quantrs2_device/quantum_ml/

variational_algorithms.rs

//! Variational Quantum Algorithms for Machine Learning
//!
//! This module implements various variational quantum algorithms including VQE, QAOA,
//! and VQC with hardware-optimized circuits and gradient computation.

use super::*;
use crate::{DeviceError, DeviceResult, QuantumDevice};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;

/// Variational Quantum Eigensolver (VQE) implementation
pub struct VQE {
    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
    hamiltonian: Hamiltonian,
    ansatz: Box<dyn VariationalAnsatz + Send + Sync>,
    optimizer: Box<dyn VariationalOptimizer + Send + Sync>,
    config: VQEConfig,
}

/// VQE configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VQEConfig {
    pub max_iterations: usize,
    pub energy_tolerance: f64,
    pub gradient_tolerance: f64,
    pub shots_per_measurement: usize,
    pub use_error_mitigation: bool,
    pub measurement_grouping: bool,
    pub adaptive_shots: bool,
}

impl Default for VQEConfig {
    fn default() -> Self {
        Self {
            max_iterations: 1000,
            energy_tolerance: 1e-6,
            gradient_tolerance: 1e-8,
            shots_per_measurement: 1024,
            use_error_mitigation: true,
            measurement_grouping: true,
            adaptive_shots: false,
        }
    }
}

/// Hamiltonian representation for VQE
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Hamiltonian {
    pub terms: Vec<PauliTerm>,
    pub num_qubits: usize,
}

/// Pauli term in Hamiltonian
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PauliTerm {
    pub coefficient: f64,
    pub paulis: Vec<(usize, PauliOperator)>, // (qubit_index, pauli_op)
}

/// Pauli operators
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum PauliOperator {
    I, // Identity
    X, // Pauli-X
    Y, // Pauli-Y
    Z, // Pauli-Z
}

impl VQE {
    /// Create a new VQE instance
    pub fn new(
        device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
        hamiltonian: Hamiltonian,
        ansatz: Box<dyn VariationalAnsatz + Send + Sync>,
        optimizer: Box<dyn VariationalOptimizer + Send + Sync>,
        config: VQEConfig,
    ) -> Self {
        Self {
            device,
            hamiltonian,
            ansatz,
            optimizer,
            config,
        }
    }

    /// Run VQE optimization
    pub async fn optimize(&mut self) -> DeviceResult<VQEResult> {
        let mut parameters = self.ansatz.initialize_parameters();
        let mut energy_history = Vec::new();
        let mut best_energy = f64::INFINITY;
        let mut best_parameters = parameters.clone();

        for iteration in 0..self.config.max_iterations {
            // Compute energy expectation value
            let energy = self.compute_energy_expectation(&parameters).await?;
            energy_history.push(energy);

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

            // Check convergence
            if iteration > 0 {
                let energy_change =
                    (energy_history[iteration] - energy_history[iteration - 1]).abs();
                if energy_change < self.config.energy_tolerance {
                    break;
                }
            }

            // Compute gradients
            let gradients = self.compute_gradients(&parameters).await?;
            let gradient_norm = gradients.iter().map(|g| g * g).sum::<f64>().sqrt();

            if gradient_norm < self.config.gradient_tolerance {
                break;
            }

            // Update parameters
            parameters = self.optimizer.update_parameters(parameters, gradients)?;
        }

        Ok(VQEResult {
            optimal_energy: best_energy,
            optimal_parameters: best_parameters,
            energy_history,
            converged: true,
        })
    }

    /// Compute energy expectation value
    async fn compute_energy_expectation(&self, parameters: &[f64]) -> DeviceResult<f64> {
        let mut total_energy = 0.0;

        // Group measurements to reduce circuit executions
        let measurement_groups = if self.config.measurement_grouping {
            self.group_pauli_measurements()
        } else {
            self.hamiltonian
                .terms
                .iter()
                .map(|term| vec![term.clone()])
                .collect()
        };

        for group in measurement_groups {
            let group_expectation = self.compute_group_expectation(&group, parameters).await?;
            total_energy += group_expectation;
        }

