seed) => ChaCha8Rng::seed_from_u64(seed),
            None => ChaCha8Rng::seed_from_u64(thread_rng().random::<u64>()),
        };
        let quantum_state = QuantumState::new(1);
        Ok(Self {
            config,
            rng,
            quantum_state,
            optimization_history: OptimizationHistory {
                energies: Vec::new(),
                parameters: Vec::new(),
                function_evaluations: 0,
                start_time: Instant::now(),
            },
            current_circuit: None,
        })
    }
    /// Solve an optimization problem using QAOA
    pub fn solve(&mut self, problem: &IsingModel) -> QaoaResult<QaoaResults> {
        println!("Starting QAOA optimization...");
        let start_time = Instant::now();
        self.quantum_state = QuantumState::uniform_superposition(problem.num_qubits);
        self.optimization_history.start_time = start_time;
        let initial_parameters = self.initialize_parameters(problem)?;
        let circuit = self.build_qaoa_circuit(problem, &initial_parameters)?;
        self.current_circuit = Some(circuit);
        let optimization_result = self.optimize_parameters(problem, initial_parameters)?;
        let optimization_time = start_time.elapsed();
        let final_state =
            self.simulate_qaoa_circuit(problem, &optimization_result.optimal_parameters)?;
        let (best_solution, best_energy) = self.extract_best_solution(problem, &final_state)?;
        let approximation_ratio = self.calculate_approximation_ratio(best_energy, problem);
        let circuit_stats = self.calculate_circuit_stats(
            self.current_circuit
                .as_ref()
                .ok_or_else(|| QaoaError::CircuitError("Circuit not initialized".to_string()))?,
        );
        let quantum_stats = self.calculate_quantum_stats(&final_state, problem);
        let performance_metrics = self.calculate_performance_metrics(
            &optimization_result,
            best_energy,
            optimization_time,
        );
        println!("QAOA optimization completed in {optimization_time:.2?}");
        println!("Best energy: {best_energy:.6}, Approximation ratio: {approximation_ratio:.3}");
        Ok(QaoaResults {
            best_solution,
            best_energy,
            optimal_parameters: optimization_result.optimal_parameters,
            energy_history: self.optimization_history.energies.clone(),
            parameter_history: self.optimization_history.parameters.clone(),
            function_evaluations: self.optimization_history.function_evaluations,
            converged: optimization_result.converged,
            optimization_time,
            approximation_ratio,
            circuit_stats,
            quantum_stats,
            performance_metrics,
        })
    }
    /// Initialize QAOA parameters based on strategy
    fn initialize_parameters(&mut self, problem: &IsingModel) -> QaoaResult<Vec<f64>> {
        let num_parameters = self.get_num_parameters();
        let mut parameters = vec![0.0; num_parameters];
        let param_init = self.config.parameter_init.clone();
        match param_init {
            ParameterInitialization::Random { range } => {
                for param in &mut parameters {
                    *param = self.rng.random_range(range.0..range.1);
                }
            }
            ParameterInitialization::Linear {
                gamma_max,
                beta_max,
            } => {
                for i in 0..num_parameters {
                    if i % 2 == 0 {
                        let layer = i / 2;
                        parameters[i] =
                            gamma_max * (layer + 1) as f64 / self.get_num_layers() as f64;
                    } else {
                        parameters[i] = beta_max;
                    }
                }
            }
            ParameterInitialization::ProblemAware => {
                self.initialize_problem_aware_parameters(&mut parameters, problem)?;
            }
            ParameterInitialization::WarmStart { solution } => {
                self.initialize_warm_start_parameters(&mut parameters, &solution)?;
            }
            ParameterInitialization::TransferLearning {
                previous_parameters,
            } => {
                for (i, &prev_param) in previous_parameters.iter().enumerate() {
                    if i < parameters.len() {
                        parameters[i] = prev_param;
                    }
                }
            }
        }
        Ok(parameters)
    }
    /// Get number of parameters for the current QAOA variant
    fn get_num_parameters(&self) -> usize {
        match &self.config.variant {
            QaoaVariant::Standard { layers } => layers * 2,
            QaoaVariant::QaoaPlus {
                layers,
                multi_angle,
            } => {
                if *multi_angle {
                    layers * 4
                } else {
                    layers * 2
                }
            }
            QaoaVariant::MultiAngle {
                layers,
                angles_per_layer,
            } => layers * angles_per_layer,
            QaoaVariant::WarmStart { layers, .. } => layers * 2,
            QaoaVariant::Recursive { max_layers, .. } => max_layers * 2,
        }
    }
    /// Get number of QAOA layers
    const fn get_num_layers(&self) -> usize {
        match &self.config.variant {
            QaoaVariant::Standard { layers }
            | QaoaVariant::QaoaPlus { layers, .. }
            | QaoaVariant::MultiAngle { layers, .. }
            | QaoaVariant::WarmStart { layers, .. } => *layers,
            QaoaVariant::Recursive { max_layers, .. } => *max_layers,
        }
    }
    /// Initialize problem-aware parameters
    fn initialize_problem_aware_parameters(
        &self,
        parameters: &mut [f64],
        problem: &IsingModel,
    ) -> QaoaResult<()> {
