quantrs2-core 0.1.3

//! Ultra-High-Fidelity Gate Synthesis
//!
//! Beyond-Shannon decomposition with quantum optimal control theory and
//! machine learning-optimized gate sequences.

use crate::error::QuantRS2Error;
use scirs2_core::ndarray::Array2;
use scirs2_core::random::ChaCha20Rng;
use scirs2_core::random::{Rng, RngExt, SeedableRng};
use scirs2_core::Complex64;
use std::collections::HashMap;
use std::f64::consts::PI;

/// GRAPE (Gradient Ascent Pulse Engineering) integration
#[derive(Debug, Clone)]
pub struct GrapeOptimizer {
    pub control_hamiltonians: Vec<Array2<Complex64>>,
    pub drift_hamiltonian: Array2<Complex64>,
    pub target_unitary: Array2<Complex64>,
    pub pulse_amplitudes: Array2<f64>,
    pub time_steps: usize,
    pub total_time: f64,
    pub convergence_threshold: f64,
}

impl GrapeOptimizer {
    /// Create a new GRAPE optimizer
    pub fn new(
        control_hamiltonians: Vec<Array2<Complex64>>,
        drift_hamiltonian: Array2<Complex64>,
        target_unitary: Array2<Complex64>,
        time_steps: usize,
        total_time: f64,
    ) -> Self {
        let num_controls = control_hamiltonians.len();
        let pulse_amplitudes = Array2::zeros((num_controls, time_steps));

        Self {
            control_hamiltonians,
            drift_hamiltonian,
            target_unitary,
            pulse_amplitudes,
            time_steps,
            total_time,
            convergence_threshold: 1e-8,
        }
    }

    /// Optimize control pulses using GRAPE algorithm
    pub fn optimize(
        &mut self,
        max_iterations: usize,
        learning_rate: f64,
    ) -> Result<GrapeResult, QuantRS2Error> {
        let dt = self.total_time / (self.time_steps as f64);
        let mut fidelity_history = Vec::new();

        for iteration in 0..max_iterations {
            // Forward propagation
            let (propagators, final_unitary) = self.forward_propagation(dt)?;

            // Compute fidelity
            let fidelity = self.compute_fidelity(&final_unitary);
            fidelity_history.push(fidelity);

            if fidelity > 1.0 - self.convergence_threshold {
                return Ok(GrapeResult {
                    optimized_pulses: self.pulse_amplitudes.clone(),
                    final_fidelity: fidelity,
                    iterations: iteration + 1,
                    fidelity_history,
                });
            }

            // Backward propagation and gradient computation
            let gradients = self.backward_propagation(&propagators, &final_unitary, dt)?;

            // Update pulse amplitudes
            self.update_pulses(&gradients, learning_rate);

            if iteration % 100 == 0 {
                println!("GRAPE Iteration {iteration}: Fidelity = {fidelity:.6}");
            }
        }

        Err(QuantRS2Error::OptimizationFailed(
            "GRAPE optimization did not converge".to_string(),
        ))
    }

    /// Forward propagation through time evolution
    fn forward_propagation(
        &self,
        dt: f64,
    ) -> Result<(Vec<Array2<Complex64>>, Array2<Complex64>), QuantRS2Error> {
        let n = self.drift_hamiltonian.nrows();
        let mut propagators = Vec::with_capacity(self.time_steps);
        let mut unitary = Array2::eye(n);

        for t in 0..self.time_steps {
            // Construct total Hamiltonian at time step t
            let mut total_h = self.drift_hamiltonian.clone();

            for (i, control_h) in self.control_hamiltonians.iter().enumerate() {
                let amplitude = self.pulse_amplitudes[[i, t]];
                total_h = total_h + Complex64::new(amplitude, 0.0) * control_h;
            }

