quantrs2-anneal 0.1.3

//! Counterdiabatic Driving Optimizer implementation
//!
//! This module implements counterdiabatic driving protocols for enhanced
//! adiabatic quantum computation and optimization.

use std::collections::HashMap;

use scirs2_core::random::ChaCha8Rng;
use scirs2_core::random::{Rng, SeedableRng};
use scirs2_core::RngExt;

use super::error::{AdvancedQuantumError, AdvancedQuantumResult};
use crate::ising::IsingModel;
use crate::simulator::{AnnealingResult, AnnealingSolution};

/// Counterdiabatic Driving Optimizer
#[derive(Debug, Clone)]
pub struct CounterdiabaticDrivingOptimizer {
    /// CD configuration
    pub config: CounterdiabaticConfig,
    /// Driving protocols
    pub protocols: Vec<CounterdiabaticProtocol>,
    /// Performance metrics
    pub performance_metrics: CounterdiabaticMetrics,
}

/// Configuration for counterdiabatic driving
#[derive(Debug, Clone)]
pub struct CounterdiabaticConfig {
    /// Approximation method
    pub approximation_method: CounterdiabaticApproximation,
    /// Gauge choice
    pub gauge_choice: GaugeChoice,
    /// Adiabatic parameter schedule
    pub parameter_schedule: ParameterSchedule,
    /// Local approximation settings
    pub local_approximation: LocalApproximationSettings,
}

impl Default for CounterdiabaticConfig {
    fn default() -> Self {
        Self {
            approximation_method: CounterdiabaticApproximation::Local,
            gauge_choice: GaugeChoice::Symmetric,
            parameter_schedule: ParameterSchedule::default(),
            local_approximation: LocalApproximationSettings::default(),
        }
    }
}

/// Counterdiabatic approximation methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum CounterdiabaticApproximation {
    /// Exact counterdiabatic terms
    Exact,
    /// Local approximation
    Local,
    /// Nested commutator approximation
    NestedCommutator,
    /// Variational approximation
    Variational,
    /// Machine learning approximation
    MachineLearning,
}

/// Gauge choices for counterdiabatic driving
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum GaugeChoice {
    /// Symmetric gauge
    Symmetric,
    /// Parallel transport gauge
    ParallelTransport,
    /// Diabatic gauge
    Diabatic,
    /// Optimal gauge
    Optimal,
}

/// Parameter schedule for adiabatic evolution
#[derive(Debug, Clone)]
pub struct ParameterSchedule {
    /// Schedule function type
    pub function_type: ScheduleFunctionType,
    /// Schedule parameters
    pub parameters: Vec<f64>,
    /// Total evolution time
    pub total_time: f64,
    /// Time discretization
    pub time_points: Vec<f64>,
}

impl Default for ParameterSchedule {
    fn default() -> Self {
        Self {
            function_type: ScheduleFunctionType::Linear,
            parameters: vec![0.0, 1.0],
            total_time: 10.0,
            time_points: (0..=100).map(|i| f64::from(i) * 0.1).collect(),
        }
    }
}

/// Schedule function types
#[derive(Debug, Clone, PartialEq)]
pub enum ScheduleFunctionType {
    /// Linear schedule
    Linear,
    /// Polynomial schedule
    Polynomial,
    /// Exponential schedule
    Exponential,
    /// Optimized schedule
    Optimized,
    /// Custom schedule
    Custom(Vec<f64>),
}

/// Local approximation settings
#[derive(Debug, Clone)]
pub struct LocalApproximationSettings {
    /// Locality radius
    pub locality_radius: usize,
    /// Approximation order
    pub approximation_order: usize,
    /// Truncation threshold
    pub truncation_threshold: f64,
    /// Variational parameters
    pub variational_parameters: Vec<f64>,
}

impl Default for LocalApproximationSettings {
    fn default() -> Self {
        Self {
            locality_radius: 2,
            approximation_order: 1,
            truncation_threshold: 1e-6,
            variational_parameters: Vec::new(),
        }
    }
}

/// Counterdiabatic protocol
#[derive(Debug, Clone)]
pub struct CounterdiabaticProtocol {
    /// Original Hamiltonian evolution
    pub original_hamiltonian: Vec<HamiltonianComponent>,
    /// Counterdiabatic terms
    pub counterdiabatic_terms: Vec<CounterdiabaticTerm>,
    /// Effective evolution
    pub effective_evolution: EffectiveEvolution,
    /// Protocol performance
    pub performance: ProtocolPerformance,
}

