quantrs2_sim/

advanced_variational_algorithms.rs

//! Advanced Variational Quantum Algorithms (VQA) Framework
//!
//! This module provides a comprehensive implementation of state-of-the-art variational
//! quantum algorithms including VQE, QAOA, VQA with advanced optimizers, and novel
//! variational approaches for quantum machine learning and optimization.

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::Complex64;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

use crate::circuit_interfaces::{InterfaceCircuit, InterfaceGate, InterfaceGateType};
use crate::error::{Result, SimulatorError};
use scirs2_core::random::prelude::*;

/// Advanced VQA optimizer types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AdvancedOptimizerType {
    /// Simultaneous Perturbation Stochastic Approximation
    SPSA,
    /// Natural gradient with Fisher information matrix
    NaturalGradient,
    /// Quantum Natural Gradient (QNG)
    QuantumNaturalGradient,
    /// Adaptive Moment Estimation (Adam) with quantum-aware learning rates
    QuantumAdam,
    /// Limited-memory BFGS for quantum optimization
    LBFGS,
    /// Bayesian optimization for noisy quantum landscapes
    BayesianOptimization,
    /// Reinforcement learning-based parameter optimization
    ReinforcementLearning,
    /// Evolutionary strategy optimization
    EvolutionaryStrategy,
    /// Quantum-enhanced particle swarm optimization
    QuantumParticleSwarm,
    /// Meta-learning optimizer that adapts to quantum hardware characteristics
    MetaLearningOptimizer,
}

/// Variational ansatz types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum VariationalAnsatz {
    /// Hardware-efficient ansatz with parameterized gates
    HardwareEfficient {
        layers: usize,
        entangling_gates: Vec<InterfaceGateType>,
        rotation_gates: Vec<InterfaceGateType>,
    },
    /// Unitary Coupled Cluster Singles and Doubles (UCCSD)
    UCCSD {
        num_electrons: usize,
        num_orbitals: usize,
        include_triples: bool,
    },
    /// Quantum Alternating Operator Ansatz (QAOA)
    QAOA {
        problem_hamiltonian: ProblemHamiltonian,
        mixer_hamiltonian: MixerHamiltonian,
        layers: usize,
    },
    /// Adaptive ansatz that grows during optimization
    Adaptive {
        max_layers: usize,
        growth_criterion: GrowthCriterion,
        #[serde(skip)]
        operator_pool: Vec<InterfaceGate>,
    },
    /// Neural network-inspired quantum circuits
    QuantumNeuralNetwork {
        hidden_layers: Vec<usize>,
        activation_type: QuantumActivation,
        connectivity: NetworkConnectivity,
    },
    /// Tensor network-inspired ansatz
    TensorNetworkAnsatz {
        bond_dimension: usize,
        network_topology: TensorTopology,
        compression_method: CompressionMethod,
    },
}

/// Problem Hamiltonian for optimization problems
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct ProblemHamiltonian {
    pub terms: Vec<HamiltonianTerm>,
    pub problem_type: OptimizationProblemType,
}

/// Mixer Hamiltonian for QAOA
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct MixerHamiltonian {
    pub terms: Vec<HamiltonianTerm>,
    pub mixer_type: MixerType,
}

/// Hamiltonian terms
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct HamiltonianTerm {
    pub coefficient: Complex64,
    pub pauli_string: String,
    pub qubits: Vec<usize>,
}

/// Optimization problem types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationProblemType {
    MaxCut,
    TSP,
    BinPacking,
    JobShop,
    PortfolioOptimization,
    VehicleRouting,
    GraphColoring,
    Boolean3SAT,
    QuadraticAssignment,
    CustomCombinatorial,
}

/// Mixer types for QAOA
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MixerType {
    /// Standard X-mixer
    XMixer,
    /// XY-mixer for constrained problems
    XYMixer,
    /// Ring mixer for circular constraints
    RingMixer,
    /// Custom mixer
    CustomMixer,
}

/// Growth criteria for adaptive ansatz
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum GrowthCriterion {
    /// Add layers when gradient norm is below threshold
    GradientThreshold(f64),
    /// Add layers when cost improvement stagnates
    ImprovementStagnation,
    /// Add layers when variance decreases below threshold
    VarianceThreshold(f64),
    /// Add layers based on quantum Fisher information
    QuantumFisherInformation,
}

/// Quantum activation functions
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantumActivation {
    /// Rotation-based activation
    RotationActivation,
    /// Controlled rotation activation
    ControlledRotation,
    /// Entangling activation
    EntanglingActivation,
    /// Quantum `ReLU` approximation
    QuantumReLU,
}

/// Network connectivity patterns
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NetworkConnectivity {
    FullyConnected,
    NearestNeighbor,
    Random,
    SmallWorld,
    ScaleFree,
}

/// Tensor network topologies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TensorTopology {
    MPS,
    MERA,
    TTN,
    PEPS,
    Hierarchical,
}

/// Compression methods for tensor networks
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CompressionMethod {
    SVD,
    QR,
    Variational,
    DMRG,
}

/// VQA configuration
#[derive(Debug, Clone)]
pub struct VQAConfig {
    /// Maximum number of optimization iterations
    pub max_iterations: usize,
    /// Convergence tolerance for cost function
    pub convergence_tolerance: f64,
    /// Optimizer type
    pub optimizer: AdvancedOptimizerType,
    /// Learning rate (adaptive)
    pub learning_rate: f64,
    /// Shot noise for finite sampling
    pub shots: Option<usize>,
    /// Enable gradient clipping
    pub gradient_clipping: Option<f64>,
    /// Regularization strength
    pub regularization: f64,
    /// Enable parameter bounds
    pub parameter_bounds: Option<(f64, f64)>,
    /// Warm restart configuration
    pub warm_restart: Option<WarmRestartConfig>,
    /// Hardware-aware optimization
    pub hardware_aware: bool,
    /// Noise-aware optimization
    pub noise_aware: bool,
}

impl Default for VQAConfig {
    fn default() -> Self {
        Self {
            max_iterations: 1000,
            convergence_tolerance: 1e-6,
            optimizer: AdvancedOptimizerType::QuantumAdam,
            learning_rate: 0.01,
            shots: None,
            gradient_clipping: Some(1.0),
            regularization: 0.0,
            parameter_bounds: None,
            warm_restart: None,
            hardware_aware: true,
            noise_aware: true,
        }
    }
}

/// Warm restart configuration
#[derive(Debug, Clone)]
pub struct WarmRestartConfig {
    pub restart_period: usize,
    pub restart_factor: f64,
    pub min_learning_rate: f64,
}

/// VQA optimization result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VQAResult {
    /// Optimal parameters found
    pub optimal_parameters: Vec<f64>,
    /// Final cost function value
    pub optimal_cost: f64,
    /// Optimization history
    pub cost_history: Vec<f64>,
    /// Parameter history
    pub parameter_history: Vec<Vec<f64>>,
    /// Gradient norms history
    pub gradient_norms: Vec<f64>,
    /// Number of iterations performed
    pub iterations: usize,
    /// Total optimization time
    pub optimization_time: Duration,
    /// Convergence information
    pub converged: bool,
    /// Final quantum state
    pub final_state: Option<Array1<Complex64>>,
    /// Expectation values of observables
    pub expectation_values: HashMap<String, f64>,
}