            // Time evolution operator U = exp(-iHΔt)
            let evolution_op = self.matrix_exp(&(-Complex64::i() * dt * &total_h))?;
            propagators.push(evolution_op.clone());

            unitary = evolution_op.dot(&unitary);
        }

        Ok((propagators, unitary))
    }

    /// Backward propagation for gradient computation
    fn backward_propagation(
        &self,
        propagators: &[Array2<Complex64>],
        final_unitary: &Array2<Complex64>,
        dt: f64,
    ) -> Result<Array2<f64>, QuantRS2Error> {
        let num_controls = self.control_hamiltonians.len();
        let mut gradients = Array2::zeros((num_controls, self.time_steps));

        // Compute backward propagators
        let n = self.drift_hamiltonian.nrows();
        let mut backward_unitary = Array2::eye(n);
        let mut backward_props = vec![Array2::eye(n); self.time_steps];

        for t in (0..self.time_steps).rev() {
            backward_props[t].clone_from(&backward_unitary);
            backward_unitary = backward_unitary.dot(&propagators[t].t().mapv(|x| x.conj()));
        }

        // Compute fidelity gradients
        let fidelity_error = &self.target_unitary.t().mapv(|x| x.conj()) - final_unitary;

        for t in 0..self.time_steps {
            let forward_part = if t == 0 {
                Array2::eye(n)
            } else {
                propagators[..t]
                    .iter()
                    .fold(Array2::eye(n), |acc, p| acc.dot(p))
            };

            for (i, control_h) in self.control_hamiltonians.iter().enumerate() {
                // Gradient computation using chain rule
                let d_u_dpulse = -Complex64::i() * dt * control_h.dot(&forward_part);
                let gradient_matrix = &backward_props[t]
                    .t()
                    .mapv(|x| x.conj())
                    .dot(&fidelity_error.dot(&d_u_dpulse));

                gradients[[i, t]] = gradient_matrix.diag().map(|x| x.re).sum();
            }
        }

        Ok(gradients)
    }

    /// Update pulse amplitudes using gradients
    fn update_pulses(&mut self, gradients: &Array2<f64>, learning_rate: f64) {
        self.pulse_amplitudes = &self.pulse_amplitudes + learning_rate * gradients;

        // Apply constraints (e.g., amplitude bounds)
        self.pulse_amplitudes.mapv_inplace(|x| x.clamp(-10.0, 10.0));
    }

    /// Compute gate fidelity
    fn compute_fidelity(&self, achieved_unitary: &Array2<Complex64>) -> f64 {
        let n = achieved_unitary.nrows();
        let overlap = self
            .target_unitary
            .t()
            .mapv(|x| x.conj())
            .dot(achieved_unitary);
        let trace = overlap.diag().sum();
        trace.norm_sqr() / (n as f64).powi(2)
    }

    /// Matrix exponential (simplified implementation)
    fn matrix_exp(&self, matrix: &Array2<Complex64>) -> Result<Array2<Complex64>, QuantRS2Error> {
        // Simplified matrix exponential using series expansion
        let n = matrix.nrows();
        let mut result = Array2::eye(n);
        let mut term = Array2::eye(n);

        for k in 1..=20 {
            term = term.dot(matrix) / (k as f64);
            result = result + &term;

            // Calculate Frobenius norm manually
            let frobenius_norm = term.iter().map(|x| x.norm_sqr()).sum::<f64>().sqrt();
            if frobenius_norm < 1e-12 {
                break;
            }
        }

        Ok(result)
    }
}

/// GRAPE optimization result
#[derive(Debug, Clone)]
pub struct GrapeResult {
    pub optimized_pulses: Array2<f64>,
    pub final_fidelity: f64,
    pub iterations: usize,
    pub fidelity_history: Vec<f64>,
}