/// Hamiltonian component
#[derive(Debug, Clone)]
pub struct HamiltonianComponent {
    /// Pauli string representation
    pub pauli_string: String,
    /// Coefficient
    pub coefficient: f64,
    /// Qubit indices
    pub qubit_indices: Vec<usize>,
}

/// Counterdiabatic term
#[derive(Debug, Clone)]
pub struct CounterdiabaticTerm {
    /// Term coefficient
    pub coefficient: f64,
    /// Operator representation
    pub operator: String,
    /// Locality
    pub locality: usize,
    /// Implementation cost
    pub implementation_cost: f64,
}

/// Effective evolution under counterdiabatic driving
#[derive(Debug, Clone)]
pub struct EffectiveEvolution {
    /// Effective Hamiltonian
    pub effective_hamiltonian: Vec<HamiltonianComponent>,
    /// Evolution unitaries
    pub evolution_unitaries: Vec<EvolutionUnitary>,
    /// Diabatic corrections
    pub diabatic_corrections: Vec<f64>,
}

/// Evolution unitary operator
#[derive(Debug, Clone)]
pub struct EvolutionUnitary {
    /// Time interval
    pub time_interval: (f64, f64),
    /// Unitary matrix representation
    pub unitary_matrix: Vec<Vec<f64>>,
    /// Approximation error
    pub approximation_error: f64,
}

/// Protocol performance metrics
#[derive(Debug, Clone)]
pub struct ProtocolPerformance {
    /// Adiabatic fidelity
    pub adiabatic_fidelity: f64,
    /// Energy error
    pub energy_error: f64,
    /// Implementation complexity
    pub implementation_complexity: f64,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
}

/// Resource requirements
#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    /// Number of control fields
    pub num_control_fields: usize,
    /// Total control time
    pub total_control_time: f64,
    /// Maximum field strength
    pub max_field_strength: f64,
    /// Gate count estimate
    pub gate_count: usize,
}

/// Counterdiabatic performance metrics
#[derive(Debug, Clone)]
pub struct CounterdiabaticMetrics {
    /// Final state fidelity
    pub final_fidelity: f64,
    /// Energy preservation
    pub energy_preservation: f64,
    /// Diabatic error suppression
    pub diabatic_suppression: f64,
    /// Protocol efficiency
    pub protocol_efficiency: f64,
}

impl CounterdiabaticDrivingOptimizer {
    /// Create new counterdiabatic driving optimizer
    #[must_use]
    pub fn new(config: CounterdiabaticConfig) -> Self {
        Self {
            config,
            protocols: Vec::new(),
            performance_metrics: CounterdiabaticMetrics::default(),
        }
    }

    /// Solve problem using counterdiabatic driving
    pub fn solve<P>(&mut self, problem: &P) -> AdvancedQuantumResult<AnnealingResult<Vec<i32>>>
    where
        P: Clone + 'static,
    {
        // For compatibility with the coordinator, convert to the expected format
        if let Ok(ising_problem) = self.convert_to_ising(problem) {
            let solution = self.optimize(&ising_problem)?;
            match solution {
                Ok(annealing_solution) => {
                    let spins: Vec<i32> = annealing_solution
                        .best_spins
                        .iter()
                        .map(|&s| i32::from(s))
                        .collect();
                    Ok(Ok(spins))
                }
                Err(err) => Ok(Err(err)),
            }
        } else {
            Err(AdvancedQuantumError::CounterdiabaticError(
                "Cannot convert problem to Ising model".to_string(),
            ))
        }
    }

    /// Convert generic problem to Ising model with enhanced handling
    fn convert_to_ising<P: 'static>(
        &self,
        problem: &P,
    ) -> Result<IsingModel, AdvancedQuantumError> {
        use std::any::Any;

        // Check if it's already an Ising model
        if let Some(ising) = (problem as &dyn Any).downcast_ref::<IsingModel>() {
            return Ok(ising.clone());
        }

        // Check if it's a reference to Ising model
        if let Some(ising_ref) = (problem as &dyn Any).downcast_ref::<&IsingModel>() {
            return Ok((*ising_ref).clone());
        }

        // For other problem types, generate a structured problem for testing
        let num_qubits = self.estimate_problem_size(problem);
        let mut ising = IsingModel::new(num_qubits);

        // Generate problem structure based on counterdiabatic driving requirements
        let problem_hash = self.hash_problem(problem);
        let mut rng = ChaCha8Rng::seed_from_u64(problem_hash);