        Ok(total_energy)
    }

    /// Group Pauli measurements that can be measured simultaneously
    fn group_pauli_measurements(&self) -> Vec<Vec<PauliTerm>> {
        // Simplified grouping - in practice, this would implement sophisticated grouping
        // based on commutativity of Pauli operators
        let mut groups = Vec::new();
        let mut current_group = Vec::new();

        for term in &self.hamiltonian.terms {
            if current_group.len() < 10 {
                // Simple grouping by size
                current_group.push(term.clone());
            } else {
                groups.push(current_group);
                current_group = vec![term.clone()];
            }
        }

        if !current_group.is_empty() {
            groups.push(current_group);
        }

        groups
    }

    /// Compute expectation value for a group of Pauli terms
    async fn compute_group_expectation(
        &self,
        group: &[PauliTerm],
        parameters: &[f64],
    ) -> DeviceResult<f64> {
        // Prepare quantum circuit with ansatz
        let mut circuit = self.ansatz.build_circuit(parameters)?;

        // Add measurement basis rotations for Pauli terms
        for term in group {
            for (qubit, pauli_op) in &term.paulis {
                match pauli_op {
                    PauliOperator::X => {
                        // Add H gate before measurement
                        circuit.add_h_gate(*qubit)?;
                    }
                    PauliOperator::Y => {
                        // Add S† and H gates before measurement
                        circuit.add_s_dagger_gate(*qubit)?;
                        circuit.add_h_gate(*qubit)?;
                    }
                    PauliOperator::Z | PauliOperator::I => {
                        // No rotation needed for Z measurement
                    }
                }
            }
        }

        // Execute circuit and compute expectation
        let shots = if self.config.adaptive_shots {
            self.adaptive_shot_count(group)
        } else {
            self.config.shots_per_measurement
        };

        let device = self.device.read().await;
        let result = Self::execute_circuit_helper(&*device, &circuit, shots).await?;

        // Compute expectation value from measurement results
        let mut expectation = 0.0;
        for term in group {
            let term_expectation = self.compute_term_expectation(term, &result)?;
            expectation += term.coefficient * term_expectation;
        }

        Ok(expectation)
    }

    /// Compute expectation value for a single Pauli term
    fn compute_term_expectation(
        &self,
        term: &PauliTerm,
        circuit_result: &CircuitResult,
    ) -> DeviceResult<f64> {
        let mut expectation = 0.0;
        let total_shots = circuit_result.shots as f64;

        for (bitstring, count) in &circuit_result.counts {
            let probability = *count as f64 / total_shots;
            let parity = self.compute_pauli_parity(term, bitstring);
            expectation += probability * parity;
        }

        Ok(expectation)
    }

    /// Compute parity for Pauli term measurement
    fn compute_pauli_parity(&self, term: &PauliTerm, bitstring: &str) -> f64 {
        let mut parity = 1.0;

        for (qubit_idx, _pauli_op) in &term.paulis {
            if let Some(bit_char) = bitstring.chars().nth(*qubit_idx) {
                if bit_char == '1' {
                    parity *= -1.0;
                }
            }
        }

        parity
    }

    /// Compute gradients using parameter shift rule
    async fn compute_gradients(&self, parameters: &[f64]) -> DeviceResult<Vec<f64>> {
        let mut gradients = vec![0.0; parameters.len()];
        let shift = std::f64::consts::PI / 2.0;

        for (i, &param) in parameters.iter().enumerate() {
            let mut params_plus = parameters.to_vec();
            let mut params_minus = parameters.to_vec();

            params_plus[i] = param + shift;
            params_minus[i] = param - shift;

            let energy_plus = self.compute_energy_expectation(&params_plus).await?;
            let energy_minus = self.compute_energy_expectation(&params_minus).await?;

            gradients[i] = (energy_plus - energy_minus) / 2.0;
        }

        Ok(gradients)
    }

    fn adaptive_shot_count(&self, group: &[PauliTerm]) -> usize {
        // Adaptive shot allocation based on term coefficients
        let max_coeff = group
            .iter()
            .map(|term| term.coefficient.abs())
            .fold(0.0, f64::max);

        let base_shots = self.config.shots_per_measurement;
        (base_shots as f64 * (1.0 + max_coeff)).min(10000.0) as usize
    }

    /// Execute a circuit on the quantum device (helper function to work around trait object limitations)
    async fn execute_circuit_helper(
        _device: &(dyn QuantumDevice + Send + Sync),
        circuit: &ParameterizedQuantumCircuit,
        shots: usize,
    ) -> DeviceResult<CircuitResult> {
        // The `QuantumDevice` trait exposes no execution entry point for the
        // dynamic `ParameterizedQuantumCircuit` type, so we evaluate the circuit
        // with the in-crate exact state-vector simulator and sample real
        // measurement counts from its output distribution. This yields genuine,
        // circuit-dependent statistics for the VQE/QAOA optimisation loops
        // (previously this returned a fabricated uniform 50/50 split that
        // silently corrupted every energy estimate and gradient).
        crate::quantum_ml::circuit_simulation::simulate_and_sample(circuit, shots)
    }
}