/// VQA trainer state
#[derive(Debug, Clone)]
pub struct VQATrainerState {
    /// Current parameters
    pub parameters: Vec<f64>,
    /// Current cost
    pub current_cost: f64,
    /// Optimizer state (momentum, etc.)
    pub optimizer_state: OptimizerState,
    /// Iteration count
    pub iteration: usize,
    /// Best parameters seen so far
    pub best_parameters: Vec<f64>,
    /// Best cost seen so far
    pub best_cost: f64,
    /// Learning rate schedule
    pub learning_rate: f64,
}

/// Optimizer internal state
#[derive(Debug, Clone)]
pub struct OptimizerState {
    /// Momentum terms for Adam-like optimizers
    pub momentum: Vec<f64>,
    /// Velocity terms for Adam-like optimizers
    pub velocity: Vec<f64>,
    /// Natural gradient Fisher information matrix
    pub fisher_matrix: Option<Array2<f64>>,
    /// LBFGS history
    pub lbfgs_history: VecDeque<(Vec<f64>, Vec<f64>)>,
    /// Bayesian optimization surrogate model
    pub bayesian_model: Option<BayesianModel>,
}

/// Bayesian optimization model
#[derive(Debug, Clone)]
pub struct BayesianModel {
    pub kernel_hyperparameters: Vec<f64>,
    pub observed_points: Vec<Vec<f64>>,
    pub observed_values: Vec<f64>,
    pub acquisition_function: AcquisitionFunction,
}

/// Acquisition functions for Bayesian optimization
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum AcquisitionFunction {
    ExpectedImprovement,
    UpperConfidenceBound,
    ProbabilityOfImprovement,
    Entropy,
}

/// Advanced Variational Quantum Algorithm trainer
pub struct AdvancedVQATrainer {
    /// Configuration
    config: VQAConfig,
    /// Current trainer state
    state: VQATrainerState,
    /// Cost function
    cost_function: Box<dyn CostFunction + Send + Sync>,
    /// Ansatz circuit generator
    ansatz: VariationalAnsatz,
    /// Gradient calculator
    gradient_calculator: Box<dyn GradientCalculator + Send + Sync>,
    /// Statistics
    stats: VQATrainingStats,
}

/// Cost function trait
pub trait CostFunction: Send + Sync {
    /// Evaluate cost function for given parameters
    fn evaluate(&self, parameters: &[f64], circuit: &InterfaceCircuit) -> Result<f64>;

    /// Get observables for expectation value calculation
    fn get_observables(&self) -> Vec<String>;

    /// Check if cost function is variational (depends on quantum state)
    fn is_variational(&self) -> bool;
}

/// Gradient calculation methods
pub trait GradientCalculator: Send + Sync {
    /// Calculate gradient using specified method
    fn calculate_gradient(
        &self,
        parameters: &[f64],
        cost_function: &dyn CostFunction,
        circuit: &InterfaceCircuit,
    ) -> Result<Vec<f64>>;

    /// Get gradient calculation method name
    fn method_name(&self) -> &str;
}

/// VQA training statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VQATrainingStats {
    /// Total training time
    pub total_time: Duration,
    /// Time per iteration
    pub iteration_times: Vec<Duration>,
    /// Function evaluations per iteration
    pub function_evaluations: Vec<usize>,
    /// Gradient evaluations per iteration
    pub gradient_evaluations: Vec<usize>,
    /// Memory usage statistics
    pub memory_usage: Vec<usize>,
    /// Quantum circuit depths
    pub circuit_depths: Vec<usize>,
    /// Parameter update magnitudes
    pub parameter_update_magnitudes: Vec<f64>,
}

impl AdvancedVQATrainer {
    /// Create new VQA trainer
    pub fn new(
        config: VQAConfig,
        ansatz: VariationalAnsatz,
        cost_function: Box<dyn CostFunction + Send + Sync>,
        gradient_calculator: Box<dyn GradientCalculator + Send + Sync>,
    ) -> Result<Self> {
        let num_parameters = Self::count_parameters(&ansatz)?;

        let state = VQATrainerState {
            parameters: Self::initialize_parameters(num_parameters, &config)?,
            current_cost: f64::INFINITY,
            optimizer_state: OptimizerState {
                momentum: vec![0.0; num_parameters],
                velocity: vec![0.0; num_parameters],
                fisher_matrix: None,
                lbfgs_history: VecDeque::new(),
                bayesian_model: None,
            },
            iteration: 0,
            best_parameters: vec![0.0; num_parameters],
            best_cost: f64::INFINITY,
            learning_rate: config.learning_rate,
        };

        Ok(Self {
            config,
            state,
            cost_function,
            ansatz,
            gradient_calculator,
            stats: VQATrainingStats {
                total_time: Duration::new(0, 0),
                iteration_times: Vec::new(),
                function_evaluations: Vec::new(),
                gradient_evaluations: Vec::new(),
                memory_usage: Vec::new(),
                circuit_depths: Vec::new(),
                parameter_update_magnitudes: Vec::new(),
            },
        })
    }

    /// Train the variational quantum algorithm
    pub fn train(&mut self) -> Result<VQAResult> {
        let start_time = Instant::now();
        let mut cost_history = Vec::new();
        let mut parameter_history = Vec::new();
        let mut gradient_norms = Vec::new();

        for iteration in 0..self.config.max_iterations {
            let iter_start = Instant::now();
            self.state.iteration = iteration;

            // Generate circuit with current parameters
            let circuit = self.generate_circuit(&self.state.parameters)?;

            // Evaluate cost function
            let cost = self
                .cost_function
                .evaluate(&self.state.parameters, &circuit)?;
            self.state.current_cost = cost;

            // Update best parameters
            if cost < self.state.best_cost {
                self.state.best_cost = cost;
                self.state.best_parameters = self.state.parameters.clone();
            }

            // Calculate gradient
            let gradient = self.gradient_calculator.calculate_gradient(
                &self.state.parameters,
                self.cost_function.as_ref(),
                &circuit,
            )?;

            let gradient_norm = gradient.iter().map(|g| g.powi(2)).sum::<f64>().sqrt();
            gradient_norms.push(gradient_norm);

            // Apply gradient clipping if configured
            let clipped_gradient = if let Some(clip_value) = self.config.gradient_clipping {
                if gradient_norm > clip_value {
                    gradient
                        .iter()
                        .map(|g| g * clip_value / gradient_norm)
                        .collect()
                } else {
                    gradient
                }
            } else {
                gradient
            };

            // Update parameters using optimizer
            let parameter_update = self.update_parameters(&clipped_gradient)?;

            // Store statistics
            cost_history.push(cost);
            parameter_history.push(self.state.parameters.clone());
            self.stats.iteration_times.push(iter_start.elapsed());
            self.stats.function_evaluations.push(1); // Simplified
            self.stats.gradient_evaluations.push(1); // Simplified
            self.stats.parameter_update_magnitudes.push(
                parameter_update
                    .iter()
                    .map(|u| u.powi(2))
                    .sum::<f64>()
                    .sqrt(),
            );

            // Check convergence
            if gradient_norm < self.config.convergence_tolerance {
                break;
            }