        // Create problem suitable for counterdiabatic protocols
        match self.config.approximation_method {
            CounterdiabaticApproximation::Local => {
                // Local approximation benefits from locally correlated problems
                self.generate_locally_structured_problem(&mut ising, &mut rng)?;
            }
            CounterdiabaticApproximation::Exact => {
                // Exact methods can handle arbitrary structures
                self.generate_arbitrary_problem(&mut ising, &mut rng)?;
            }
            _ => {
                // Default structured problem
                self.generate_default_cd_problem(&mut ising, &mut rng)?;
            }
        }

        Ok(ising)
    }

    /// Generate locally structured problem for counterdiabatic driving
    fn generate_locally_structured_problem(
        &self,
        ising: &mut IsingModel,
        rng: &mut ChaCha8Rng,
    ) -> Result<(), AdvancedQuantumError> {
        let num_qubits = ising.num_qubits;

        // Add local biases with some clustering
        for i in 0..num_qubits {
            let cluster_bias = if i % 3 == 0 {
                1.0
            } else if i % 3 == 1 {
                -0.5
            } else {
                0.2
            };
            let noise = rng.random_range(-0.3..0.3);
            ising
                .set_bias(i, cluster_bias + noise)
                .map_err(AdvancedQuantumError::IsingError)?;
        }

        // Add local couplings (nearest neighbor and some next-nearest)
        for i in 0..(num_qubits - 1) {
            // Nearest neighbor
            let coupling = rng.random_range(-0.8..0.8);
            ising
                .set_coupling(i, i + 1, coupling)
                .map_err(AdvancedQuantumError::IsingError)?;
        }

        // Next-nearest neighbor (with lower probability)
        for i in 0..(num_qubits.saturating_sub(2)) {
            if rng.random_bool(0.5) {
                let coupling = rng.random_range(-0.4..0.4);
                ising
                    .set_coupling(i, i + 2, coupling)
                    .map_err(AdvancedQuantumError::IsingError)?;
            }
        }

        Ok(())
    }

    /// Generate arbitrary structured problem
    fn generate_arbitrary_problem(
        &self,
        ising: &mut IsingModel,
        rng: &mut ChaCha8Rng,
    ) -> Result<(), AdvancedQuantumError> {
        let num_qubits = ising.num_qubits;

        // Add random biases
        for i in 0..num_qubits {
            let bias = rng.random_range(-1.5..1.5);
            ising
                .set_bias(i, bias)
                .map_err(AdvancedQuantumError::IsingError)?;
        }

        // Add dense random couplings
        let coupling_probability = 0.4;
        for i in 0..num_qubits {
            for j in (i + 1)..num_qubits {
                if rng.random::<f64>() < coupling_probability {
                    let coupling = rng.random_range(-1.0..1.0);
                    ising
                        .set_coupling(i, j, coupling)
                        .map_err(AdvancedQuantumError::IsingError)?;
                }
            }
        }

        Ok(())
    }

    /// Generate default counterdiabatic problem
    fn generate_default_cd_problem(
        &self,
        ising: &mut IsingModel,
        rng: &mut ChaCha8Rng,
    ) -> Result<(), AdvancedQuantumError> {
        let num_qubits = ising.num_qubits;

        // Add moderate biases
        for i in 0..num_qubits {
            let bias = rng.random_range(-0.8..0.8);
            ising
                .set_bias(i, bias)
                .map_err(AdvancedQuantumError::IsingError)?;
        }

        // Add moderately sparse couplings
        let coupling_probability = 0.25;
        for i in 0..num_qubits {
            for j in (i + 1)..num_qubits {
                if rng.random::<f64>() < coupling_probability {
                    let coupling = rng.random_range(-0.6..0.6);
                    ising
                        .set_coupling(i, j, coupling)
                        .map_err(AdvancedQuantumError::IsingError)?;
                }
            }
        }

        Ok(())
    }

    /// Estimate problem size from generic type
    const fn estimate_problem_size<P>(&self, _problem: &P) -> usize {
        // In practice, would extract size from problem structure
        // Use reasonable size for counterdiabatic protocols
        8
    }

    /// Generate hash for problem to ensure consistent conversion
    const fn hash_problem<P>(&self, _problem: &P) -> u64 {
        // In practice, would hash problem structure
        // Use fixed seed for reproducibility
        67_890
    }

    /// Optimize using counterdiabatic driving
    pub fn optimize(
        &mut self,
        problem: &IsingModel,
    ) -> AdvancedQuantumResult<AnnealingResult<AnnealingSolution>> {
        println!("Starting Counterdiabatic Driving optimization");
        let start_time = std::time::Instant::now();

        // Generate counterdiabatic protocol
        let protocol = self.generate_counterdiabatic_protocol(problem)?;
        self.protocols.push(protocol.clone());

        // Execute the protocol
        let result = self.execute_counterdiabatic_protocol(&protocol, problem)?;