/// Reinforcement learning for gate sequence optimization
#[derive(Debug, Clone)]
pub struct QuantumGateRL {
    pub gate_library: Vec<Array2<Complex64>>,
    pub q_table: HashMap<Vec<usize>, Vec<f64>>,
    pub learning_rate: f64,
    pub discount_factor: f64,
    pub exploration_rate: f64,
    pub target_unitary: Array2<Complex64>,
}

impl QuantumGateRL {
    /// Create a new quantum gate RL optimizer
    pub fn new(gate_library: Vec<Array2<Complex64>>, target_unitary: Array2<Complex64>) -> Self {
        Self {
            gate_library,
            q_table: HashMap::new(),
            learning_rate: 0.1,
            discount_factor: 0.9,
            exploration_rate: 0.1,
            target_unitary,
        }
    }

    /// Train the RL agent to find optimal gate sequences
    pub fn train(
        &mut self,
        episodes: usize,
        max_sequence_length: usize,
    ) -> Result<RLResult, QuantRS2Error> {
        let mut best_sequence = Vec::new();
        let mut best_fidelity = 0.0;
        let mut fidelity_history = Vec::new();

        for episode in 0..episodes {
            let (sequence, fidelity) = self.run_episode(max_sequence_length)?;
            fidelity_history.push(fidelity);

            if fidelity > best_fidelity {
                best_fidelity = fidelity;
                best_sequence = sequence;
            }

            // Decay exploration rate
            self.exploration_rate *= 0.995;

            if episode % 1000 == 0 {
                println!("RL Episode {episode}: Best Fidelity = {best_fidelity:.6}");
            }
        }

        Ok(RLResult {
            best_sequence,
            best_fidelity,
            fidelity_history,
        })
    }

    /// Run a single training episode
    fn run_episode(&mut self, max_length: usize) -> Result<(Vec<usize>, f64), QuantRS2Error> {
        let mut sequence = Vec::new();
        let mut current_unitary = Array2::eye(self.target_unitary.nrows());
        let mut rng = ChaCha20Rng::from_seed([0u8; 32]); // Use fixed seed for deterministic behavior

        for step in 0..max_length {
            // Choose action (gate index) using ε-greedy policy
            let action = if rng.random::<f64>() < self.exploration_rate {
                rng.random_range(0..self.gate_library.len())
            } else {
                self.choose_best_action(&sequence)
            };

            // Apply gate
            current_unitary = current_unitary.dot(&self.gate_library[action]);
            sequence.push(action);

            // Compute reward (fidelity improvement)
            let fidelity = self.compute_fidelity(&current_unitary);
            let reward = if step == 0 {
                fidelity
            } else {
                fidelity - self.compute_partial_fidelity(&sequence[..step])
            };

            // Update Q-table
            self.update_q_table(&sequence, action, reward, step == max_length - 1);

            // Early termination if high fidelity achieved
            if fidelity > 0.999 {
                break;
            }
        }

        let final_fidelity = self.compute_fidelity(&current_unitary);
        Ok((sequence, final_fidelity))
    }

    /// Choose the best action based on Q-values
    fn choose_best_action(&self, state: &[usize]) -> usize {
        if let Some(q_values) = self.q_table.get(state) {
            q_values
                .iter()
                .enumerate()
                .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                .map_or(0, |(i, _)| i)
        } else {
            0 // Default action if state not seen before
        }
    }

    /// Update Q-table using Q-learning
    fn update_q_table(&mut self, sequence: &[usize], action: usize, reward: f64, terminal: bool) {
        let state = sequence[..sequence.len().saturating_sub(1)].to_vec();
        let next_state = sequence.to_vec();

        // Get current Q-value
        let current_q = self
            .q_table
            .entry(state.clone())
            .or_insert_with(|| vec![0.0; self.gate_library.len()])[action];

        // Compute target Q-value
        let next_q_max = if terminal {
            0.0
        } else {
            self.q_table.get(&next_state).map_or(0.0, |values| {
                values.iter().copied().fold(f64::NEG_INFINITY, f64::max)
            })
        };

        let target_q = self.discount_factor.mul_add(next_q_max, reward);

        // Update Q-value
        if let Some(q_values) = self.q_table.get_mut(&state) {
            q_values[action] += self.learning_rate * (target_q - current_q);
        }
    }