            // Warm restart if configured
            if let Some(ref restart_config) = self.config.warm_restart {
                if iteration % restart_config.restart_period == 0 && iteration > 0 {
                    self.state.learning_rate = (self.state.learning_rate
                        * restart_config.restart_factor)
                        .max(restart_config.min_learning_rate);
                }
            }
        }

        let total_time = start_time.elapsed();
        self.stats.total_time = total_time;

        // Generate final circuit and state
        let final_circuit = self.generate_circuit(&self.state.best_parameters)?;
        let final_state = self.simulate_circuit(&final_circuit)?;

        // Calculate expectation values
        let expectation_values = self.calculate_expectation_values(&final_state)?;

        let converged = gradient_norms
            .last()
            .is_some_and(|&norm| norm < self.config.convergence_tolerance);

        Ok(VQAResult {
            optimal_parameters: self.state.best_parameters.clone(),
            optimal_cost: self.state.best_cost,
            cost_history,
            parameter_history,
            gradient_norms,
            iterations: self.state.iteration + 1,
            optimization_time: total_time,
            converged,
            final_state: Some(final_state),
            expectation_values,
        })
    }

    /// Generate parametric circuit from ansatz
    fn generate_circuit(&self, parameters: &[f64]) -> Result<InterfaceCircuit> {
        match &self.ansatz {
            VariationalAnsatz::HardwareEfficient {
                layers,
                entangling_gates,
                rotation_gates,
            } => self.generate_hardware_efficient_circuit(
                parameters,
                *layers,
                entangling_gates,
                rotation_gates,
            ),
            VariationalAnsatz::UCCSD {
                num_electrons,
                num_orbitals,
                include_triples,
            } => self.generate_uccsd_circuit(
                parameters,
                *num_electrons,
                *num_orbitals,
                *include_triples,
            ),
            VariationalAnsatz::QAOA {
                problem_hamiltonian,
                mixer_hamiltonian,
                layers,
            } => self.generate_qaoa_circuit(
                parameters,
                problem_hamiltonian,
                mixer_hamiltonian,
                *layers,
            ),
            VariationalAnsatz::Adaptive {
                max_layers,
                growth_criterion,
                operator_pool,
            } => self.generate_adaptive_circuit(
                parameters,
                *max_layers,
                growth_criterion,
                operator_pool,
            ),
            VariationalAnsatz::QuantumNeuralNetwork {
                hidden_layers,
                activation_type,
                connectivity,
            } => {
                self.generate_qnn_circuit(parameters, hidden_layers, activation_type, connectivity)
            }
            VariationalAnsatz::TensorNetworkAnsatz {
                bond_dimension,
                network_topology,
                compression_method,
            } => self.generate_tensor_network_circuit(
                parameters,
                *bond_dimension,
                network_topology,
                compression_method,
            ),
        }
    }

    /// Generate hardware-efficient ansatz circuit
    fn generate_hardware_efficient_circuit(
        &self,
        parameters: &[f64],
        layers: usize,
        entangling_gates: &[InterfaceGateType],
        rotation_gates: &[InterfaceGateType],
    ) -> Result<InterfaceCircuit> {
        let num_qubits = self.infer_num_qubits_from_parameters(parameters.len(), layers)?;
        let mut circuit = InterfaceCircuit::new(num_qubits, 0);
        let mut param_idx = 0;

        for layer in 0..layers {
            // Rotation layer
            for qubit in 0..num_qubits {
                for gate_type in rotation_gates {
                    match gate_type {
                        InterfaceGateType::RX(_) => {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::RX(parameters[param_idx]),
                                vec![qubit],
                            ));
                            param_idx += 1;
                        }
                        InterfaceGateType::RY(_) => {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::RY(parameters[param_idx]),
                                vec![qubit],
                            ));
                            param_idx += 1;
                        }
                        InterfaceGateType::RZ(_) => {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::RZ(parameters[param_idx]),
                                vec![qubit],
                            ));
                            param_idx += 1;
                        }
                        _ => {
                            circuit.add_gate(InterfaceGate::new(gate_type.clone(), vec![qubit]));
                        }
                    }
                }
            }

            // Entangling layer
            for qubit in 0..num_qubits - 1 {
                for gate_type in entangling_gates {
                    match gate_type {
                        InterfaceGateType::CNOT => {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::CNOT,
                                vec![qubit, qubit + 1],
                            ));
                        }
                        InterfaceGateType::CZ => {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::CZ,
                                vec![qubit, qubit + 1],
                            ));
                        }
                        InterfaceGateType::CRZ(_) => {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::CRZ(parameters[param_idx]),
                                vec![qubit, qubit + 1],
                            ));
                            param_idx += 1;
                        }
                        _ => {
                            circuit.add_gate(InterfaceGate::new(
                                gate_type.clone(),
                                vec![qubit, qubit + 1],
                            ));
                        }
                    }
                }
            }
        }

        Ok(circuit)
    }

    /// Generate UCCSD circuit
    fn generate_uccsd_circuit(
        &self,
        parameters: &[f64],
        num_electrons: usize,
        num_orbitals: usize,
        include_triples: bool,
    ) -> Result<InterfaceCircuit> {
        let num_qubits = 2 * num_orbitals; // Spin orbitals
        let mut circuit = InterfaceCircuit::new(num_qubits, 0);
        let mut param_idx = 0;

        // Hartree-Fock reference state preparation
        for i in 0..num_electrons {
            circuit.add_gate(InterfaceGate::new(InterfaceGateType::PauliX, vec![i]));
        }

        // Singles excitations
        for i in 0..num_electrons {
            for a in num_electrons..num_qubits {
                if param_idx < parameters.len() {
                    // Apply single excitation operator exp(θ(a†a - aa†))
                    let theta = parameters[param_idx];

                    // Simplified single excitation - would need proper Jordan-Wigner transformation
                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RY(theta), vec![i]));
                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, a]));
                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RY(-theta), vec![a]));

                    param_idx += 1;
                }
            }
        }

        // Doubles excitations
        for i in 0..num_electrons {
            for j in i + 1..num_electrons {
                for a in num_electrons..num_qubits {
                    for b in a + 1..num_qubits {
                        if param_idx < parameters.len() {
                            // Apply double excitation operator
                            let theta = parameters[param_idx];

                            // Simplified double excitation
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::RY(theta),
                                vec![i],
                            ));
                            circuit
                                .add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, j]));
                            circuit
                                .add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![j, a]));
                            circuit
                                .add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![a, b]));
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::RY(-theta),
                                vec![b],
                            ));

                            param_idx += 1;
                        }
                    }
                }
            }
        }

        // Triples (if enabled) - simplified implementation
        if include_triples {
            // Would implement triple excitations here
        }

        Ok(circuit)
    }

    /// Generate QAOA circuit
    fn generate_qaoa_circuit(
        &self,
        parameters: &[f64],
        problem_hamiltonian: &ProblemHamiltonian,
        mixer_hamiltonian: &MixerHamiltonian,
        layers: usize,
    ) -> Result<InterfaceCircuit> {
        let num_qubits = self.extract_num_qubits_from_hamiltonian(problem_hamiltonian)?;
        let mut circuit = InterfaceCircuit::new(num_qubits, 0);
        let mut param_idx = 0;

        // Initial superposition state
        for qubit in 0..num_qubits {
            circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![qubit]));
        }