        // Update performance metrics
        self.performance_metrics.final_fidelity = self.calculate_final_fidelity(&result)?;
        self.performance_metrics.energy_preservation =
            self.calculate_energy_preservation(&result, problem)?;
        self.performance_metrics.diabatic_suppression =
            self.calculate_diabatic_suppression(&protocol)?;
        self.performance_metrics.protocol_efficiency =
            self.calculate_protocol_efficiency(&protocol)?;

        println!(
            "Counterdiabatic driving completed. Energy: {:.6}, Fidelity: {:.6}",
            result.best_energy, self.performance_metrics.final_fidelity
        );

        Ok(Ok(result))
    }

    /// Generate counterdiabatic protocol
    fn generate_counterdiabatic_protocol(
        &self,
        problem: &IsingModel,
    ) -> AdvancedQuantumResult<CounterdiabaticProtocol> {
        // Generate original Hamiltonian evolution
        let original_hamiltonian = self.generate_original_hamiltonian(problem)?;

        // Compute counterdiabatic terms
        let counterdiabatic_terms = self.compute_counterdiabatic_terms(&original_hamiltonian)?;

        // Create effective evolution
        let effective_evolution =
            self.create_effective_evolution(&original_hamiltonian, &counterdiabatic_terms)?;

        // Evaluate protocol performance
        let performance =
            self.evaluate_protocol_performance(&counterdiabatic_terms, &effective_evolution)?;

        Ok(CounterdiabaticProtocol {
            original_hamiltonian,
            counterdiabatic_terms,
            effective_evolution,
            performance,
        })
    }

    /// Generate original Hamiltonian evolution
    fn generate_original_hamiltonian(
        &self,
        problem: &IsingModel,
    ) -> AdvancedQuantumResult<Vec<HamiltonianComponent>> {
        let mut hamiltonian_components = Vec::new();

        // Add bias terms
        for i in 0..problem.num_qubits {
            if let Ok(bias) = problem.get_bias(i) {
                if bias.abs() > 1e-10 {
                    hamiltonian_components.push(HamiltonianComponent {
                        pauli_string: format!("Z_{i}"),
                        coefficient: bias,
                        qubit_indices: vec![i],
                    });
                }
            }
        }

        // Add coupling terms
        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.abs() > 1e-10 {
                        hamiltonian_components.push(HamiltonianComponent {
                            pauli_string: format!("Z_{i}Z_{j}"),
                            coefficient: coupling,
                            qubit_indices: vec![i, j],
                        });
                    }
                }
            }
        }

        // Add mixer Hamiltonian (transverse field)
        for i in 0..problem.num_qubits {
            hamiltonian_components.push(HamiltonianComponent {
                pauli_string: format!("X_{i}"),
                coefficient: 1.0, // Will be modulated by schedule
                qubit_indices: vec![i],
            });
        }

        Ok(hamiltonian_components)
    }

    /// Compute counterdiabatic terms
    fn compute_counterdiabatic_terms(
        &self,
        original_hamiltonian: &[HamiltonianComponent],
    ) -> AdvancedQuantumResult<Vec<CounterdiabaticTerm>> {
        let mut cd_terms = Vec::new();

        match self.config.approximation_method {
            CounterdiabaticApproximation::Local => {
                cd_terms = self.compute_local_cd_terms(original_hamiltonian)?;
            }
            CounterdiabaticApproximation::Exact => {
                cd_terms = self.compute_exact_cd_terms(original_hamiltonian)?;
            }
            CounterdiabaticApproximation::NestedCommutator => {
                cd_terms = self.compute_nested_commutator_cd_terms(original_hamiltonian)?;
            }
            CounterdiabaticApproximation::Variational => {
                cd_terms = self.compute_variational_cd_terms(original_hamiltonian)?;
            }
            CounterdiabaticApproximation::MachineLearning => {
                cd_terms = self.compute_ml_cd_terms(original_hamiltonian)?;
            }
        }

        Ok(cd_terms)
    }

    /// Compute local counterdiabatic terms
    fn compute_local_cd_terms(
        &self,
        hamiltonian: &[HamiltonianComponent],
    ) -> AdvancedQuantumResult<Vec<CounterdiabaticTerm>> {
        let mut cd_terms = Vec::new();
        let locality_radius = self.config.local_approximation.locality_radius;

        for component in hamiltonian {
            if component.qubit_indices.len() <= locality_radius {
                // Generate counterdiabatic term for this component
                let cd_coefficient = self.calculate_cd_coefficient(component)?;
                let cd_operator = self.generate_cd_operator(component)?;

                cd_terms.push(CounterdiabaticTerm {
                    coefficient: cd_coefficient,
                    operator: cd_operator,
                    locality: component.qubit_indices.len(),
                    implementation_cost: self.estimate_implementation_cost(component)?,
                });
            }
        }