/// VQE optimization result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VQEResult {
    pub optimal_energy: f64,
    pub optimal_parameters: Vec<f64>,
    pub energy_history: Vec<f64>,
    pub converged: bool,
}

/// Variational ansatz trait
pub trait VariationalAnsatz: Send + Sync {
    fn initialize_parameters(&self) -> Vec<f64>;
    fn build_circuit(&self, parameters: &[f64]) -> DeviceResult<ParameterizedQuantumCircuit>;
    fn parameter_count(&self) -> usize;
}

/// Hardware-efficient ansatz
pub struct HardwareEfficientAnsatz {
    pub num_qubits: usize,
    pub num_layers: usize,
    pub entangling_gates: EntanglingGateType,
}

#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum EntanglingGateType {
    CNOT,
    CZ,
    ISwap,
    Linear,
    Circular,
    AllToAll,
}

impl VariationalAnsatz for HardwareEfficientAnsatz {
    fn initialize_parameters(&self) -> Vec<f64> {
        let param_count = self.parameter_count();
        (0..param_count)
            .map(|_| fastrand::f64() * 2.0 * std::f64::consts::PI)
            .collect()
    }

    fn build_circuit(&self, parameters: &[f64]) -> DeviceResult<ParameterizedQuantumCircuit> {
        if parameters.len() != self.parameter_count() {
            return Err(DeviceError::InvalidInput(format!(
                "Expected {} parameters, got {}",
                self.parameter_count(),
                parameters.len()
            )));
        }

        let mut circuit = ParameterizedQuantumCircuit::new(self.num_qubits);
        let mut param_idx = 0;

        for layer in 0..self.num_layers {
            // Parameterized rotation gates
            for qubit in 0..self.num_qubits {
                circuit.add_ry_gate(qubit, parameters[param_idx])?;
                param_idx += 1;
                circuit.add_rz_gate(qubit, parameters[param_idx])?;
                param_idx += 1;
            }

            // Entangling gates
            match self.entangling_gates {
                EntanglingGateType::Linear => {
                    for qubit in 0..self.num_qubits - 1 {
                        circuit.add_cnot_gate(qubit, qubit + 1)?;
                    }
                }
                EntanglingGateType::Circular => {
                    for qubit in 0..self.num_qubits - 1 {
                        circuit.add_cnot_gate(qubit, qubit + 1)?;
                    }
                    if self.num_qubits > 2 {
                        circuit.add_cnot_gate(self.num_qubits - 1, 0)?;
                    }
                }
                EntanglingGateType::AllToAll => {
                    for i in 0..self.num_qubits {
                        for j in i + 1..self.num_qubits {
                            circuit.add_cnot_gate(i, j)?;
                        }
                    }
                }
                _ => {
                    // Default to linear connectivity
                    for qubit in 0..self.num_qubits - 1 {
                        circuit.add_cnot_gate(qubit, qubit + 1)?;
                    }
                }
            }
        }

        Ok(circuit)
    }

    fn parameter_count(&self) -> usize {
        2 * self.num_qubits * self.num_layers // RY + RZ for each qubit and layer
    }
}

/// Quantum Approximate Optimization Algorithm (QAOA)
pub struct QAOA {
    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
    problem: QAOAProblem,
    num_layers: usize,
    optimizer: Box<dyn VariationalOptimizer + Send + Sync>,
    config: QAOAConfig,
}

/// QAOA configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QAOAConfig {
    pub max_iterations: usize,
    pub shots_per_evaluation: usize,
    pub parameter_bounds: Option<(f64, f64)>,
    pub use_warm_start: bool,
    pub adaptive_layers: bool,
}

impl Default for QAOAConfig {
    fn default() -> Self {
        Self {
            max_iterations: 500,
            shots_per_evaluation: 2048,
            parameter_bounds: Some((0.0, 2.0 * std::f64::consts::PI)),
            use_warm_start: true,
            adaptive_layers: false,
        }
    }
}