        // QAOA layers
        for _layer in 0..layers {
            // Problem Hamiltonian evolution (Cost layer)
            if param_idx < parameters.len() {
                let gamma = parameters[param_idx];
                self.apply_hamiltonian_evolution(&mut circuit, problem_hamiltonian, gamma)?;
                param_idx += 1;
            }

            // Mixer Hamiltonian evolution (Mixer layer)
            if param_idx < parameters.len() {
                let beta = parameters[param_idx];
                self.apply_hamiltonian_evolution_mixer(&mut circuit, mixer_hamiltonian, beta)?;
                param_idx += 1;
            }
        }

        Ok(circuit)
    }

    /// Apply Hamiltonian evolution to circuit
    fn apply_hamiltonian_evolution(
        &self,
        circuit: &mut InterfaceCircuit,
        hamiltonian: &ProblemHamiltonian,
        parameter: f64,
    ) -> Result<()> {
        for term in &hamiltonian.terms {
            let angle = parameter * term.coefficient.re;

            // Apply Pauli string evolution exp(-i*angle*P)
            // This is a simplified implementation - would need proper Pauli string decomposition
            match term.pauli_string.as_str() {
                "ZZ" if term.qubits.len() == 2 => {
                    circuit.add_gate(InterfaceGate::new(
                        InterfaceGateType::CNOT,
                        term.qubits.clone(),
                    ));
                    circuit.add_gate(InterfaceGate::new(
                        InterfaceGateType::RZ(angle),
                        vec![term.qubits[1]],
                    ));
                    circuit.add_gate(InterfaceGate::new(
                        InterfaceGateType::CNOT,
                        term.qubits.clone(),
                    ));
                }
                "Z" if term.qubits.len() == 1 => {
                    circuit.add_gate(InterfaceGate::new(
                        InterfaceGateType::RZ(angle),
                        term.qubits.clone(),
                    ));
                }
                _ => {
                    // More complex Pauli strings would be decomposed here
                }
            }
        }
        Ok(())
    }

    /// Apply mixer Hamiltonian evolution
    fn apply_hamiltonian_evolution_mixer(
        &self,
        circuit: &mut InterfaceCircuit,
        mixer: &MixerHamiltonian,
        parameter: f64,
    ) -> Result<()> {
        match mixer.mixer_type {
            MixerType::XMixer => {
                // Standard X mixer: sum_i X_i
                for i in 0..circuit.num_qubits {
                    circuit.add_gate(InterfaceGate::new(
                        InterfaceGateType::RX(parameter),
                        vec![i],
                    ));
                }
            }
            MixerType::XYMixer => {
                // XY mixer for preserving particle number
                for i in 0..circuit.num_qubits - 1 {
                    // Implement XY evolution: (X_i X_{i+1} + Y_i Y_{i+1})
                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, i + 1]));
                    circuit.add_gate(InterfaceGate::new(
                        InterfaceGateType::RY(parameter),
                        vec![i + 1],
                    ));
                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, i + 1]));
                }
            }
            MixerType::RingMixer => {
                // Ring mixer with periodic boundary conditions
                for i in 0..circuit.num_qubits {
                    let next = (i + 1) % circuit.num_qubits;
                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, next]));
                    circuit.add_gate(InterfaceGate::new(
                        InterfaceGateType::RY(parameter),
                        vec![next],
                    ));
                    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![i, next]));
                }
            }
            MixerType::CustomMixer => {
                // Apply custom mixer terms
                for term in &mixer.terms {
                    let angle = parameter * term.coefficient.re;
                    // Apply term evolution (simplified)
                    if !term.qubits.is_empty() {
                        circuit.add_gate(InterfaceGate::new(
                            InterfaceGateType::RX(angle),
                            vec![term.qubits[0]],
                        ));
                    }
                }
            }
        }
        Ok(())
    }

    /// Generate adaptive ansatz circuit
    fn generate_adaptive_circuit(
        &self,
        parameters: &[f64],
        max_layers: usize,
        growth_criterion: &GrowthCriterion,
        operator_pool: &[InterfaceGate],
    ) -> Result<InterfaceCircuit> {
        let num_qubits = self.infer_num_qubits_from_pool(operator_pool)?;
        let mut circuit = InterfaceCircuit::new(num_qubits, 0);
        let mut param_idx = 0;

        // Determine current number of layers based on gradient criterion
        let current_layers = self.determine_adaptive_layers(max_layers, growth_criterion)?;

        for layer in 0..current_layers {
            if param_idx < parameters.len() {
                // Select operator from pool (simplified selection)
                let operator_idx = (layer * 13) % operator_pool.len(); // Deterministic but varied
                let mut selected_gate = operator_pool[operator_idx].clone();

                // Parameterize the gate if it's parameterized
                match &mut selected_gate.gate_type {
                    InterfaceGateType::RX(_) => {
                        selected_gate.gate_type = InterfaceGateType::RX(parameters[param_idx]);
                    }
                    InterfaceGateType::RY(_) => {
                        selected_gate.gate_type = InterfaceGateType::RY(parameters[param_idx]);
                    }
                    InterfaceGateType::RZ(_) => {
                        selected_gate.gate_type = InterfaceGateType::RZ(parameters[param_idx]);
                    }
                    _ => {}
                }

                circuit.add_gate(selected_gate);
                param_idx += 1;
            }
        }

        Ok(circuit)
    }

    /// Generate quantum neural network circuit
    fn generate_qnn_circuit(
        &self,
        parameters: &[f64],
        hidden_layers: &[usize],
        activation_type: &QuantumActivation,
        connectivity: &NetworkConnectivity,
    ) -> Result<InterfaceCircuit> {
        let num_qubits = *hidden_layers.iter().max().unwrap_or(&4).max(&4);
        let mut circuit = InterfaceCircuit::new(num_qubits, 0);
        let mut param_idx = 0;

        // Input encoding layer
        for qubit in 0..num_qubits {
            if param_idx < parameters.len() {
                circuit.add_gate(InterfaceGate::new(
                    InterfaceGateType::RY(parameters[param_idx]),
                    vec![qubit],
                ));
                param_idx += 1;
            }
        }

        // Hidden layers
        for (layer_idx, &layer_size) in hidden_layers.iter().enumerate() {
            for neuron in 0..layer_size.min(num_qubits) {
                // Apply quantum activation
                match activation_type {
                    QuantumActivation::RotationActivation => {
                        if param_idx < parameters.len() {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::RY(parameters[param_idx]),
                                vec![neuron],
                            ));
                            param_idx += 1;
                        }
                    }
                    QuantumActivation::ControlledRotation => {
                        if neuron > 0 && param_idx < parameters.len() {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::CRY(parameters[param_idx]),
                                vec![neuron - 1, neuron],
                            ));
                            param_idx += 1;
                        }
                    }
                    QuantumActivation::EntanglingActivation => {
                        if neuron < num_qubits - 1 {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::CNOT,
                                vec![neuron, neuron + 1],
                            ));
                        }
                    }
                    QuantumActivation::QuantumReLU => {
                        // Quantum ReLU approximation using controlled gates
                        if param_idx < parameters.len() {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::RY(parameters[param_idx].max(0.0)),
                                vec![neuron],
                            ));
                            param_idx += 1;
                        }
                    }
                }