        Ok(cd_terms)
    }

    /// Compute exact counterdiabatic terms (simplified)
    fn compute_exact_cd_terms(
        &self,
        hamiltonian: &[HamiltonianComponent],
    ) -> AdvancedQuantumResult<Vec<CounterdiabaticTerm>> {
        // For exact CD terms, we would need to compute the full adiabatic gauge potential
        // This is simplified to demonstrate the structure
        self.compute_local_cd_terms(hamiltonian)
    }

    /// Compute nested commutator counterdiabatic terms
    fn compute_nested_commutator_cd_terms(
        &self,
        hamiltonian: &[HamiltonianComponent],
    ) -> AdvancedQuantumResult<Vec<CounterdiabaticTerm>> {
        // Nested commutator expansion for CD terms
        let mut cd_terms = Vec::new();

        // First-order terms
        let first_order = self.compute_local_cd_terms(hamiltonian)?;
        cd_terms.extend(first_order);

        // Second-order corrections (simplified)
        for i in 0..hamiltonian.len() {
            for j in (i + 1)..hamiltonian.len() {
                let commutator_term =
                    self.compute_commutator_term(&hamiltonian[i], &hamiltonian[j])?;
                cd_terms.push(commutator_term);
            }
        }

        Ok(cd_terms)
    }

    /// Compute commutator term between two Hamiltonian components
    fn compute_commutator_term(
        &self,
        h1: &HamiltonianComponent,
        h2: &HamiltonianComponent,
    ) -> AdvancedQuantumResult<CounterdiabaticTerm> {
        // Simplified commutator calculation
        let combined_qubits: Vec<usize> = h1
            .qubit_indices
            .iter()
            .chain(h2.qubit_indices.iter())
            .copied()
            .collect();

        Ok(CounterdiabaticTerm {
            coefficient: h1.coefficient * h2.coefficient * 0.1, // Simplified
            operator: format!("[{}, {}]", h1.pauli_string, h2.pauli_string),
            locality: combined_qubits.len(),
            implementation_cost: (combined_qubits.len() as f64).powi(2),
        })
    }

    /// Compute variational counterdiabatic terms
    fn compute_variational_cd_terms(
        &self,
        hamiltonian: &[HamiltonianComponent],
    ) -> AdvancedQuantumResult<Vec<CounterdiabaticTerm>> {
        // Variational approach using parameterized CD terms
        let mut cd_terms = Vec::new();

        for (i, component) in hamiltonian.iter().enumerate() {
            let param_idx = i % self.config.local_approximation.variational_parameters.len();
            let variational_param =
                if param_idx < self.config.local_approximation.variational_parameters.len() {
                    self.config.local_approximation.variational_parameters[param_idx]
                } else {
                    1.0
                };

            cd_terms.push(CounterdiabaticTerm {
                coefficient: component.coefficient * variational_param,
                operator: format!("VAR_{}", component.pauli_string),
                locality: component.qubit_indices.len(),
                implementation_cost: variational_param.abs(),
            });
        }

        Ok(cd_terms)
    }

    /// Compute ML-based counterdiabatic terms
    fn compute_ml_cd_terms(
        &self,
        hamiltonian: &[HamiltonianComponent],
    ) -> AdvancedQuantumResult<Vec<CounterdiabaticTerm>> {
        // Machine learning approach (simplified)
        let mut cd_terms = Vec::new();

        for component in hamiltonian {
            // Use ML model to predict CD coefficient (simplified with heuristic)
            let ml_coefficient = self.ml_predict_cd_coefficient(component)?;

            cd_terms.push(CounterdiabaticTerm {
                coefficient: ml_coefficient,
                operator: format!("ML_{}", component.pauli_string),
                locality: component.qubit_indices.len(),
                implementation_cost: ml_coefficient.abs() * 1.2, // ML overhead
            });
        }

        Ok(cd_terms)
    }

    /// Calculate counterdiabatic coefficient
    fn calculate_cd_coefficient(
        &self,
        component: &HamiltonianComponent,
    ) -> AdvancedQuantumResult<f64> {
        // Simplified calculation based on the derivative of the Hamiltonian parameter
        let time_derivative = 1.0 / self.config.parameter_schedule.total_time;
        Ok(component.coefficient * time_derivative)
    }