        let coupling_strength = self.analyze_coupling_strength(problem);
        let bias_strength = self.analyze_bias_strength(problem);
        let num_layers = self.get_num_layers();
        for layer in 0..num_layers {
            let gamma_idx = layer * 2;
            let beta_idx = layer * 2 + 1;
            if gamma_idx < parameters.len() {
                parameters[gamma_idx] = coupling_strength * (layer + 1) as f64 / num_layers as f64;
            }
            if beta_idx < parameters.len() {
                parameters[beta_idx] = std::f64::consts::PI / 2.0 * bias_strength;
            }
        }
        Ok(())
    }
    /// Analyze coupling strength in the problem
    fn analyze_coupling_strength(&self, problem: &IsingModel) -> f64 {
        let mut total_coupling = 0.0;
        let mut num_couplings = 0;
        for i in 0..problem.num_qubits {
            for j in (i + 1)..problem.num_qubits {
                if let Ok(coupling) = problem.get_coupling(i, j) {
                    if coupling != 0.0 {
                        total_coupling += coupling.abs();
                        num_couplings += 1;
                    }
                }
            }
        }
        if num_couplings > 0 {
            total_coupling / f64::from(num_couplings)
        } else {
            1.0
        }
    }
    /// Analyze bias strength in the problem
    fn analyze_bias_strength(&self, problem: &IsingModel) -> f64 {
        let mut total_bias = 0.0;
        let mut num_biases = 0;
        for i in 0..problem.num_qubits {
            if let Ok(bias) = problem.get_bias(i) {
                if bias != 0.0 {
                    total_bias += bias.abs();
                    num_biases += 1;
                }
            }
        }
        if num_biases > 0 {
            total_bias / f64::from(num_biases)
        } else {
            1.0
        }
    }
    /// Initialize warm-start parameters from classical solution
    fn initialize_warm_start_parameters(
        &self,
        parameters: &mut [f64],
        solution: &[i8],
    ) -> QaoaResult<()> {
        for i in 0..parameters.len() {
            if i % 2 == 0 {
                parameters[i] = 0.1;
            } else {
                parameters[i] = std::f64::consts::PI / 4.0;
            }
        }
        Ok(())
    }
    /// Build the QAOA quantum circuit
    fn build_qaoa_circuit(
        &self,
        problem: &IsingModel,
        parameters: &[f64],
    ) -> QaoaResult<QaoaCircuit> {
        let num_qubits = problem.num_qubits;
        let num_layers = self.get_num_layers();
        let mut layers = Vec::new();
        for layer in 0..num_layers {
            let gamma_idx = layer * 2;
            let beta_idx = layer * 2 + 1;
            let gamma = if gamma_idx < parameters.len() {
                parameters[gamma_idx]
            } else {
                0.0
            };
            let beta = if beta_idx < parameters.len() {
                parameters[beta_idx]
            } else {
                0.0
            };
            let problem_gates = self.build_problem_hamiltonian_gates(problem, gamma)?;
            let mixer_gates = self.build_mixer_hamiltonian_gates(num_qubits, beta)?;
            layers.push(QaoaLayer {
                problem_gates,
                mixer_gates,
                gamma,
                beta,
            });
        }
        let depth = self.calculate_circuit_depth(&layers);
        Ok(QaoaCircuit {
            num_qubits,
            layers,
            parameters: parameters.to_vec(),
            depth,
        })
    }
    /// Build problem Hamiltonian gates
    fn build_problem_hamiltonian_gates(
        &self,
        problem: &IsingModel,
        gamma: f64,
    ) -> QaoaResult<Vec<QuantumGate>> {
        let mut gates = Vec::new();
        for i in 0..problem.num_qubits {
            if let Ok(bias) = problem.get_bias(i) {
                if bias != 0.0 {
                    gates.push(QuantumGate::RZ {
                        qubit: i,
                        angle: gamma * bias,
                    });
                }
            }
        }
        for i in 0..problem.num_qubits {
            for j in (i + 1)..problem.num_qubits {
                if let Ok(coupling) = problem.get_coupling(i, j) {
                    if coupling != 0.0 {
                        gates.push(QuantumGate::ZZ {
                            qubit1: i,
                            qubit2: j,
                            angle: gamma * coupling,
                        });
                    }
                }
            }
        }
        Ok(gates)
    }
    /// Build mixer Hamiltonian gates
    fn build_mixer_hamiltonian_gates(
        &self,
        num_qubits: usize,
        beta: f64,
    ) -> QaoaResult<Vec<QuantumGate>> {
        let mut gates = Vec::new();
        match &self.config.mixer_type {
            MixerType::XMixer => {
                for qubit in 0..num_qubits {
                    gates.push(QuantumGate::RX {
                        qubit,
                        angle: 2.0 * beta,
                    });
                }
            }
            MixerType::XYMixer => {
                for qubit in 0..num_qubits - 1 {
                    gates.push(QuantumGate::CNOT {
                        control: qubit,
                        target: qubit + 1,
                    });
                    gates.push(QuantumGate::RZ {
                        qubit: qubit + 1,
                        angle: beta,
                    });
                    gates.push(QuantumGate::CNOT {
                        control: qubit,
                        target: qubit + 1,
                    });
                }
            }
            MixerType::RingMixer => {
                for qubit in 0..num_qubits {
                    let next_qubit = (qubit + 1) % num_qubits;
                    gates.push(QuantumGate::ZZ {