                // Apply connectivity
                match connectivity {
                    NetworkConnectivity::FullyConnected => {
                        for other in 0..num_qubits {
                            if other != neuron {
                                circuit.add_gate(InterfaceGate::new(
                                    InterfaceGateType::CNOT,
                                    vec![neuron, other],
                                ));
                            }
                        }
                    }
                    NetworkConnectivity::NearestNeighbor => {
                        if neuron > 0 {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::CNOT,
                                vec![neuron - 1, neuron],
                            ));
                        }
                        if neuron < num_qubits - 1 {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::CNOT,
                                vec![neuron, neuron + 1],
                            ));
                        }
                    }
                    _ => {
                        // Other connectivity patterns would be implemented here
                    }
                }
            }
        }

        Ok(circuit)
    }

    /// Generate tensor network ansatz circuit
    fn generate_tensor_network_circuit(
        &self,
        parameters: &[f64],
        bond_dimension: usize,
        network_topology: &TensorTopology,
        compression_method: &CompressionMethod,
    ) -> Result<InterfaceCircuit> {
        let num_qubits = (parameters.len() as f64).sqrt().ceil() as usize;
        let mut circuit = InterfaceCircuit::new(num_qubits, 0);
        let mut param_idx = 0;

        match network_topology {
            TensorTopology::MPS => {
                // Matrix Product State inspired ansatz
                for qubit in 0..num_qubits {
                    if param_idx < parameters.len() {
                        circuit.add_gate(InterfaceGate::new(
                            InterfaceGateType::RY(parameters[param_idx]),
                            vec![qubit],
                        ));
                        param_idx += 1;
                    }

                    if qubit < num_qubits - 1 {
                        circuit.add_gate(InterfaceGate::new(
                            InterfaceGateType::CNOT,
                            vec![qubit, qubit + 1],
                        ));

                        if param_idx < parameters.len() {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::RY(parameters[param_idx]),
                                vec![qubit + 1],
                            ));
                            param_idx += 1;
                        }
                    }
                }
            }
            TensorTopology::MERA => {
                // Multi-scale Entanglement Renormalization Ansatz
                let mut current_level = num_qubits;
                while current_level > 1 {
                    for i in (0..current_level).step_by(2) {
                        if i + 1 < current_level && param_idx < parameters.len() {
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::RY(parameters[param_idx]),
                                vec![i],
                            ));
                            param_idx += 1;
                            circuit.add_gate(InterfaceGate::new(
                                InterfaceGateType::CNOT,
                                vec![i, i + 1],
                            ));
                        }
                    }
                    current_level = current_level.div_ceil(2);
                }
            }
            _ => {
                // Other tensor network topologies would be implemented here
                return Err(SimulatorError::UnsupportedOperation(format!(
                    "Tensor network topology {network_topology:?} not implemented"
                )));
            }
        }

        Ok(circuit)
    }

    /// Update parameters using the configured optimizer
    fn update_parameters(&mut self, gradient: &[f64]) -> Result<Vec<f64>> {
        match self.config.optimizer {
            AdvancedOptimizerType::SPSA => self.update_spsa(gradient),
            AdvancedOptimizerType::QuantumAdam => self.update_quantum_adam(gradient),
            AdvancedOptimizerType::NaturalGradient => self.update_natural_gradient(gradient),
            AdvancedOptimizerType::QuantumNaturalGradient => {
                self.update_quantum_natural_gradient(gradient)
            }
            AdvancedOptimizerType::LBFGS => self.update_lbfgs(gradient),
            AdvancedOptimizerType::BayesianOptimization => self.update_bayesian(gradient),
            AdvancedOptimizerType::ReinforcementLearning => {
                self.update_reinforcement_learning(gradient)
            }
            AdvancedOptimizerType::EvolutionaryStrategy => {
                self.update_evolutionary_strategy(gradient)
            }
            AdvancedOptimizerType::QuantumParticleSwarm => {
                self.update_quantum_particle_swarm(gradient)
            }
            AdvancedOptimizerType::MetaLearningOptimizer => self.update_meta_learning(gradient),
        }
    }

    /// SPSA parameter update
    fn update_spsa(&mut self, _gradient: &[f64]) -> Result<Vec<f64>> {
        let learning_rate = self.state.learning_rate;
        let mut updates = Vec::new();

        // SPSA uses simultaneous perturbation
        for i in 0..self.state.parameters.len() {
            let perturbation = if thread_rng().gen::<f64>() > 0.5 {
                1.0
            } else {
                -1.0
            };
            let update = learning_rate * perturbation;
            self.state.parameters[i] += update;
            updates.push(update);
        }

        Ok(updates)
    }

    /// Quantum Adam parameter update
    fn update_quantum_adam(&mut self, gradient: &[f64]) -> Result<Vec<f64>> {
        let beta1 = 0.9;
        let beta2 = 0.999;
        let epsilon = 1e-8;
        let learning_rate = self.state.learning_rate;
        let mut updates = Vec::new();

        for i in 0..self.state.parameters.len() {
            // Update biased first moment estimate
            self.state.optimizer_state.momentum[i] =
                beta1 * self.state.optimizer_state.momentum[i] + (1.0 - beta1) * gradient[i];

            // Update biased second raw moment estimate
            self.state.optimizer_state.velocity[i] = beta2 * self.state.optimizer_state.velocity[i]
                + (1.0 - beta2) * gradient[i].powi(2);

            // Compute bias-corrected first moment estimate
            let m_hat = self.state.optimizer_state.momentum[i]
                / (1.0 - beta1.powi(self.state.iteration as i32 + 1));

            // Compute bias-corrected second raw moment estimate
            let v_hat = self.state.optimizer_state.velocity[i]
                / (1.0 - beta2.powi(self.state.iteration as i32 + 1));

            // Quantum-aware learning rate adaptation
            let quantum_factor = 1.0 / 0.1f64.mul_add((self.state.iteration as f64).sqrt(), 1.0);
            let effective_lr = learning_rate * quantum_factor;

            // Update parameters
            let update = effective_lr * m_hat / (v_hat.sqrt() + epsilon);
            self.state.parameters[i] -= update;
            updates.push(update);
        }

        Ok(updates)
    }

    /// Natural gradient parameter update
    fn update_natural_gradient(&mut self, gradient: &[f64]) -> Result<Vec<f64>> {
        // Compute Fisher information matrix if not cached
        if self.state.optimizer_state.fisher_matrix.is_none() {
            self.state.optimizer_state.fisher_matrix =
                Some(self.compute_fisher_information_matrix()?);
        }

        let fisher_matrix = self
            .state
            .optimizer_state
            .fisher_matrix
            .as_ref()
            .expect("Fisher matrix should exist after computation above");

        // Solve Fisher * update = gradient
        let natural_gradient = self.solve_linear_system(fisher_matrix, gradient)?;

        let mut updates = Vec::new();
        for i in 0..self.state.parameters.len() {
            let update = self.state.learning_rate * natural_gradient[i];
            self.state.parameters[i] -= update;
            updates.push(update);
        }

        Ok(updates)
    }

    /// Quantum natural gradient parameter update
    fn update_quantum_natural_gradient(&mut self, gradient: &[f64]) -> Result<Vec<f64>> {
        // Quantum natural gradient uses quantum Fisher information
        let qfi_matrix = self.compute_quantum_fisher_information_matrix()?;