/// QAOA problem representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QAOAProblem {
    pub cost_hamiltonian: Hamiltonian,
    pub mixer_hamiltonian: Hamiltonian,
    pub num_qubits: usize,
}

impl QAOA {
    pub fn new(
        device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
        problem: QAOAProblem,
        num_layers: usize,
        optimizer: Box<dyn VariationalOptimizer + Send + Sync>,
        config: QAOAConfig,
    ) -> Self {
        Self {
            device,
            problem,
            num_layers,
            optimizer,
            config,
        }
    }

    /// Optimize QAOA parameters
    pub async fn optimize(&mut self) -> DeviceResult<QAOAResult> {
        let mut parameters = self.initialize_parameters();
        let mut cost_history = Vec::new();
        let mut best_cost = f64::INFINITY;
        let mut best_parameters = parameters.clone();

        for iteration in 0..self.config.max_iterations {
            let cost = self.evaluate_cost(&parameters).await?;
            cost_history.push(cost);

            if cost < best_cost {
                best_cost = cost;
                best_parameters.clone_from(&parameters);
            }

            // Compute gradients and update parameters
            let gradients = self.compute_gradients(&parameters).await?;
            parameters = self.optimizer.update_parameters(parameters, gradients)?;

            // Apply parameter bounds if specified
            if let Some((min_bound, max_bound)) = self.config.parameter_bounds {
                for param in &mut parameters {
                    *param = param.clamp(min_bound, max_bound);
                }
            }
        }

        // Get final state and solution
        let final_state = self.prepare_qaoa_state(&best_parameters).await?;
        let solution = self.extract_solution(&final_state).await?;

        Ok(QAOAResult {
            optimal_cost: best_cost,
            optimal_parameters: best_parameters,
            cost_history,
            solution,
            converged: true,
        })
    }

    fn initialize_parameters(&self) -> Vec<f64> {
        if self.config.use_warm_start {
            // Initialize with heuristic values
            let mut params = Vec::with_capacity(2 * self.num_layers);
            for _ in 0..self.num_layers {
                params.push(0.1); // Small gamma for cost Hamiltonian
                params.push(0.1); // Small beta for mixer Hamiltonian
            }
            params
        } else {
            // Random initialization
            (0..2 * self.num_layers)
                .map(|_| fastrand::f64() * std::f64::consts::PI)
                .collect()
        }
    }

    async fn evaluate_cost(&self, parameters: &[f64]) -> DeviceResult<f64> {
        let circuit = self.build_qaoa_circuit(parameters)?;

        // Measure cost Hamiltonian expectation
        let device = self.device.read().await;
        let result =
            Self::execute_circuit_helper(&*device, &circuit, self.config.shots_per_evaluation)
                .await?;

        self.compute_hamiltonian_expectation(&self.problem.cost_hamiltonian, &result)
    }

    fn build_qaoa_circuit(&self, parameters: &[f64]) -> DeviceResult<ParameterizedQuantumCircuit> {
        let mut circuit = ParameterizedQuantumCircuit::new(self.problem.num_qubits);

        // Initialize in |+⟩ state
        for qubit in 0..self.problem.num_qubits {
            circuit.add_h_gate(qubit)?;
        }

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

            // Cost Hamiltonian evolution
            self.apply_hamiltonian_evolution(&mut circuit, &self.problem.cost_hamiltonian, gamma)?;

            // Mixer Hamiltonian evolution
            self.apply_hamiltonian_evolution(&mut circuit, &self.problem.mixer_hamiltonian, beta)?;
        }

        Ok(circuit)
    }

    fn apply_hamiltonian_evolution(
        &self,
        circuit: &mut ParameterizedQuantumCircuit,
        hamiltonian: &Hamiltonian,
        angle: f64,
    ) -> DeviceResult<()> {
        for term in &hamiltonian.terms {
            let evolution_angle = term.coefficient * angle;

            // Apply Pauli rotation for each term
            match term.paulis.len() {
                1 => {
                    let (qubit, pauli_op) = &term.paulis[0];
                    match pauli_op {
                        PauliOperator::X => circuit.add_rx_gate(*qubit, evolution_angle)?,
                        PauliOperator::Y => circuit.add_ry_gate(*qubit, evolution_angle)?,
                        PauliOperator::Z => circuit.add_rz_gate(*qubit, evolution_angle)?,
                        PauliOperator::I => {} // No operation for identity
                    }
                }
                2 => {
                    // Two-qubit Pauli rotation (simplified)
                    let (q1, op1) = &term.paulis[0];
                    let (q2, op2) = &term.paulis[1];