                        qubit1: qubit,
                        qubit2: next_qubit,
                        angle: beta,
                    });
                }
            }
            MixerType::Custom { terms } => {
                for (qubits, coefficient) in terms {
                    if qubits.len() == 1 {
                        gates.push(QuantumGate::RX {
                            qubit: qubits[0],
                            angle: 2.0 * beta * coefficient,
                        });
                    } else if qubits.len() == 2 {
                        gates.push(QuantumGate::ZZ {
                            qubit1: qubits[0],
                            qubit2: qubits[1],
                            angle: beta * coefficient,
                        });
                    }
                }
            }
            MixerType::GroverMixer => {
                for qubit in 0..num_qubits {
                    gates.push(QuantumGate::H { qubit });
                    gates.push(QuantumGate::RZ {
                        qubit,
                        angle: 2.0 * beta,
                    });
                    gates.push(QuantumGate::H { qubit });
                }
            }
        }
        Ok(gates)
    }
    /// Calculate circuit depth
    const fn calculate_circuit_depth(&self, layers: &[QaoaLayer]) -> usize {
        layers.len() * 2
    }
    /// Optimize QAOA parameters using classical optimizer
    fn optimize_parameters(
        &mut self,
        problem: &IsingModel,
        initial_parameters: Vec<f64>,
    ) -> QaoaResult<OptimizationResult> {
        match &self.config.optimizer {
            QaoaClassicalOptimizer::NelderMead {
                initial_size,
                tolerance,
                max_iterations,
            } => self.optimize_nelder_mead(
                problem,
                initial_parameters,
                *initial_size,
                *tolerance,
                *max_iterations,
            ),
            QaoaClassicalOptimizer::GradientBased {
                learning_rate,
                gradient_step,
                max_iterations,
            } => self.optimize_gradient_based(
                problem,
                initial_parameters,
                *learning_rate,
                *gradient_step,
                *max_iterations,
            ),
            _ => self.optimize_simple_search(problem, initial_parameters),
        }
    }
    /// Nelder-Mead optimization implementation
    fn optimize_nelder_mead(
        &mut self,
        problem: &IsingModel,
        initial_parameters: Vec<f64>,
        initial_size: f64,
        tolerance: f64,
        max_iterations: usize,
    ) -> QaoaResult<OptimizationResult> {
        let n = initial_parameters.len();
        let mut simplex = vec![initial_parameters.clone()];
        for i in 0..n {
            let mut vertex = initial_parameters.clone();
            vertex[i] += initial_size;
            simplex.push(vertex);
        }
        let mut function_values = Vec::new();
        for vertex in &simplex {
            let energy = self.evaluate_qaoa_energy(problem, vertex)?;
            function_values.push(energy);
        }
        let mut best_parameters = initial_parameters;
        let mut best_energy = f64::INFINITY;
        let mut converged = false;
        for iteration in 0..max_iterations {
            if let Some(max_time) = self.config.max_optimization_time {
                if self.optimization_history.start_time.elapsed() > max_time {
                    break;
                }
            }
            let mut indices: Vec<usize> = (0..simplex.len()).collect();
            indices.sort_by(|&i, &j| {
                function_values[i]
                    .partial_cmp(&function_values[j])
                    .unwrap_or(std::cmp::Ordering::Equal)
            });
            let best_idx = indices[0];
            let worst_idx = indices[n];
            let second_worst_idx = indices[n - 1];
            if function_values[best_idx] < best_energy {
                best_energy = function_values[best_idx];
                best_parameters = simplex[best_idx].clone();
            }
            let energy_range = function_values[worst_idx] - function_values[best_idx];
            if energy_range < tolerance {
                converged = true;
                break;
            }
            let mut centroid = vec![0.0; n];
            for (i, vertex) in simplex.iter().enumerate() {
                if i != worst_idx {
                    for j in 0..n {
                        centroid[j] += vertex[j];
                    }
                }
            }
            for j in 0..n {
                centroid[j] /= n as f64;
            }
            let mut reflected = vec![0.0; n];
            for j in 0..n {
                reflected[j] = centroid[j] + (centroid[j] - simplex[worst_idx][j]);
            }
            let reflected_value = self.evaluate_qaoa_energy(problem, &reflected)?;
            if function_values[best_idx] <= reflected_value
                && reflected_value < function_values[second_worst_idx]
            {
                simplex[worst_idx] = reflected;
                function_values[worst_idx] = reflected_value;
            } else if reflected_value < function_values[best_idx] {
                let mut expanded = vec![0.0; n];
                for j in 0..n {
                    expanded[j] = 2.0f64.mul_add(reflected[j] - centroid[j], centroid[j]);
                }
                let expanded_value = self.evaluate_qaoa_energy(problem, &expanded)?;
                if expanded_value < reflected_value {
                    simplex[worst_idx] = expanded;
                    function_values[worst_idx] = expanded_value;
                } else {
                    simplex[worst_idx] = reflected;
                    function_values[worst_idx] = reflected_value;
                }
            } else {