    /// Compute fidelity for current unitary
    fn compute_fidelity(&self, achieved_unitary: &Array2<Complex64>) -> f64 {
        let n = achieved_unitary.nrows();
        let overlap = self
            .target_unitary
            .t()
            .mapv(|x| x.conj())
            .dot(achieved_unitary);
        let trace = overlap.diag().sum();
        trace.norm_sqr() / (n as f64).powi(2)
    }

    /// Compute fidelity for partial sequence
    fn compute_partial_fidelity(&self, sequence: &[usize]) -> f64 {
        let mut unitary = Array2::eye(self.target_unitary.nrows());
        for &gate_idx in sequence {
            unitary = unitary.dot(&self.gate_library[gate_idx]);
        }
        self.compute_fidelity(&unitary)
    }
}

/// RL optimization result
#[derive(Debug, Clone)]
pub struct RLResult {
    pub best_sequence: Vec<usize>,
    pub best_fidelity: f64,
    pub fidelity_history: Vec<f64>,
}

/// Quantum error suppression during gate synthesis
#[derive(Debug, Clone)]
pub struct ErrorSuppressionSynthesis {
    pub target_unitary: Array2<Complex64>,
    pub noise_model: NoiseModel,
    pub error_suppression_level: f64,
    pub dynamical_decoupling: bool,
}

#[derive(Debug, Clone)]
pub enum NoiseModel {
    Depolarizing { error_rate: f64 },
    Dephasing { error_rate: f64 },
    AmplitudeDamping { error_rate: f64 },
    Composite { models: Vec<Self> },
}

impl ErrorSuppressionSynthesis {
    /// Create a new error suppression synthesis
    pub const fn new(
        target_unitary: Array2<Complex64>,
        noise_model: NoiseModel,
        error_suppression_level: f64,
    ) -> Self {
        Self {
            target_unitary,
            noise_model,
            error_suppression_level,
            dynamical_decoupling: true,
        }
    }

    /// Synthesize gate sequence with error suppression
    pub fn synthesize(&self) -> Result<ErrorSuppressedSequence, QuantRS2Error> {
        // Decompose target unitary with error awareness
        let base_sequence = self.base_decomposition()?;

        // Apply error suppression techniques
        let suppressed_sequence = if self.dynamical_decoupling {
            self.apply_dynamical_decoupling(&base_sequence)?
        } else {
            self.apply_composite_pulses(&base_sequence)?
        };

        // Optimize for noise resilience
        let optimized_sequence = self.optimize_for_noise(&suppressed_sequence)?;

        Ok(ErrorSuppressedSequence {
            gate_sequence: optimized_sequence.clone(),
            estimated_fidelity: self.estimate_noisy_fidelity(&optimized_sequence)?,
            error_suppression_factor: self.error_suppression_level,
        })
    }

    /// Base decomposition without error suppression
    fn base_decomposition(&self) -> Result<Vec<GateOperation>, QuantRS2Error> {
        // Simplified decomposition - in practice would use advanced algorithms
        let n = self.target_unitary.nrows();
        let mut operations = Vec::new();

        // Single-qubit case
        if n == 2 {
            let angles = self.single_qubit_angles(&self.target_unitary)?;
            operations.push(GateOperation::RZ(angles.0));
            operations.push(GateOperation::RY(angles.1));
            operations.push(GateOperation::RZ(angles.2));
        } else {
            // Multi-qubit decomposition (simplified)
            for i in 0..n.ilog2() {
                operations.push(GateOperation::Hadamard(i as usize));
                if i > 0 {
                    operations.push(GateOperation::CNOT(0, i as usize));
                }
            }
        }

        Ok(operations)
    }

    /// Apply dynamical decoupling
    fn apply_dynamical_decoupling(
        &self,
        sequence: &[GateOperation],
    ) -> Result<Vec<GateOperation>, QuantRS2Error> {
        let mut dd_sequence = Vec::new();

        for (i, operation) in sequence.iter().enumerate() {
            dd_sequence.push(operation.clone());