                    // This is a simplified implementation
                    // Full implementation would handle all Pauli combinations
                    if op1 == &PauliOperator::Z && op2 == &PauliOperator::Z {
                        circuit.add_cnot_gate(*q1, *q2)?;
                        circuit.add_rz_gate(*q2, evolution_angle)?;
                        circuit.add_cnot_gate(*q1, *q2)?;
                    }
                }
                _ => {
                    // Multi-qubit Pauli rotations (would need more sophisticated implementation)
                    return Err(DeviceError::InvalidInput(
                        "Multi-qubit Pauli rotations not yet fully implemented".to_string(),
                    ));
                }
            }
        }

        Ok(())
    }

    async fn compute_gradients(&self, parameters: &[f64]) -> DeviceResult<Vec<f64>> {
        let mut gradients = vec![0.0; parameters.len()];
        let shift = std::f64::consts::PI / 2.0;

        for (i, &param) in parameters.iter().enumerate() {
            let mut params_plus = parameters.to_vec();
            let mut params_minus = parameters.to_vec();

            params_plus[i] = param + shift;
            params_minus[i] = param - shift;

            let cost_plus = self.evaluate_cost(&params_plus).await?;
            let cost_minus = self.evaluate_cost(&params_minus).await?;

            gradients[i] = (cost_plus - cost_minus) / 2.0;
        }

        Ok(gradients)
    }

    async fn prepare_qaoa_state(&self, parameters: &[f64]) -> DeviceResult<QuantumState> {
        let circuit = self.build_qaoa_circuit(parameters)?;

        // Execute circuit to get quantum state
        let device = self.device.read().await;
        let result = Self::execute_circuit_helper(&*device, &circuit, 10000).await?; // High shots for accurate state

        // Convert measurement results to state representation
        Ok(QuantumState::from_measurements(
            &result.counts,
            self.problem.num_qubits,
        ))
    }

    async fn extract_solution(&self, state: &QuantumState) -> DeviceResult<QAOASolution> {
        // Find most probable bitstring
        let most_probable = state.get_most_probable_bitstring();
        let probability = state.get_probability(&most_probable);

        // Evaluate cost for this solution
        let cost = self.evaluate_classical_cost(&most_probable);

        Ok(QAOASolution {
            bitstring: most_probable,
            probability,
            cost,
            all_amplitudes: state.get_all_amplitudes(),
        })
    }

    fn evaluate_classical_cost(&self, bitstring: &str) -> f64 {
        let mut cost = 0.0;

        for term in &self.problem.cost_hamiltonian.terms {
            let mut term_value = term.coefficient;

            for (qubit_idx, pauli_op) in &term.paulis {
                if let Some(bit_char) = bitstring.chars().nth(*qubit_idx) {
                    let bit_value = if bit_char == '1' { 1.0 } else { -1.0 };

                    match pauli_op {
                        PauliOperator::Z => term_value *= bit_value,
                        PauliOperator::I | _ => {
                            // Identity doesn't change value; X and Y terms would need quantum evaluation
                        }
                    }
                }
            }

            cost += term_value;
        }

        cost
    }

    fn compute_hamiltonian_expectation(
        &self,
        hamiltonian: &Hamiltonian,
        result: &CircuitResult,
    ) -> DeviceResult<f64> {
        let mut expectation = 0.0;
        let total_shots = result.shots as f64;

        for (bitstring, count) in &result.counts {
            let probability = *count as f64 / total_shots;
            let energy = self.evaluate_classical_cost(bitstring);
            expectation += probability * energy;
        }

        Ok(expectation)
    }

    /// Execute a circuit on the quantum device (helper function to work around trait object limitations)
    async fn execute_circuit_helper(
        _device: &(dyn QuantumDevice + Send + Sync),
        circuit: &ParameterizedQuantumCircuit,
        shots: usize,
    ) -> DeviceResult<CircuitResult> {
        // The `QuantumDevice` trait exposes no execution entry point for the
        // dynamic `ParameterizedQuantumCircuit` type, so we evaluate the circuit
        // with the in-crate exact state-vector simulator and sample real
        // measurement counts from its output distribution. This yields genuine,
        // circuit-dependent statistics for the VQE/QAOA optimisation loops
        // (previously this returned a fabricated uniform 50/50 split that
        // silently corrupted every energy estimate and gradient).
        crate::quantum_ml::circuit_simulation::simulate_and_sample(circuit, shots)
    }
}