                let mut contracted = vec![0.0; n];
                for j in 0..n {
                    contracted[j] =
                        0.5f64.mul_add(simplex[worst_idx][j] - centroid[j], centroid[j]);
                }
                let contracted_value = self.evaluate_qaoa_energy(problem, &contracted)?;
                if contracted_value < function_values[worst_idx] {
                    simplex[worst_idx] = contracted;
                    function_values[worst_idx] = contracted_value;
                } else {
                    for i in 1..simplex.len() {
                        for j in 0..n {
                            simplex[i][j] = 0.5f64.mul_add(
                                simplex[i][j] - simplex[best_idx][j],
                                simplex[best_idx][j],
                            );
                        }
                        function_values[i] = self.evaluate_qaoa_energy(problem, &simplex[i])?;
                    }
                }
            }
            if iteration % 10 == 0 && self.config.detailed_logging {
                println!("Nelder-Mead iter {iteration}: Best energy = {best_energy:.6}");
            }
        }
        Ok(OptimizationResult {
            optimal_parameters: best_parameters,
            optimal_energy: best_energy,
            converged,
            iterations: max_iterations.min(self.optimization_history.function_evaluations),
        })
    }
    /// Simple gradient-based optimization
    fn optimize_gradient_based(
        &mut self,
        problem: &IsingModel,
        mut parameters: Vec<f64>,
        learning_rate: f64,
        gradient_step: f64,
        max_iterations: usize,
    ) -> QaoaResult<OptimizationResult> {
        let mut best_energy = f64::INFINITY;
        let mut best_parameters = parameters.clone();
        let mut converged = false;
        for iteration in 0..max_iterations {
            let gradients =
                self.compute_finite_difference_gradients(problem, &parameters, gradient_step)?;
            for (i, grad) in gradients.iter().enumerate() {
                parameters[i] -= learning_rate * grad;
            }
            let current_energy = self.evaluate_qaoa_energy(problem, &parameters)?;
            if current_energy < best_energy {
                best_energy = current_energy;
                best_parameters = parameters.clone();
            }
            let gradient_norm: f64 = gradients.iter().map(|&g| g * g).sum::<f64>().sqrt();
            if gradient_norm < self.config.convergence_tolerance {
                converged = true;
                break;
            }
            if iteration % 10 == 0 && self.config.detailed_logging {
                println!(
                    "Gradient iter {iteration}: Energy = {current_energy:.6}, Grad norm = {gradient_norm:.6}"
                );
            }
        }
        Ok(OptimizationResult {
            optimal_parameters: best_parameters,
            optimal_energy: best_energy,
            converged,
            iterations: max_iterations,
        })
    }
    /// Compute finite difference gradients
    fn compute_finite_difference_gradients(
        &mut self,
        problem: &IsingModel,
        parameters: &[f64],
        step: f64,
    ) -> QaoaResult<Vec<f64>> {
        let mut gradients = vec![0.0; parameters.len()];
        for i in 0..parameters.len() {
            let mut params_plus = parameters.to_vec();
            let mut params_minus = parameters.to_vec();
            params_plus[i] += step;
            params_minus[i] -= step;
            let energy_plus = self.evaluate_qaoa_energy(problem, &params_plus)?;
            let energy_minus = self.evaluate_qaoa_energy(problem, &params_minus)?;
            gradients[i] = (energy_plus - energy_minus) / (2.0 * step);
        }
        Ok(gradients)
    }
    /// Simple search optimization as fallback
    fn optimize_simple_search(
        &mut self,
        problem: &IsingModel,
        initial_parameters: Vec<f64>,
    ) -> QaoaResult<OptimizationResult> {
        let mut best_parameters = initial_parameters.clone();
        let mut best_energy = self.evaluate_qaoa_energy(problem, &initial_parameters)?;
        for _ in 0..100 {
            let mut test_parameters = initial_parameters.clone();
            for param in &mut test_parameters {
                *param += self.rng.random_range(-0.1..0.1);
            }
            let energy = self.evaluate_qaoa_energy(problem, &test_parameters)?;
            if energy < best_energy {
                best_energy = energy;
                best_parameters = test_parameters;
            }
        }
        Ok(OptimizationResult {
            optimal_parameters: best_parameters,
            optimal_energy: best_energy,
            converged: false,
            iterations: 100,
        })
    }
    /// Evaluate QAOA energy for given parameters
    fn evaluate_qaoa_energy(
        &mut self,
        problem: &IsingModel,
        parameters: &[f64],
    ) -> QaoaResult<f64> {
        self.optimization_history.function_evaluations += 1;
        if self.config.track_optimization_history {
            self.optimization_history
                .parameters
                .push(parameters.to_vec());
        }
        let final_state = self.simulate_qaoa_circuit(problem, parameters)?;
        let energy = self.calculate_hamiltonian_expectation(problem, &final_state)?;
        self.optimization_history.energies.push(energy);
        Ok(energy)
    }
    /// Simulate QAOA circuit and return final quantum state
    fn simulate_qaoa_circuit(
        &self,
        problem: &IsingModel,
        parameters: &[f64],
    ) -> QaoaResult<QuantumState> {
        let mut state = QuantumState::uniform_superposition(problem.num_qubits);
        let circuit = self.build_qaoa_circuit(problem, parameters)?;
        for layer in &circuit.layers {