            // Insert decoupling pulses between operations
            if i < sequence.len() - 1 {
                dd_sequence.push(GateOperation::X(0)); // π-pulse for decoupling
                dd_sequence.push(GateOperation::Delay(1e-6)); // Short delay
                dd_sequence.push(GateOperation::X(0)); // Compensating π-pulse
            }
        }

        Ok(dd_sequence)
    }

    /// Apply composite pulses
    fn apply_composite_pulses(
        &self,
        sequence: &[GateOperation],
    ) -> Result<Vec<GateOperation>, QuantRS2Error> {
        let mut composite_sequence = Vec::new();

        for operation in sequence {
            match operation {
                GateOperation::RX(angle) => {
                    // BB1 composite pulse for X rotation
                    composite_sequence.extend(self.bb1_composite_x(*angle)?);
                }
                GateOperation::RY(angle) => {
                    // SK1 composite pulse for Y rotation
                    composite_sequence.extend(self.sk1_composite_y(*angle)?);
                }
                _ => composite_sequence.push(operation.clone()),
            }
        }

        Ok(composite_sequence)
    }

    /// BB1 composite pulse for robust X rotation
    fn bb1_composite_x(&self, angle: f64) -> Result<Vec<GateOperation>, QuantRS2Error> {
        let phi = angle;
        Ok(vec![
            GateOperation::RX(phi),
            GateOperation::RY(2.0 * PI),
            GateOperation::RX(2.0 * phi),
            GateOperation::RY(2.0 * PI),
            GateOperation::RX(phi),
        ])
    }

    /// SK1 composite pulse for robust Y rotation
    fn sk1_composite_y(&self, angle: f64) -> Result<Vec<GateOperation>, QuantRS2Error> {
        let phi = angle;
        Ok(vec![
            GateOperation::RY(phi / 2.0),
            GateOperation::RX(PI),
            GateOperation::RY(phi / 2.0),
        ])
    }

    /// Optimize sequence for noise resilience
    fn optimize_for_noise(
        &self,
        sequence: &[GateOperation],
    ) -> Result<Vec<GateOperation>, QuantRS2Error> {
        // Apply gradient-based optimization to minimize noise effects
        let mut optimized = sequence.to_vec();

        // Simplified optimization - adjust gate parameters
        for operation in &mut optimized {
            match operation {
                GateOperation::RX(angle) | GateOperation::RY(angle) | GateOperation::RZ(angle) => {
                    *angle *= self.error_suppression_level.mul_add(0.01, 1.0);
                }
                _ => {}
            }
        }

        Ok(optimized)
    }

    /// Estimate fidelity under noise
    fn estimate_noisy_fidelity(&self, sequence: &[GateOperation]) -> Result<f64, QuantRS2Error> {
        // Monte Carlo simulation of noisy execution
        let mut total_fidelity = 0.0;
        let num_samples = 100;

        for _ in 0..num_samples {
            let noisy_unitary = self.simulate_noisy_execution(sequence)?;
            let fidelity = self.compute_fidelity(&noisy_unitary);
            total_fidelity += fidelity;
        }

        Ok(total_fidelity / (num_samples as f64))
    }

    /// Simulate noisy execution of gate sequence
    fn simulate_noisy_execution(
        &self,
        sequence: &[GateOperation],
    ) -> Result<Array2<Complex64>, QuantRS2Error> {
        let n = self.target_unitary.nrows();
        let mut unitary = Array2::eye(n);
        let mut rng = ChaCha20Rng::from_seed([0u8; 32]); // Use fixed seed for deterministic behavior

        for operation in sequence {
            // Apply ideal gate
            let ideal_gate = self.operation_to_matrix(operation)?;
            unitary = unitary.dot(&ideal_gate);

            // Apply noise
            let noise_channel = self.sample_noise_channel(&mut rng)?;
            unitary = unitary.dot(&noise_channel);
        }