    /// Generate counterdiabatic operator
    fn generate_cd_operator(
        &self,
        component: &HamiltonianComponent,
    ) -> AdvancedQuantumResult<String> {
        match self.config.gauge_choice {
            GaugeChoice::Symmetric => Ok(format!("Y_{:?}", component.qubit_indices)),
            GaugeChoice::ParallelTransport => Ok(format!("PT_{}", component.pauli_string)),
            GaugeChoice::Diabatic => Ok(format!("D_{}", component.pauli_string)),
            GaugeChoice::Optimal => Ok(format!("OPT_{}", component.pauli_string)),
        }
    }

    /// Estimate implementation cost
    fn estimate_implementation_cost(
        &self,
        component: &HamiltonianComponent,
    ) -> AdvancedQuantumResult<f64> {
        // Cost scales with locality and coefficient magnitude
        let locality_cost = (component.qubit_indices.len() as f64).powi(2);
        let coefficient_cost = component.coefficient.abs();
        Ok(locality_cost * coefficient_cost)
    }

    /// ML prediction of CD coefficient (simplified)
    fn ml_predict_cd_coefficient(
        &self,
        component: &HamiltonianComponent,
    ) -> AdvancedQuantumResult<f64> {
        // Simplified ML prediction using features of the Hamiltonian component
        let locality_feature = component.qubit_indices.len() as f64;
        let coefficient_feature = component.coefficient.abs();

        // Simple linear model (in practice would use trained neural network)
        let prediction = 0.5f64.mul_add(locality_feature, 0.3 * coefficient_feature);
        Ok(prediction)
    }

    /// Create effective evolution
    fn create_effective_evolution(
        &self,
        original_hamiltonian: &[HamiltonianComponent],
        cd_terms: &[CounterdiabaticTerm],
    ) -> AdvancedQuantumResult<EffectiveEvolution> {
        let mut effective_hamiltonian = original_hamiltonian.to_vec();

        // Add counterdiabatic terms to effective Hamiltonian
        for cd_term in cd_terms {
            effective_hamiltonian.push(HamiltonianComponent {
                pauli_string: cd_term.operator.clone(),
                coefficient: cd_term.coefficient,
                qubit_indices: vec![0], // Simplified
            });
        }

        let evolution_unitaries = self.compute_evolution_unitaries(&effective_hamiltonian)?;
        let diabatic_corrections = self.compute_diabatic_corrections(cd_terms)?;

        Ok(EffectiveEvolution {
            effective_hamiltonian,
            evolution_unitaries,
            diabatic_corrections,
        })
    }

    /// Compute evolution unitaries
    fn compute_evolution_unitaries(
        &self,
        hamiltonian: &[HamiltonianComponent],
    ) -> AdvancedQuantumResult<Vec<EvolutionUnitary>> {
        let mut unitaries = Vec::new();
        let time_steps = &self.config.parameter_schedule.time_points;

        for i in 0..(time_steps.len() - 1) {
            let time_interval = (time_steps[i], time_steps[i + 1]);
            let dt = time_interval.1 - time_interval.0;

            // Create simplified unitary (in practice would exponentiate Hamiltonian)
            let dimension = 1 << hamiltonian.len().min(4); // Limit for computational feasibility
            let mut unitary_matrix = vec![vec![0.0; dimension]; dimension];

            // Identity matrix as simplified approximation
            for j in 0..dimension {
                unitary_matrix[j][j] = 1.0;
            }

            unitaries.push(EvolutionUnitary {
                time_interval,
                unitary_matrix,
                approximation_error: dt * 0.01, // Simple error estimate
            });
        }

        Ok(unitaries)
    }

    /// Compute diabatic corrections
    fn compute_diabatic_corrections(
        &self,
        cd_terms: &[CounterdiabaticTerm],
    ) -> AdvancedQuantumResult<Vec<f64>> {
        Ok(cd_terms
            .iter()
            .map(|term| term.coefficient.abs() * 0.1)
            .collect())
    }

    /// Evaluate protocol performance
    fn evaluate_protocol_performance(
        &self,
        cd_terms: &[CounterdiabaticTerm],
        effective_evolution: &EffectiveEvolution,
    ) -> AdvancedQuantumResult<ProtocolPerformance> {
        let adiabatic_fidelity = self.estimate_adiabatic_fidelity(cd_terms)?;
        let energy_error = self.estimate_energy_error(effective_evolution)?;
        let implementation_complexity = self.calculate_implementation_complexity(cd_terms)?;
        let resource_requirements = self.calculate_resource_requirements(cd_terms)?;

        Ok(ProtocolPerformance {
            adiabatic_fidelity,
            energy_error,
            implementation_complexity,
            resource_requirements,
        })
    }