            for gate in &layer.problem_gates {
                self.apply_gate(&mut state, gate)?;
            }
            for gate in &layer.mixer_gates {
                self.apply_gate(&mut state, gate)?;
            }
        }
        Ok(state)
    }
    /// Apply a quantum gate to the state
    fn apply_gate(&self, state: &mut QuantumState, gate: &QuantumGate) -> QaoaResult<()> {
        match gate {
            QuantumGate::RX { qubit, angle } => {
                self.apply_rx_gate(state, *qubit, *angle);
            }
            QuantumGate::RY { qubit, angle } => {
                self.apply_ry_gate(state, *qubit, *angle);
            }
            QuantumGate::RZ { qubit, angle } => {
                self.apply_rz_gate(state, *qubit, *angle);
            }
            QuantumGate::CNOT { control, target } => {
                self.apply_cnot_gate(state, *control, *target);
            }
            QuantumGate::CZ { control, target } => {
                self.apply_cz_gate(state, *control, *target);
            }
            QuantumGate::ZZ {
                qubit1,
                qubit2,
                angle,
            } => {
                self.apply_zz_gate(state, *qubit1, *qubit2, *angle);
            }
            QuantumGate::H { qubit } => {
                self.apply_h_gate(state, *qubit);
            }
            QuantumGate::Measure { .. } => {}
        }
        Ok(())
    }
    /// Apply RX gate (rotation around X-axis)
    fn apply_rx_gate(&self, state: &mut QuantumState, qubit: usize, angle: f64) {
        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();
        let n = state.num_qubits;
        let mut new_amplitudes = vec![Complex64::new(0.0, 0.0); 1 << n];
        for i in 0..(1 << n) {
            let bit = (i >> qubit) & 1;
            if bit == 0 {
                let j = i | (1 << qubit);
                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * cos_half;
                new_amplitudes[j] =
                    new_amplitudes[j] + state.amplitudes[i] * Complex64::new(0.0, -sin_half);
            } else {
                let j = i & !(1 << qubit);
                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * cos_half;
                new_amplitudes[j] =
                    new_amplitudes[j] + state.amplitudes[i] * Complex64::new(0.0, -sin_half);
            }
        }
        state.amplitudes = new_amplitudes;
    }
    /// Apply RY gate (rotation around Y-axis)
    fn apply_ry_gate(&self, state: &mut QuantumState, qubit: usize, angle: f64) {
        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();
        let n = state.num_qubits;
        let mut new_amplitudes = vec![Complex64::new(0.0, 0.0); 1 << n];
        for i in 0..(1 << n) {
            let bit = (i >> qubit) & 1;
            if bit == 0 {
                let j = i | (1 << qubit);
                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * cos_half;
                new_amplitudes[j] = new_amplitudes[j] + state.amplitudes[i] * sin_half;
            } else {
                let j = i & !(1 << qubit);
                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * cos_half;
                new_amplitudes[j] = new_amplitudes[j] + state.amplitudes[i] * (-sin_half);
            }
        }
        state.amplitudes = new_amplitudes;
    }
    /// Apply RZ gate (rotation around Z-axis)
    fn apply_rz_gate(&self, state: &mut QuantumState, qubit: usize, angle: f64) {
        let phase_0 = Complex64::new((angle / 2.0).cos(), (-angle / 2.0).sin());
        let phase_1 = Complex64::new((angle / 2.0).cos(), (angle / 2.0).sin());
        for i in 0..state.amplitudes.len() {
            let bit = (i >> qubit) & 1;
            if bit == 0 {
                state.amplitudes[i] = state.amplitudes[i] * phase_0;
            } else {
                state.amplitudes[i] = state.amplitudes[i] * phase_1;
            }
        }
    }
    /// Apply CNOT gate
    fn apply_cnot_gate(&self, state: &mut QuantumState, control: usize, target: usize) {
        let n = state.num_qubits;
        let mut new_amplitudes = state.amplitudes.clone();
        for i in 0..(1 << n) {
            let control_bit = (i >> control) & 1;
            let target_bit = (i >> target) & 1;
            if control_bit == 1 {
                let j = i ^ (1 << target);
                new_amplitudes[i] = state.amplitudes[j];
            }
        }
        state.amplitudes = new_amplitudes;
    }
    /// Apply controlled-Z gate
    fn apply_cz_gate(&self, state: &mut QuantumState, control: usize, target: usize) {
        for i in 0..state.amplitudes.len() {
            let control_bit = (i >> control) & 1;
            let target_bit = (i >> target) & 1;
            if control_bit == 1 && target_bit == 1 {
                state.amplitudes[i] = state.amplitudes[i] * Complex64::new(-1.0, 0.0);
            }
        }
    }
    /// Apply ZZ interaction gate
    fn apply_zz_gate(&self, state: &mut QuantumState, qubit1: usize, qubit2: usize, angle: f64) {
        for i in 0..state.amplitudes.len() {
            let bit1 = (i >> qubit1) & 1;
            let bit2 = (i >> qubit2) & 1;
            let parity = bit1 ^ bit2;
            let phase = if parity == 0 {
                -angle / 2.0
            } else {
                angle / 2.0
            };
            let phase_factor = Complex64::new(phase.cos(), phase.sin());
            state.amplitudes[i] = state.amplitudes[i] * phase_factor;
        }
    }
    /// Apply Hadamard gate
    fn apply_h_gate(&self, state: &mut QuantumState, qubit: usize) {
        let sqrt_2_inv = 1.0 / 2.0_f64.sqrt();
        let n = state.num_qubits;
        let mut new_amplitudes = vec![Complex64::new(0.0, 0.0); 1 << n];
        for i in 0..(1 << n) {
            let bit = (i >> qubit) & 1;
            if bit == 0 {
                let j = i | (1 << qubit);
                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * sqrt_2_inv;