/// QAOA optimization result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QAOAResult {
    pub optimal_cost: f64,
    pub optimal_parameters: Vec<f64>,
    pub cost_history: Vec<f64>,
    pub solution: QAOASolution,
    pub converged: bool,
}

/// QAOA solution representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QAOASolution {
    pub bitstring: String,
    pub probability: f64,
    pub cost: f64,
    pub all_amplitudes: HashMap<String, f64>,
}

/// Quantum state representation
#[derive(Debug, Clone)]
pub struct QuantumState {
    amplitudes: HashMap<String, f64>,
    num_qubits: usize,
}

impl QuantumState {
    pub fn from_measurements(counts: &HashMap<String, usize>, num_qubits: usize) -> Self {
        let total_shots: usize = counts.values().sum();
        let mut amplitudes = HashMap::new();

        for (bitstring, count) in counts {
            let probability = *count as f64 / total_shots as f64;
            amplitudes.insert(bitstring.clone(), probability.sqrt());
        }

        Self {
            amplitudes,
            num_qubits,
        }
    }

    pub fn get_most_probable_bitstring(&self) -> String {
        self.amplitudes
            .iter()
            .max_by(|a, b| {
                (a.1 * a.1)
                    .partial_cmp(&(b.1 * b.1))
                    .unwrap_or(std::cmp::Ordering::Equal)
            })
            .map_or_else(
                || "0".repeat(self.num_qubits),
                |(bitstring, _)| bitstring.clone(),
            )
    }

    pub fn get_probability(&self, bitstring: &str) -> f64 {
        self.amplitudes.get(bitstring).map_or(0.0, |amp| amp * amp)
    }

    pub fn get_all_amplitudes(&self) -> HashMap<String, f64> {
        self.amplitudes.clone()
    }
}

/// Trait for variational optimizers
pub trait VariationalOptimizer: Send + Sync {
    fn update_parameters(
        &mut self,
        parameters: Vec<f64>,
        gradients: Vec<f64>,
    ) -> DeviceResult<Vec<f64>>;
    fn reset(&mut self);
}

/// Adam optimizer implementation
pub struct AdamOptimizer {
    learning_rate: f64,
    beta1: f64,
    beta2: f64,
    epsilon: f64,
    m: Vec<f64>, // First moment
    v: Vec<f64>, // Second moment
    t: usize,    // Time step
}

impl AdamOptimizer {
    pub const fn new(learning_rate: f64) -> Self {
        Self {
            learning_rate,
            beta1: 0.9,
            beta2: 0.999,
            epsilon: 1e-8,
            m: Vec::new(),
            v: Vec::new(),
            t: 0,
        }
    }
}

impl VariationalOptimizer for AdamOptimizer {
    fn update_parameters(
        &mut self,
        parameters: Vec<f64>,
        gradients: Vec<f64>,
    ) -> DeviceResult<Vec<f64>> {
        if self.m.is_empty() {
            self.m = vec![0.0; parameters.len()];
            self.v = vec![0.0; parameters.len()];
        }

        self.t += 1;
        let mut updated_params = parameters;

        for i in 0..updated_params.len() {
            // Update biased first moment estimate
            self.m[i] = self
                .beta1
                .mul_add(self.m[i], (1.0 - self.beta1) * gradients[i]);

            // Update biased second raw moment estimate
            self.v[i] = self
                .beta2
                .mul_add(self.v[i], (1.0 - self.beta2) * gradients[i] * gradients[i]);

            // Compute bias-corrected first moment estimate
            let m_hat = self.m[i] / (1.0 - self.beta1.powi(self.t as i32));

            // Compute bias-corrected second raw moment estimate
            let v_hat = self.v[i] / (1.0 - self.beta2.powi(self.t as i32));

            // Update parameters
            updated_params[i] -= self.learning_rate * m_hat / (v_hat.sqrt() + self.epsilon);
        }

        Ok(updated_params)
    }

    fn reset(&mut self) {
        self.m.clear();
        self.v.clear();
        self.t = 0;
    }
}

/// Placeholder for parameterized quantum circuit
#[derive(Debug, Clone)]
pub struct ParameterizedQuantumCircuit {
    num_qubits: usize,
    gates: Vec<QuantumGate>,
}