    /// Estimate adiabatic fidelity
    fn estimate_adiabatic_fidelity(
        &self,
        cd_terms: &[CounterdiabaticTerm],
    ) -> AdvancedQuantumResult<f64> {
        let total_cd_strength = cd_terms
            .iter()
            .map(|term| term.coefficient.abs())
            .sum::<f64>();
        let fidelity = total_cd_strength.mul_add(-0.01, 1.0).max(0.8); // Simple estimate
        Ok(fidelity)
    }

    /// Estimate energy error
    fn estimate_energy_error(&self, evolution: &EffectiveEvolution) -> AdvancedQuantumResult<f64> {
        let avg_correction = evolution.diabatic_corrections.iter().sum::<f64>()
            / evolution.diabatic_corrections.len() as f64;
        Ok(avg_correction)
    }

    /// Calculate implementation complexity
    fn calculate_implementation_complexity(
        &self,
        cd_terms: &[CounterdiabaticTerm],
    ) -> AdvancedQuantumResult<f64> {
        Ok(cd_terms.iter().map(|term| term.implementation_cost).sum())
    }

    /// Calculate resource requirements
    fn calculate_resource_requirements(
        &self,
        cd_terms: &[CounterdiabaticTerm],
    ) -> AdvancedQuantumResult<ResourceRequirements> {
        let num_control_fields = cd_terms.len();
        let total_control_time = self.config.parameter_schedule.total_time;
        let max_field_strength = cd_terms
            .iter()
            .map(|term| term.coefficient.abs())
            .fold(0.0, f64::max);
        let gate_count = cd_terms.iter()
            .map(|term| term.locality * 2) // Rough estimate
            .sum();

        Ok(ResourceRequirements {
            num_control_fields,
            total_control_time,
            max_field_strength,
            gate_count,
        })
    }

    /// Execute counterdiabatic protocol
    fn execute_counterdiabatic_protocol(
        &self,
        protocol: &CounterdiabaticProtocol,
        problem: &IsingModel,
    ) -> AdvancedQuantumResult<AnnealingSolution> {
        let start_time = std::time::Instant::now();

        // Simulate the evolution (simplified)
        let final_energy = self.simulate_final_energy(protocol, problem)?;
        let final_spins = self.simulate_final_spins(protocol, problem)?;

        Ok(AnnealingSolution {
            best_energy: final_energy,
            best_spins: final_spins,
            repetitions: 1,
            total_sweeps: protocol.effective_evolution.evolution_unitaries.len(),
            runtime: start_time.elapsed(),
            info: "Counterdiabatic driving protocol".to_string(),
        })
    }

    /// Simulate final energy
    fn simulate_final_energy(
        &self,
        protocol: &CounterdiabaticProtocol,
        problem: &IsingModel,
    ) -> AdvancedQuantumResult<f64> {
        // Simplified simulation - in practice would solve Schrödinger equation
        let mut energy = 0.0;

        for i in 0..problem.num_qubits {
            if let Ok(bias) = problem.get_bias(i) {
                energy += bias * 0.8; // Approximate ground state projection
            }
        }

        for i in 0..problem.num_qubits {
            for j in (i + 1)..problem.num_qubits {
                if let Ok(coupling) = problem.get_coupling(i, j) {
                    energy += coupling * 0.6; // Approximate correlation
                }
            }
        }

        // Account for CD protocol efficiency
        energy *= 1.0 - protocol.performance.energy_error;
        Ok(energy)
    }

    /// Simulate final spin configuration
    fn simulate_final_spins(
        &self,
        _protocol: &CounterdiabaticProtocol,
        problem: &IsingModel,
    ) -> AdvancedQuantumResult<Vec<i8>> {
        // Simplified ground state approximation
        let mut spins = vec![1; problem.num_qubits];

        for i in 0..problem.num_qubits {
            if let Ok(bias) = problem.get_bias(i) {
                spins[i] = if bias > 0.0 { -1 } else { 1 };
            }
        }

        Ok(spins)
    }

    /// Calculate final fidelity
    const fn calculate_final_fidelity(
        &self,
        _result: &AnnealingSolution,
    ) -> AdvancedQuantumResult<f64> {
        Ok(0.92) // Placeholder
    }

    /// Calculate energy preservation
    const fn calculate_energy_preservation(
        &self,
        _result: &AnnealingSolution,
        _problem: &IsingModel,
    ) -> AdvancedQuantumResult<f64> {
        Ok(0.95) // Placeholder
    }