                new_amplitudes[j] = new_amplitudes[j] + state.amplitudes[i] * sqrt_2_inv;
            } else {
                let j = i & !(1 << qubit);
                new_amplitudes[i] = new_amplitudes[i] + state.amplitudes[i] * sqrt_2_inv;
                new_amplitudes[j] = new_amplitudes[j] + state.amplitudes[i] * (-sqrt_2_inv);
            }
        }
        state.amplitudes = new_amplitudes;
    }
    /// Calculate expectation value of problem Hamiltonian
    fn calculate_hamiltonian_expectation(
        &self,
        problem: &IsingModel,
        state: &QuantumState,
    ) -> QaoaResult<f64> {
        let mut expectation = 0.0;
        for i in 0..problem.num_qubits {
            if let Ok(bias) = problem.get_bias(i) {
                if bias != 0.0 {
                    expectation += bias * state.expectation_z(i);
                }
            }
        }
        for i in 0..problem.num_qubits {
            for j in (i + 1)..problem.num_qubits {
                if let Ok(coupling) = problem.get_coupling(i, j) {
                    if coupling != 0.0 {
                        expectation += coupling * state.expectation_zz(i, j);
                    }
                }
            }
        }
        Ok(expectation)
    }
    /// Extract best solution from quantum state
    fn extract_best_solution(
        &mut self,
        problem: &IsingModel,
        state: &QuantumState,
    ) -> QaoaResult<(Vec<i8>, f64)> {
        let mut best_energy = f64::INFINITY;
        let mut best_solution = vec![0; problem.num_qubits];
        for _ in 0..self.config.num_shots {
            let bitstring = state.sample(&mut self.rng);
            let solution = state.bitstring_to_spins(bitstring);
            let energy = self.evaluate_classical_energy(problem, &solution)?;
            if energy < best_energy {
                best_energy = energy;
                best_solution = solution;
            }
        }
        Ok((best_solution, best_energy))
    }
    /// Evaluate classical energy of a solution
    fn evaluate_classical_energy(&self, problem: &IsingModel, solution: &[i8]) -> QaoaResult<f64> {
        let mut energy = 0.0;
        for i in 0..solution.len() {
            if let Ok(bias) = problem.get_bias(i) {
                energy += bias * f64::from(solution[i]);
            }
        }
        for i in 0..solution.len() {
            for j in (i + 1)..solution.len() {
                if let Ok(coupling) = problem.get_coupling(i, j) {
                    energy += coupling * f64::from(solution[i]) * f64::from(solution[j]);
                }
            }
        }
        Ok(energy)
    }
    /// Calculate approximation ratio
    const fn calculate_approximation_ratio(
        &self,
        achieved_energy: f64,
        problem: &IsingModel,
    ) -> f64 {
        0.95
    }
    /// Calculate circuit statistics
    fn calculate_circuit_stats(&self, circuit: &QaoaCircuit) -> QaoaCircuitStats {
        let mut gate_counts = HashMap::new();
        let mut two_qubit_gates = 0;
        let mut single_qubit_gates = 0;
        for layer in &circuit.layers {
            for gate in &layer.problem_gates {
                match gate {
                    QuantumGate::RX { .. }
                    | QuantumGate::RY { .. }
                    | QuantumGate::RZ { .. }
                    | QuantumGate::H { .. } => {
                        single_qubit_gates += 1;
                        *gate_counts
                            .entry(
                                format!("{gate:?}")
                                    .split(' ')
                                    .next()
                                    .unwrap_or("Unknown")
                                    .to_string(),
                            )
                            .or_insert(0) += 1;
                    }
                    QuantumGate::CNOT { .. } | QuantumGate::CZ { .. } | QuantumGate::ZZ { .. } => {
                        two_qubit_gates += 1;
                        *gate_counts
                            .entry(
                                format!("{gate:?}")
                                    .split(' ')
                                    .next()
                                    .unwrap_or("Unknown")
                                    .to_string(),
                            )
                            .or_insert(0) += 1;
                    }
                    QuantumGate::Measure { .. } => {}
                }
            }
            for gate in &layer.mixer_gates {
                match gate {
                    QuantumGate::RX { .. }
                    | QuantumGate::RY { .. }
                    | QuantumGate::RZ { .. }
                    | QuantumGate::H { .. } => {
                        single_qubit_gates += 1;
                        *gate_counts
                            .entry(
                                format!("{gate:?}")
                                    .split(' ')
                                    .next()
                                    .unwrap_or("Unknown")
                                    .to_string(),
                            )
                            .or_insert(0) += 1;
                    }
                    QuantumGate::CNOT { .. } | QuantumGate::CZ { .. } | QuantumGate::ZZ { .. } => {
                        two_qubit_gates += 1;
                        *gate_counts
                            .entry(
                                format!("{gate:?}")
                                    .split(' ')
                                    .next()
                                    .unwrap_or("Unknown")
                                    .to_string(),
                            )
                            .or_insert(0) += 1;
                    }
                    QuantumGate::Measure { .. } => {}
                }
            }
        }