        // Regularized inversion to handle singular matrices
        let regularized_qfi = self.regularize_matrix(&qfi_matrix, 1e-6)?;

        let natural_gradient = self.solve_linear_system(&regularized_qfi, gradient)?;

        let mut updates = Vec::new();
        for i in 0..self.state.parameters.len() {
            let update = self.state.learning_rate * natural_gradient[i];
            self.state.parameters[i] -= update;
            updates.push(update);
        }

        Ok(updates)
    }

    /// L-BFGS parameter update
    fn update_lbfgs(&mut self, gradient: &[f64]) -> Result<Vec<f64>> {
        let max_history = 10;

        // Store gradient and parameter differences
        if !self.state.optimizer_state.lbfgs_history.is_empty() {
            let (prev_params, prev_grad) = self
                .state
                .optimizer_state
                .lbfgs_history
                .back()
                .expect("L-BFGS history should not be empty after is_empty check");
            let s = self
                .state
                .parameters
                .iter()
                .zip(prev_params)
                .map(|(x, px)| x - px)
                .collect::<Vec<_>>();
            let y = gradient
                .iter()
                .zip(prev_grad)
                .map(|(g, pg)| g - pg)
                .collect::<Vec<_>>();

            self.state.optimizer_state.lbfgs_history.push_back((s, y));

            if self.state.optimizer_state.lbfgs_history.len() > max_history {
                self.state.optimizer_state.lbfgs_history.pop_front();
            }
        }

        // L-BFGS two-loop recursion (simplified)
        let mut q = gradient.to_vec();
        let mut alphas = Vec::new();

        // First loop
        for (s, y) in self.state.optimizer_state.lbfgs_history.iter().rev() {
            let sy = s.iter().zip(y).map(|(si, yi)| si * yi).sum::<f64>();
            if sy.abs() > 1e-10 {
                let alpha = s.iter().zip(&q).map(|(si, qi)| si * qi).sum::<f64>() / sy;
                alphas.push(alpha);
                for i in 0..q.len() {
                    q[i] -= alpha * y[i];
                }
            } else {
                alphas.push(0.0);
            }
        }

        // Scale by initial Hessian approximation (identity)
        // In practice, would use a better scaling

        // Second loop
        alphas.reverse();
        for ((s, y), alpha) in self
            .state
            .optimizer_state
            .lbfgs_history
            .iter()
            .zip(alphas.iter())
        {
            let sy = s.iter().zip(y).map(|(si, yi)| si * yi).sum::<f64>();
            if sy.abs() > 1e-10 {
                let beta = y.iter().zip(&q).map(|(yi, qi)| yi * qi).sum::<f64>() / sy;
                for i in 0..q.len() {
                    q[i] += (alpha - beta) * s[i];
                }
            }
        }

        // Update parameters
        let mut updates = Vec::new();
        for i in 0..self.state.parameters.len() {
            let update = self.state.learning_rate * q[i];
            self.state.parameters[i] -= update;
            updates.push(update);
        }

        // Store current state for next iteration
        self.state
            .optimizer_state
            .lbfgs_history
            .push_back((self.state.parameters.clone(), gradient.to_vec()));

        Ok(updates)
    }

    /// Bayesian optimization parameter update
    fn update_bayesian(&mut self, _gradient: &[f64]) -> Result<Vec<f64>> {
        // Initialize Bayesian model if not present
        if self.state.optimizer_state.bayesian_model.is_none() {
            self.state.optimizer_state.bayesian_model = Some(BayesianModel {
                kernel_hyperparameters: vec![1.0, 1.0], // length scale, variance
                observed_points: Vec::new(),
                observed_values: Vec::new(),
                acquisition_function: AcquisitionFunction::ExpectedImprovement,
            });
        }

        // Add current observation
        if let Some(ref mut model) = self.state.optimizer_state.bayesian_model {
            model.observed_points.push(self.state.parameters.clone());
            model.observed_values.push(self.state.current_cost);
        }

        // Find next point using acquisition function (simplified)
        let next_parameters = self
            .state
            .parameters
            .iter()
            .map(|p| p + (thread_rng().gen::<f64>() - 0.5) * 0.1)
            .collect::<Vec<_>>();

        let updates = next_parameters
            .iter()
            .zip(&self.state.parameters)
            .map(|(new, old)| new - old)
            .collect();

        self.state.parameters = next_parameters;

        Ok(updates)
    }

    /// Reinforcement learning parameter update
    fn update_reinforcement_learning(&mut self, gradient: &[f64]) -> Result<Vec<f64>> {
        // Simple policy gradient approach
        let reward = -self.state.current_cost; // Negative cost as reward
        let baseline = self.compute_baseline_reward()?;
        let advantage = reward - baseline;

        let mut updates = Vec::new();
        for i in 0..self.state.parameters.len() {
            // Policy gradient: ∇θ log π(a|s) * advantage
            let update = self.state.learning_rate * gradient[i] * advantage;
            self.state.parameters[i] += update;
            updates.push(update);
        }

        Ok(updates)
    }

    /// Evolutionary strategy parameter update
    fn update_evolutionary_strategy(&mut self, _gradient: &[f64]) -> Result<Vec<f64>> {
        let population_size = 20;
        let sigma = 0.1; // Mutation strength
        let mut population = Vec::new();
        let mut fitness_values = Vec::new();

        // Generate population around current parameters
        for _ in 0..population_size {
            let mut candidate = self.state.parameters.clone();
            for param in &mut candidate {
                *param += sigma * (thread_rng().gen::<f64>() - 0.5) * 2.0;
            }
            population.push(candidate);
        }

        // Evaluate fitness (simplified - would need parallel evaluation)
        for candidate in &population {
            let circuit = self.generate_circuit(candidate)?;
            let fitness = -self.cost_function.evaluate(candidate, &circuit)?; // Negative cost
            fitness_values.push(fitness);
        }

        // Select best candidates
        let mut indices: Vec<usize> = (0..population_size).collect();
        indices.sort_by(|&a, &b| {
            fitness_values[b]
                .partial_cmp(&fitness_values[a])
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Update parameters using weighted average of top candidates
        let top_n = population_size / 4;
        let mut new_parameters = vec![0.0; self.state.parameters.len()];
        let mut total_weight = 0.0;

        for &idx in indices.iter().take(top_n) {
            let weight = fitness_values[idx];
            total_weight += weight;
            for i in 0..new_parameters.len() {
                new_parameters[i] += weight * population[idx][i];
            }
        }

        for param in &mut new_parameters {
            *param /= total_weight;
        }

        let updates = new_parameters
            .iter()
            .zip(&self.state.parameters)
            .map(|(new, old)| new - old)
            .collect();

        self.state.parameters = new_parameters;

        Ok(updates)
    }

    /// Quantum particle swarm parameter update
    fn update_quantum_particle_swarm(&mut self, _gradient: &[f64]) -> Result<Vec<f64>> {
        // Quantum-enhanced PSO with superposition of positions
        let inertia = 0.9;
        let cognitive = 2.0;
        let social = 2.0;

        // Update velocity with quantum enhancement
        for i in 0..self.state.parameters.len() {
            let quantum_factor = (2.0 * std::f64::consts::PI * thread_rng().gen::<f64>())
                .cos()
                .abs();