#[derive(Debug, Clone)]
pub enum QuantumGate {
    H(usize),
    X(usize),
    Y(usize),
    Z(usize),
    RX(usize, f64),
    RY(usize, f64),
    RZ(usize, f64),
    CNOT(usize, usize),
    CZ(usize, usize),
    SDagger(usize),
}

impl ParameterizedQuantumCircuit {
    pub const fn new(num_qubits: usize) -> Self {
        Self {
            num_qubits,
            gates: Vec::new(),
        }
    }

    pub const fn num_qubits(&self) -> usize {
        self.num_qubits
    }

    /// Read-only access to the ordered gate list.
    ///
    /// Used by the in-crate state-vector simulator
    /// ([`crate::quantum_ml::circuit_simulation`]) to execute the circuit and
    /// produce real measurement statistics.
    pub fn gates(&self) -> &[QuantumGate] {
        &self.gates
    }

    /// Substitute a flat parameter vector into this circuit's parameterized
    /// rotation gates, returning a new circuit.
    ///
    /// Parameterized gates (`RX`/`RY`/`RZ`) consume successive entries from
    /// `parameters` in gate order; non-parameterized gates are copied
    /// unchanged.  This is the real implementation of "evaluate circuit with
    /// parameters" used by the parameter-shift gradient estimators (previously
    /// a no-op clone that ignored its parameters entirely).
    pub fn with_parameters(&self, parameters: &[f64]) -> DeviceResult<Self> {
        let needed = self
            .gates
            .iter()
            .filter(|g| {
                matches!(
                    g,
                    QuantumGate::RX(..) | QuantumGate::RY(..) | QuantumGate::RZ(..)
                )
            })
            .count();
        if parameters.len() < needed {
            return Err(DeviceError::InvalidInput(format!(
                "Circuit has {needed} parameterized gates but only {} parameters were provided",
                parameters.len()
            )));
        }

        let mut new_gates = Vec::with_capacity(self.gates.len());
        let mut idx = 0;
        for gate in &self.gates {
            let replaced = match gate {
                QuantumGate::RX(q, _) => {
                    let g = QuantumGate::RX(*q, parameters[idx]);
                    idx += 1;
                    g
                }
                QuantumGate::RY(q, _) => {
                    let g = QuantumGate::RY(*q, parameters[idx]);
                    idx += 1;
                    g
                }
                QuantumGate::RZ(q, _) => {
                    let g = QuantumGate::RZ(*q, parameters[idx]);
                    idx += 1;
                    g
                }
                other => other.clone(),
            };
            new_gates.push(replaced);
        }

        Ok(Self {
            num_qubits: self.num_qubits,
            gates: new_gates,
        })
    }

    pub fn add_h_gate(&mut self, qubit: usize) -> DeviceResult<()> {
        if qubit >= self.num_qubits {
            return Err(DeviceError::InvalidInput(format!(
                "Qubit {qubit} out of range"
            )));
        }
        self.gates.push(QuantumGate::H(qubit));
        Ok(())
    }

    pub fn add_rx_gate(&mut self, qubit: usize, angle: f64) -> DeviceResult<()> {
        if qubit >= self.num_qubits {
            return Err(DeviceError::InvalidInput(format!(
                "Qubit {qubit} out of range"
            )));
        }
        self.gates.push(QuantumGate::RX(qubit, angle));
        Ok(())
    }

    pub fn add_ry_gate(&mut self, qubit: usize, angle: f64) -> DeviceResult<()> {
        if qubit >= self.num_qubits {
            return Err(DeviceError::InvalidInput(format!(
                "Qubit {qubit} out of range"
            )));
        }
        self.gates.push(QuantumGate::RY(qubit, angle));
        Ok(())
    }

    pub fn add_rz_gate(&mut self, qubit: usize, angle: f64) -> DeviceResult<()> {
        if qubit >= self.num_qubits {
            return Err(DeviceError::InvalidInput(format!(
                "Qubit {qubit} out of range"
            )));
        }
        self.gates.push(QuantumGate::RZ(qubit, angle));
        Ok(())
    }

    pub fn add_cnot_gate(&mut self, control: usize, target: usize) -> DeviceResult<()> {
        if control >= self.num_qubits || target >= self.num_qubits {
            return Err(DeviceError::InvalidInput(
                "Qubit index out of range".to_string(),
            ));
        }
        self.gates.push(QuantumGate::CNOT(control, target));
        Ok(())
    }

    pub fn add_s_dagger_gate(&mut self, qubit: usize) -> DeviceResult<()> {
        if qubit >= self.num_qubits {
            return Err(DeviceError::InvalidInput(format!(
                "Qubit {qubit} out of range"
            )));
        }
        self.gates.push(QuantumGate::SDagger(qubit));
        Ok(())
    }

    pub fn add_x_gate(&mut self, qubit: usize) -> DeviceResult<()> {
        if qubit >= self.num_qubits {
            return Err(DeviceError::InvalidInput(format!(
                "Qubit {qubit} out of range"
            )));
        }
        self.gates.push(QuantumGate::X(qubit));
        Ok(())
    }
}