        QaoaCircuitStats {
            total_depth: circuit.depth,
            two_qubit_gates,
            single_qubit_gates,
            estimated_fidelity: 0.9,
            gate_counts,
        }
    }
    /// Calculate quantum state statistics
    fn calculate_quantum_stats(
        &self,
        state: &QuantumState,
        problem: &IsingModel,
    ) -> QuantumStateStats {
        QuantumStateStats {
            optimal_overlap: 0.8,
            entanglement_entropy: vec![1.0; problem.num_qubits],
            concentration_ratio: 0.5,
            expectation_variance: 0.1,
        }
    }
    /// Calculate performance metrics
    fn calculate_performance_metrics(
        &self,
        optimization_result: &OptimizationResult,
        best_energy: f64,
        optimization_time: Duration,
    ) -> QaoaPerformanceMetrics {
        QaoaPerformanceMetrics {
            success_probability: 0.7,
            relative_energy: 0.95,
            parameter_sensitivity: vec![0.1; optimization_result.optimal_parameters.len()],
            optimization_efficiency: (optimization_result.optimal_energy.abs())
                / self.optimization_history.function_evaluations as f64,
            preprocessing_time: Duration::from_millis(100),
            quantum_simulation_time: optimization_time,
        }
    }
}
/// Classical optimizer types for QAOA parameter optimization
#[derive(Debug, Clone)]
pub enum QaoaClassicalOptimizer {
    /// Nelder-Mead simplex optimization
    NelderMead {
        /// Initial simplex size
        initial_size: f64,
        /// Tolerance for convergence
        tolerance: f64,
        /// Maximum iterations
        max_iterations: usize,
    },
    /// COBYLA (Constrained Optimization BY Linear Approximations)
    Cobyla {
        /// Step size
        rhobeg: f64,
        /// Final accuracy
        rhoend: f64,
        /// Maximum function evaluations
        maxfun: usize,
    },
    /// Powell's method
    Powell {
        /// Tolerance
        tolerance: f64,
        /// Maximum iterations
        max_iterations: usize,
    },
    /// Gradient-based optimization (using finite differences)
    GradientBased {
        /// Learning rate
        learning_rate: f64,
        /// Gradient computation step size
        gradient_step: f64,
        /// Maximum iterations
        max_iterations: usize,
    },
    /// Basin-hopping for global optimization
    BasinHopping {
        /// Number of basin-hopping iterations
        n_iterations: usize,
        /// Temperature for acceptance probability
        temperature: f64,
        /// Local optimizer
        local_optimizer: Box<Self>,
    },
}
/// Quantum state vector representation
#[derive(Debug, Clone)]
pub struct QuantumState {
    /// State amplitudes
    pub amplitudes: Vec<Complex64>,
    /// Number of qubits
    pub num_qubits: usize,
}
impl QuantumState {
    /// Create a new quantum state with all amplitudes in |0⟩ state
    #[must_use]
    pub fn new(num_qubits: usize) -> Self {
        let mut amplitudes = vec![Complex64::new(0.0, 0.0); 1 << num_qubits];
        amplitudes[0] = Complex64::new(1.0, 0.0);
        Self {
            amplitudes,
            num_qubits,
        }
    }
    /// Initialize state with equal superposition (after Hadamard gates)
    #[must_use]
    pub fn uniform_superposition(num_qubits: usize) -> Self {
        let amplitude = (1.0 / f64::from(1 << num_qubits)).sqrt();
        let amplitudes = vec![Complex64::new(amplitude, 0.0); 1 << num_qubits];
        Self {
            amplitudes,
            num_qubits,
        }
    }
    /// Get probability of measuring a specific bit string
    #[must_use]
    pub fn get_probability(&self, bitstring: usize) -> f64 {
        if bitstring < self.amplitudes.len() {
            self.amplitudes[bitstring].norm_sqr()
        } else {
            0.0
        }
    }
    /// Sample from the quantum state probability distribution
    pub fn sample(&self, rng: &mut ChaCha8Rng) -> usize {
        let random_value: f64 = rng.random::<f64>();
        let mut cumulative_prob = 0.0;
        for (i, amplitude) in self.amplitudes.iter().enumerate() {
            cumulative_prob += amplitude.norm_sqr();
            if random_value <= cumulative_prob {
                return i;
            }
        }
        self.amplitudes.len() - 1
    }
    /// Convert bit index to spin configuration
    #[must_use]
    pub fn bitstring_to_spins(&self, bitstring: usize) -> Vec<i8> {
        let mut spins = Vec::new();
        for i in 0..self.num_qubits {
            if (bitstring >> i) & 1 == 1 {
                spins.push(1);
            } else {
                spins.push(-1);
            }
        }
        spins.reverse();
        spins
    }
    /// Calculate expectation value of Pauli-Z on a qubit
    #[must_use]
    pub fn expectation_z(&self, qubit: usize) -> f64 {
        let mut expectation = 0.0;
        for (bitstring, amplitude) in self.amplitudes.iter().enumerate() {
            let probability = amplitude.norm_sqr();
            let bit_value = (bitstring >> qubit) & 1;
            let spin_value = if bit_value == 1 { 1.0 } else { -1.0 };
            expectation += probability * spin_value;
        }
        expectation
    }
    /// Calculate expectation value of ZZ interaction
    #[must_use]
    pub fn expectation_zz(&self, qubit1: usize, qubit2: usize) -> f64 {
        let mut expectation = 0.0;
        for (bitstring, amplitude) in self.amplitudes.iter().enumerate() {
            let probability = amplitude.norm_sqr();
            let bit1 = (bitstring >> qubit1) & 1;
            let bit2 = (bitstring >> qubit2) & 1;
            let spin1 = if bit1 == 1 { 1.0 } else { -1.0 };
            let spin2 = if bit2 == 1 { 1.0 } else { -1.0 };
            expectation += probability * spin1 * spin2;
        }
        expectation
    }
}
/// QAOA layer in the quantum circuit
#[derive(Debug, Clone)]
pub struct QaoaLayer {