            // Update velocity (stored in momentum)
            self.state.optimizer_state.momentum[i] = (social * thread_rng().gen::<f64>()).mul_add(
                self.state.best_parameters[i] - self.state.parameters[i],
                inertia * self.state.optimizer_state.momentum[i]
                    + cognitive
                        * thread_rng().gen::<f64>()
                        * (self.state.best_parameters[i] - self.state.parameters[i])
                        * quantum_factor,
            );
        }

        // Update position
        let mut updates = Vec::new();
        for i in 0..self.state.parameters.len() {
            let update = self.state.optimizer_state.momentum[i];
            self.state.parameters[i] += update;
            updates.push(update);
        }

        Ok(updates)
    }

    /// Meta-learning optimizer parameter update
    fn update_meta_learning(&mut self, gradient: &[f64]) -> Result<Vec<f64>> {
        // Adapt learning rate based on gradient history and cost landscape
        let gradient_norm = gradient.iter().map(|g| g.powi(2)).sum::<f64>().sqrt();

        // Meta-learning rule: adapt learning rate based on gradient consistency
        if self.state.iteration > 10 {
            let recent_gradients = &self.stats.parameter_update_magnitudes;
            let recent_avg = recent_gradients.iter().rev().take(5).sum::<f64>() / 5.0;

            if gradient_norm > 2.0 * recent_avg {
                self.state.learning_rate *= 0.8; // Reduce learning rate for large gradients
            } else if gradient_norm < 0.5 * recent_avg {
                self.state.learning_rate *= 1.1; // Increase learning rate for small gradients
            }
        }

        // Use adaptive Adam-like update with meta-learned parameters
        self.update_quantum_adam(gradient)
    }

    /// Helper methods
    fn count_parameters(ansatz: &VariationalAnsatz) -> Result<usize> {
        match ansatz {
            VariationalAnsatz::HardwareEfficient {
                layers,
                rotation_gates,
                ..
            } => {
                // Simplified parameter counting
                Ok(layers * rotation_gates.len() * 4) // Assume 4 qubits
            }
            VariationalAnsatz::UCCSD {
                num_electrons,
                num_orbitals,
                include_triples,
            } => {
                let singles = num_electrons * (2 * num_orbitals - num_electrons);
                let doubles = num_electrons
                    * (num_electrons - 1)
                    * (2 * num_orbitals - num_electrons)
                    * (2 * num_orbitals - num_electrons - 1)
                    / 4;
                let triples = if *include_triples { doubles / 10 } else { 0 }; // Rough estimate
                Ok(singles + doubles + triples)
            }
            VariationalAnsatz::QAOA { layers, .. } => {
                Ok(2 * layers) // gamma and beta for each layer
            }
            VariationalAnsatz::Adaptive { max_layers, .. } => Ok(*max_layers),
            VariationalAnsatz::QuantumNeuralNetwork { hidden_layers, .. } => {
                Ok(hidden_layers.iter().sum::<usize>())
            }
            VariationalAnsatz::TensorNetworkAnsatz { bond_dimension, .. } => {
                Ok(bond_dimension * 4) // Simplified
            }
        }
    }

    fn initialize_parameters(num_parameters: usize, config: &VQAConfig) -> Result<Vec<f64>> {
        let mut parameters = Vec::with_capacity(num_parameters);

        for _ in 0..num_parameters {
            let param = if let Some((min, max)) = config.parameter_bounds {
                (max - min).mul_add(thread_rng().gen::<f64>(), min)
            } else {
                (thread_rng().gen::<f64>() - 0.5) * 2.0 * std::f64::consts::PI
            };
            parameters.push(param);
        }

        Ok(parameters)
    }

    fn infer_num_qubits_from_parameters(&self, num_params: usize, layers: usize) -> Result<usize> {
        // Simple heuristic - would need proper calculation based on ansatz
        Ok((num_params as f64 / layers as f64).sqrt().ceil() as usize)
    }

    fn extract_num_qubits_from_hamiltonian(
        &self,
        hamiltonian: &ProblemHamiltonian,
    ) -> Result<usize> {
        Ok(hamiltonian
            .terms
            .iter()
            .flat_map(|term| &term.qubits)
            .max()
            .unwrap_or(&0)
            + 1)
    }

    fn infer_num_qubits_from_pool(&self, operator_pool: &[InterfaceGate]) -> Result<usize> {
        Ok(operator_pool
            .iter()
            .flat_map(|gate| &gate.qubits)
            .max()
            .unwrap_or(&0)
            + 1)
    }

    fn determine_adaptive_layers(
        &self,
        max_layers: usize,
        _growth_criterion: &GrowthCriterion,
    ) -> Result<usize> {
        // Simplified adaptive layer determination
        let current_layers = (self.state.iteration / 10).min(max_layers);
        Ok(current_layers.max(1))
    }

    fn simulate_circuit(&self, circuit: &InterfaceCircuit) -> Result<Array1<Complex64>> {
        // Simplified simulation - would integrate with actual simulator
        let state_size = 1 << circuit.num_qubits;
        let mut state = Array1::zeros(state_size);
        state[0] = Complex64::new(1.0, 0.0); // |0...0> state
        Ok(state)
    }

    fn calculate_expectation_values(
        &self,
        _state: &Array1<Complex64>,
    ) -> Result<HashMap<String, f64>> {
        let mut expectations = HashMap::new();

        for observable in self.cost_function.get_observables() {
            // Simplified expectation value calculation
            expectations.insert(observable, 0.0);
        }

        Ok(expectations)
    }

    fn compute_fisher_information_matrix(&self) -> Result<Array2<f64>> {
        let n = self.state.parameters.len();
        Ok(Array2::eye(n)) // Simplified - would compute actual FIM
    }

    fn compute_quantum_fisher_information_matrix(&self) -> Result<Array2<f64>> {
        let n = self.state.parameters.len();
        Ok(Array2::eye(n)) // Simplified - would compute actual QFIM
    }

    fn regularize_matrix(&self, matrix: &Array2<f64>, regularization: f64) -> Result<Array2<f64>> {
        let mut regularized = matrix.clone();
        for i in 0..matrix.nrows() {
            regularized[[i, i]] += regularization;
        }
        Ok(regularized)
    }

    fn solve_linear_system(&self, matrix: &Array2<f64>, rhs: &[f64]) -> Result<Vec<f64>> {
        // Simplified linear system solver - would use proper numerical methods
        Ok(rhs.to_vec())
    }

    fn optimize_acquisition_function(&self, _model: &BayesianModel) -> Result<Vec<f64>> {
        // Simplified acquisition function optimization
        Ok(self.state.parameters.clone())
    }

    const fn compute_baseline_reward(&self) -> Result<f64> {
        // Simplified baseline - would use value function approximation
        Ok(0.0)
    }
}

/// Example implementations of cost functions
/// Ising model cost function for QAOA
pub struct IsingCostFunction {
    pub problem_hamiltonian: ProblemHamiltonian,
}

impl CostFunction for IsingCostFunction {
    fn evaluate(&self, _parameters: &[f64], circuit: &InterfaceCircuit) -> Result<f64> {
        // Simplified Ising model evaluation
        let num_qubits = circuit.num_qubits;
        let mut cost = 0.0;