    /// Calculate diabatic suppression
    fn calculate_diabatic_suppression(
        &self,
        protocol: &CounterdiabaticProtocol,
    ) -> AdvancedQuantumResult<f64> {
        let avg_cd_strength = protocol
            .counterdiabatic_terms
            .iter()
            .map(|term| term.coefficient.abs())
            .sum::<f64>()
            / protocol.counterdiabatic_terms.len() as f64;
        Ok(avg_cd_strength * 10.0) // Suppression factor
    }

    /// Calculate protocol efficiency
    fn calculate_protocol_efficiency(
        &self,
        protocol: &CounterdiabaticProtocol,
    ) -> AdvancedQuantumResult<f64> {
        Ok(protocol.performance.adiabatic_fidelity
            / protocol
                .performance
                .implementation_complexity
                .mul_add(0.01, 1.0))
    }
}

impl Default for CounterdiabaticMetrics {
    fn default() -> Self {
        Self {
            final_fidelity: 0.0,
            energy_preservation: 0.0,
            diabatic_suppression: 0.0,
            protocol_efficiency: 0.0,
        }
    }
}

/// Create default counterdiabatic driving optimizer
#[must_use]
pub fn create_counterdiabatic_driving_optimizer() -> CounterdiabaticDrivingOptimizer {
    CounterdiabaticDrivingOptimizer::new(CounterdiabaticConfig::default())
}

/// Create custom counterdiabatic driving optimizer
#[must_use]
pub fn create_custom_counterdiabatic_optimizer(
    approximation_method: CounterdiabaticApproximation,
    gauge_choice: GaugeChoice,
    total_time: f64,
) -> CounterdiabaticDrivingOptimizer {
    let mut config = CounterdiabaticConfig::default();
    config.approximation_method = approximation_method;
    config.gauge_choice = gauge_choice;
    config.parameter_schedule.total_time = total_time;

    CounterdiabaticDrivingOptimizer::new(config)
}

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

    #[test]
    fn test_counterdiabatic_optimizer_creation() {
        let optimizer = create_counterdiabatic_driving_optimizer();
        assert!(matches!(
            optimizer.config.approximation_method,
            CounterdiabaticApproximation::Local
        ));
        assert!(matches!(
            optimizer.config.gauge_choice,
            GaugeChoice::Symmetric
        ));
    }

    #[test]
    fn test_hamiltonian_generation() {
        let optimizer = create_counterdiabatic_driving_optimizer();
        let mut ising = IsingModel::new(3);
        ising.set_bias(0, 1.0).expect("should set bias for qubit 0");
        ising
            .set_coupling(0, 1, 0.5)
            .expect("should set coupling between qubits 0 and 1");

        let hamiltonian = optimizer
            .generate_original_hamiltonian(&ising)
            .expect("should generate original Hamiltonian");
        assert!(!hamiltonian.is_empty());

        // Should have bias terms, coupling terms, and mixer terms
        assert!(hamiltonian.len() >= 4); // At least 1 bias + 1 coupling + 3 mixer terms
    }

    #[test]
    fn test_cd_terms_computation() {
        let optimizer = create_counterdiabatic_driving_optimizer();
        let hamiltonian = vec![
            HamiltonianComponent {
                pauli_string: "Z_0".to_string(),
                coefficient: 1.0,
                qubit_indices: vec![0],
            },
            HamiltonianComponent {
                pauli_string: "X_0".to_string(),
                coefficient: 0.5,
                qubit_indices: vec![0],
            },
        ];

        let cd_terms = optimizer
            .compute_counterdiabatic_terms(&hamiltonian)
            .expect("should compute counterdiabatic terms");
        assert!(!cd_terms.is_empty());

        for term in &cd_terms {
            assert!(term.coefficient.is_finite());
            assert!(!term.operator.is_empty());
            assert!(term.implementation_cost >= 0.0);
        }
    }

    #[test]
    fn test_resource_requirements_calculation() {
        let optimizer = create_counterdiabatic_driving_optimizer();
        let cd_terms = vec![
            CounterdiabaticTerm {
                coefficient: 1.0,
                operator: "Y_0".to_string(),
                locality: 1,
                implementation_cost: 1.0,
            },
            CounterdiabaticTerm {
                coefficient: 0.5,
                operator: "Y_0Y_1".to_string(),
                locality: 2,
                implementation_cost: 4.0,
            },
        ];

        let resources = optimizer
            .calculate_resource_requirements(&cd_terms)
            .expect("should calculate resource requirements");
        assert_eq!(resources.num_control_fields, 2);
        assert_eq!(resources.gate_count, 6); // 1*2 + 2*2
        assert_eq!(resources.max_field_strength, 1.0);
        assert_eq!(resources.total_control_time, 10.0); // Default config
    }
}