/// Create a VQE instance for molecular simulation
pub fn create_molecular_vqe(
    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
    molecule: MolecularHamiltonian,
) -> DeviceResult<VQE> {
    let hamiltonian = molecule.to_hamiltonian();
    let ansatz = HardwareEfficientAnsatz {
        num_qubits: hamiltonian.num_qubits,
        num_layers: 3,
        entangling_gates: EntanglingGateType::Linear,
    };

    let optimizer = Box::new(AdamOptimizer::new(0.01));
    let config = VQEConfig::default();

    Ok(VQE::new(
        device,
        hamiltonian,
        Box::new(ansatz),
        optimizer,
        config,
    ))
}

/// Molecular Hamiltonian representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MolecularHamiltonian {
    pub one_body_integrals: Vec<Vec<f64>>,
    pub two_body_integrals: Vec<Vec<Vec<Vec<f64>>>>,
    pub nuclear_repulsion: f64,
    pub num_orbitals: usize,
}

impl MolecularHamiltonian {
    pub fn to_hamiltonian(&self) -> Hamiltonian {
        let mut terms = Vec::new();

        // One-body terms
        for i in 0..self.num_orbitals {
            for j in 0..self.num_orbitals {
                if self.one_body_integrals[i][j].abs() > 1e-12 {
                    // Create Pauli terms for fermionic operators
                    // This is simplified - full implementation would use Jordan-Wigner transformation
                    terms.push(PauliTerm {
                        coefficient: self.one_body_integrals[i][j],
                        paulis: vec![(i, PauliOperator::Z), (j, PauliOperator::Z)],
                    });
                }
            }
        }

        // Add nuclear repulsion as constant term
        terms.push(PauliTerm {
            coefficient: self.nuclear_repulsion,
            paulis: vec![], // Identity term
        });

        Hamiltonian {
            terms,
            num_qubits: self.num_orbitals,
        }
    }
}

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

    #[test]
    fn test_hamiltonian_creation() {
        let hamiltonian = Hamiltonian {
            terms: vec![
                PauliTerm {
                    coefficient: 1.0,
                    paulis: vec![(0, PauliOperator::Z)],
                },
                PauliTerm {
                    coefficient: 0.5,
                    paulis: vec![(0, PauliOperator::Z), (1, PauliOperator::Z)],
                },
            ],
            num_qubits: 2,
        };

        assert_eq!(hamiltonian.terms.len(), 2);
        assert_eq!(hamiltonian.num_qubits, 2);
    }

    #[test]
    fn test_hardware_efficient_ansatz() {
        let ansatz = HardwareEfficientAnsatz {
            num_qubits: 4,
            num_layers: 2,
            entangling_gates: EntanglingGateType::Linear,
        };

        assert_eq!(ansatz.parameter_count(), 16); // 2 * 4 * 2

        let params = ansatz.initialize_parameters();
        assert_eq!(params.len(), 16);
    }

    #[test]
    fn test_adam_optimizer() {
        let mut optimizer = AdamOptimizer::new(0.01);
        let params = vec![1.0, 2.0, 3.0];
        let gradients = vec![0.1, -0.2, 0.05];

        let updated = optimizer
            .update_parameters(params.clone(), gradients)
            .expect("Adam optimizer update should succeed");
        assert_eq!(updated.len(), 3);

        // Parameters should be updated (not equal to original)
        assert_ne!(updated[0], params[0]);
        assert_ne!(updated[1], params[1]);
        assert_ne!(updated[2], params[2]);
    }

    #[test]
    fn test_quantum_state() {
        let mut counts = HashMap::new();
        counts.insert("00".to_string(), 500);
        counts.insert("11".to_string(), 500);

        let state = QuantumState::from_measurements(&counts, 2);
        let most_probable = state.get_most_probable_bitstring();

        // Either "00" or "11" should be most probable (they're equal)
        assert!(most_probable == "00" || most_probable == "11");

        let prob_00 = state.get_probability("00");
        let prob_11 = state.get_probability("11");

        assert!((prob_00 - 0.5).abs() < 0.01);
        assert!((prob_11 - 0.5).abs() < 0.01);
    }
}