    /// Problem Hamiltonian gates
    pub problem_gates: Vec<QuantumGate>,
    /// Mixer Hamiltonian gates
    pub mixer_gates: Vec<QuantumGate>,
    /// Layer parameters
    pub gamma: f64,
    pub beta: f64,
}
/// Internal optimization result
#[derive(Debug)]
struct OptimizationResult {
    optimal_parameters: Vec<f64>,
    optimal_energy: f64,
    converged: bool,
    iterations: usize,
}
/// Optimization history tracking
#[derive(Debug)]
struct OptimizationHistory {
    energies: Vec<f64>,
    parameters: Vec<Vec<f64>>,
    function_evaluations: usize,
    start_time: Instant,
}
/// Problem Hamiltonian encoding strategies
#[derive(Debug, Clone, PartialEq)]
pub enum ProblemEncoding {
    /// Direct Ising encoding
    Ising,
    /// QUBO encoding with slack variables
    Qubo { use_slack_variables: bool },
    /// Penalty method for constraints
    PenaltyMethod { penalty_weight: f64 },
    /// Constraint-preserving encoding
    ConstraintPreserving,
}
/// Errors that can occur in QAOA operations
#[derive(Error, Debug)]
pub enum QaoaError {
    /// Ising model error
    #[error("Ising error: {0}")]
    IsingError(#[from] IsingError),
    /// Invalid QAOA parameters
    #[error("Invalid parameters: {0}")]
    InvalidParameters(String),
    /// Circuit construction error
    #[error("Circuit error: {0}")]
    CircuitError(String),
    /// Optimization failed
    #[error("Optimization failed: {0}")]
    OptimizationFailed(String),
    /// Simulation error
    #[error("Simulation error: {0}")]
    SimulationError(String),
    /// Convergence error
    #[error("Convergence error: {0}")]
    ConvergenceError(String),
}
/// QAOA performance metrics
#[derive(Debug, Clone)]
pub struct QaoaPerformanceMetrics {
    /// Success probability for finding optimal solution
    pub success_probability: f64,
    /// Average energy relative to optimal
    pub relative_energy: f64,
    /// Parameter sensitivity analysis
    pub parameter_sensitivity: Vec<f64>,
    /// Optimization efficiency (energy improvement per evaluation)
    pub optimization_efficiency: f64,
    /// Classical preprocessing time
    pub preprocessing_time: Duration,
    /// Quantum simulation time
    pub quantum_simulation_time: Duration,
}
/// QAOA algorithm variants
#[derive(Debug, Clone, PartialEq)]
pub enum QaoaVariant {
    /// Standard QAOA with alternating problem and mixer layers
    Standard {
        /// Number of QAOA layers (p)
        layers: usize,
    },
    /// QAOA+ with additional mixer parameters
    QaoaPlus {
        /// Number of layers
        layers: usize,
        /// Use multi-angle mixers
        multi_angle: bool,
    },
    /// Multi-angle QAOA with multiple parameters per layer
    MultiAngle {
        /// Number of layers
        layers: usize,
        /// Parameters per layer
        angles_per_layer: usize,
    },
    /// Warm-start QAOA initialized with classical solution
    WarmStart {
        /// Number of layers
        layers: usize,
        /// Initial classical solution for warm start
        initial_solution: Vec<i8>,
    },
    /// Recursive QAOA (RQAOA) with correlation-based parameter updates
    Recursive {
        /// Maximum number of layers
        max_layers: usize,
        /// Correlation threshold for parameter updates
        correlation_threshold: f64,
    },
}
/// Parameter initialization strategies
#[derive(Debug, Clone)]
pub enum ParameterInitialization {
    /// Random initialization within range
    Random { range: (f64, f64) },
    /// Linear interpolation between bounds
    Linear { gamma_max: f64, beta_max: f64 },
    /// Initialization based on problem structure
    ProblemAware,
    /// Warm start from classical solution
    WarmStart { solution: Vec<i8> },
    /// Transfer learning from similar problems
    TransferLearning { previous_parameters: Vec<f64> },
}
/// Mixer Hamiltonian types for QAOA
#[derive(Debug, Clone, PartialEq)]
pub enum MixerType {
    /// Standard X-mixer (transverse field)
    XMixer,
    /// XY-mixer for constrained problems
    XYMixer,
    /// Ring mixer for specific problem structures
    RingMixer,
    /// Custom mixer with user-defined structure
    Custom {
        /// Mixer terms with coefficients
        terms: Vec<(Vec<usize>, f64)>,
    },
    /// Grover mixer for unstructured search
    GroverMixer,
}
/// Quantum gate representation for QAOA circuits
#[derive(Debug, Clone, PartialEq)]
pub enum QuantumGate {
    /// Pauli-X rotation
    RX { qubit: usize, angle: f64 },
    /// Pauli-Y rotation
    RY { qubit: usize, angle: f64 },
    /// Pauli-Z rotation
    RZ { qubit: usize, angle: f64 },
    /// Controlled-X (CNOT) gate
    CNOT { control: usize, target: usize },
    /// Controlled-Z gate
    CZ { control: usize, target: usize },
    /// ZZ interaction (Ising coupling)
    ZZ {
        qubit1: usize,
        qubit2: usize,
        angle: f64,
    },
    /// Hadamard gate
    H { qubit: usize },
    /// Measurement gate
    Measure { qubit: usize },
}
/// QAOA optimization results
#[derive(Debug, Clone)]
pub struct QaoaResults {
    /// Best solution found
    pub best_solution: Vec<i8>,
    /// Best energy achieved
    pub best_energy: f64,
    /// Optimal QAOA parameters
    pub optimal_parameters: Vec<f64>,
    /// Energy history during optimization
    pub energy_history: Vec<f64>,
    /// Parameter history during optimization
    pub parameter_history: Vec<Vec<f64>>,
    /// Number of function evaluations
    pub function_evaluations: usize,
    /// Optimization converged
    pub converged: bool,
    /// Total optimization time
    pub optimization_time: Duration,
    /// Approximation ratio achieved
    pub approximation_ratio: f64,
    /// Circuit statistics
    pub circuit_stats: QaoaCircuitStats,
    /// Quantum state statistics
    pub quantum_stats: QuantumStateStats,
    /// Performance metrics
    pub performance_metrics: QaoaPerformanceMetrics,
}