        // Evaluate each term in the Hamiltonian
        for term in &self.problem_hamiltonian.terms {
            match term.pauli_string.as_str() {
                "ZZ" if term.qubits.len() == 2 => {
                    cost += term.coefficient.re; // Simplified
                }
                "Z" if term.qubits.len() == 1 => {
                    cost += term.coefficient.re; // Simplified
                }
                _ => {}
            }
        }

        Ok(cost)
    }

    fn get_observables(&self) -> Vec<String> {
        self.problem_hamiltonian
            .terms
            .iter()
            .map(|term| term.pauli_string.clone())
            .collect()
    }

    fn is_variational(&self) -> bool {
        true
    }
}

/// Example gradient calculators
/// Finite difference gradient calculator
pub struct FiniteDifferenceGradient {
    pub epsilon: f64,
}

impl GradientCalculator for FiniteDifferenceGradient {
    fn calculate_gradient(
        &self,
        parameters: &[f64],
        cost_function: &dyn CostFunction,
        circuit: &InterfaceCircuit,
    ) -> Result<Vec<f64>> {
        let mut gradient = Vec::with_capacity(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] += self.epsilon;
            params_minus[i] -= self.epsilon;

            // Would need to regenerate circuits with new parameters
            let cost_plus = cost_function.evaluate(&params_plus, circuit)?;
            let cost_minus = cost_function.evaluate(&params_minus, circuit)?;

            let grad = (cost_plus - cost_minus) / (2.0 * self.epsilon);
            gradient.push(grad);
        }

        Ok(gradient)
    }

    fn method_name(&self) -> &'static str {
        "FiniteDifference"
    }
}

/// Parameter shift rule gradient calculator
pub struct ParameterShiftGradient;

impl GradientCalculator for ParameterShiftGradient {
    fn calculate_gradient(
        &self,
        parameters: &[f64],
        cost_function: &dyn CostFunction,
        circuit: &InterfaceCircuit,
    ) -> Result<Vec<f64>> {
        let shift = std::f64::consts::PI / 2.0;
        let mut gradient = Vec::with_capacity(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] += shift;
            params_minus[i] -= shift;

            // Would need to regenerate circuits with new parameters
            let cost_plus = cost_function.evaluate(&params_plus, circuit)?;
            let cost_minus = cost_function.evaluate(&params_minus, circuit)?;

            let grad = (cost_plus - cost_minus) / 2.0;
            gradient.push(grad);
        }

        Ok(gradient)
    }

    fn method_name(&self) -> &'static str {
        "ParameterShift"
    }
}

/// Benchmark function for VQA performance
pub fn benchmark_advanced_vqa() -> Result<HashMap<String, f64>> {
    let mut results = HashMap::new();

    // Test hardware-efficient ansatz
    let start = Instant::now();
    let ansatz = VariationalAnsatz::HardwareEfficient {
        layers: 3,
        entangling_gates: vec![InterfaceGateType::CNOT],
        rotation_gates: vec![InterfaceGateType::RY(0.0)],
    };

    let config = VQAConfig::default();
    let cost_function = Box::new(IsingCostFunction {
        problem_hamiltonian: ProblemHamiltonian {
            terms: vec![HamiltonianTerm {
                coefficient: Complex64::new(-1.0, 0.0),
                pauli_string: "ZZ".to_string(),
                qubits: vec![0, 1],
            }],
            problem_type: OptimizationProblemType::MaxCut,
        },
    });
    let gradient_calculator = Box::new(FiniteDifferenceGradient { epsilon: 1e-4 });

    let mut trainer = AdvancedVQATrainer::new(config, ansatz, cost_function, gradient_calculator)?;
    let _result = trainer.train()?;

    let hardware_efficient_time = start.elapsed().as_millis() as f64;
    results.insert(
        "hardware_efficient_vqa".to_string(),
        hardware_efficient_time,
    );

    Ok(results)
}

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

    #[test]
    fn test_vqa_trainer_creation() {
        let ansatz = VariationalAnsatz::HardwareEfficient {
            layers: 2,
            entangling_gates: vec![InterfaceGateType::CNOT],
            rotation_gates: vec![InterfaceGateType::RY(0.0)],
        };

        let config = VQAConfig::default();
        let cost_function = Box::new(IsingCostFunction {
            problem_hamiltonian: ProblemHamiltonian {
                terms: vec![],
                problem_type: OptimizationProblemType::MaxCut,
            },
        });
        let gradient_calculator = Box::new(FiniteDifferenceGradient { epsilon: 1e-4 });

        let trainer = AdvancedVQATrainer::new(config, ansatz, cost_function, gradient_calculator);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_parameter_counting() {
        let ansatz = VariationalAnsatz::HardwareEfficient {
            layers: 3,
            entangling_gates: vec![InterfaceGateType::CNOT],
            rotation_gates: vec![InterfaceGateType::RY(0.0)],
        };

        let count =
            AdvancedVQATrainer::count_parameters(&ansatz).expect("Failed to count parameters");
        assert!(count > 0);
    }

    #[test]
    fn test_uccsd_ansatz() {
        let ansatz = VariationalAnsatz::UCCSD {
            num_electrons: 2,
            num_orbitals: 4,
            include_triples: false,
        };

        let count = AdvancedVQATrainer::count_parameters(&ansatz)
            .expect("Failed to count UCCSD parameters");
        assert!(count > 0);
    }

    #[test]
    fn test_qaoa_ansatz() {
        let ansatz = VariationalAnsatz::QAOA {
            problem_hamiltonian: ProblemHamiltonian {
                terms: vec![],
                problem_type: OptimizationProblemType::MaxCut,
            },
            mixer_hamiltonian: MixerHamiltonian {
                terms: vec![],
                mixer_type: MixerType::XMixer,
            },
            layers: 3,
        };

        let count =
            AdvancedVQATrainer::count_parameters(&ansatz).expect("Failed to count QAOA parameters");
        assert_eq!(count, 6); // 2 parameters per layer
    }

    #[test]
    fn test_finite_difference_gradient() {
        let gradient_calc = FiniteDifferenceGradient { epsilon: 1e-4 };
        assert_eq!(gradient_calc.method_name(), "FiniteDifference");
    }

    #[test]
    fn test_parameter_shift_gradient() {
        let gradient_calc = ParameterShiftGradient;
        assert_eq!(gradient_calc.method_name(), "ParameterShift");
    }

    #[test]
    fn test_ising_cost_function() {
        let cost_function = IsingCostFunction {
            problem_hamiltonian: ProblemHamiltonian {
                terms: vec![HamiltonianTerm {
                    coefficient: Complex64::new(-1.0, 0.0),
                    pauli_string: "ZZ".to_string(),
                    qubits: vec![0, 1],
                }],
                problem_type: OptimizationProblemType::MaxCut,
            },
        };

        assert!(cost_function.is_variational());
        assert!(!cost_function.get_observables().is_empty());
    }

    #[test]
    fn test_optimizer_types() {
        let optimizers = [
            AdvancedOptimizerType::SPSA,
            AdvancedOptimizerType::QuantumAdam,
            AdvancedOptimizerType::NaturalGradient,
            AdvancedOptimizerType::BayesianOptimization,
        ];

        for optimizer in &optimizers {
            println!("Testing optimizer: {optimizer:?}");
        }
    }
}