        Ok(unitary)
    }

    /// Sample noise channel based on noise model
    fn sample_noise_channel(
        &self,
        rng: &mut ChaCha20Rng,
    ) -> Result<Array2<Complex64>, QuantRS2Error> {
        match &self.noise_model {
            NoiseModel::Depolarizing { error_rate } => {
                if rng.random::<f64>() < *error_rate {
                    // Apply random Pauli
                    let pauli_idx = rng.random_range(0..4);
                    Ok(self.pauli_matrix(pauli_idx))
                } else {
                    Ok(Array2::eye(2))
                }
            }
            NoiseModel::Dephasing { error_rate } => {
                if rng.random::<f64>() < *error_rate {
                    Ok(self.pauli_matrix(3)) // Z gate
                } else {
                    Ok(Array2::eye(2))
                }
            }
            NoiseModel::AmplitudeDamping { error_rate: _ } => {
                // Simplified amplitude damping
                Ok(Array2::eye(2))
            }
            NoiseModel::Composite { models: _ } => {
                // Apply multiple noise channels
                Ok(Array2::eye(2))
            }
        }
    }

    /// Convert operation to matrix
    fn operation_to_matrix(
        &self,
        operation: &GateOperation,
    ) -> Result<Array2<Complex64>, QuantRS2Error> {
        match operation {
            GateOperation::RX(angle) => Ok(self.rx_matrix(*angle)),
            GateOperation::RY(angle) => Ok(self.ry_matrix(*angle)),
            GateOperation::RZ(angle) => Ok(self.rz_matrix(*angle)),
            GateOperation::X(_) => Ok(self.pauli_matrix(1)),
            GateOperation::Y(_) => Ok(self.pauli_matrix(2)),
            GateOperation::Z(_) => Ok(self.pauli_matrix(3)),
            GateOperation::Hadamard(_) => Ok(self.hadamard_matrix()),
            GateOperation::CNOT(_, _) => Ok(self.cnot_matrix()),
            GateOperation::Delay(_) => Ok(Array2::eye(2)),
        }
    }

    /// Helper: Single-qubit decomposition angles
    fn single_qubit_angles(
        &self,
        unitary: &Array2<Complex64>,
    ) -> Result<(f64, f64, f64), QuantRS2Error> {
        // ZYZ decomposition
        let u = unitary;

        // Extract angles (simplified)
        let alpha = u[[0, 0]].arg();
        let beta = 2.0 * (u[[1, 0]].norm()).acos();
        let gamma = u[[1, 1]].arg();

        Ok((alpha, beta, gamma))
    }

    /// Helper: Rotation matrices
    fn rx_matrix(&self, angle: f64) -> Array2<Complex64> {
        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();

        scirs2_core::ndarray::array![
            [
                Complex64::new(cos_half, 0.0),
                Complex64::new(0.0, -sin_half)
            ],
            [
                Complex64::new(0.0, -sin_half),
                Complex64::new(cos_half, 0.0)
            ]
        ]
    }

    fn ry_matrix(&self, angle: f64) -> Array2<Complex64> {
        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();

        scirs2_core::ndarray::array![
            [
                Complex64::new(cos_half, 0.0),
                Complex64::new(-sin_half, 0.0)
            ],
            [Complex64::new(sin_half, 0.0), Complex64::new(cos_half, 0.0)]
        ]
    }

    fn rz_matrix(&self, angle: f64) -> Array2<Complex64> {
        let exp_factor = Complex64::from_polar(1.0, angle / 2.0);

        scirs2_core::ndarray::array![
            [exp_factor.conj(), Complex64::new(0.0, 0.0)],
            [Complex64::new(0.0, 0.0), exp_factor]
        ]
    }

    fn pauli_matrix(&self, index: usize) -> Array2<Complex64> {
        match index {
            1 => scirs2_core::ndarray::array![
                // X
                [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
                [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)]
            ],
            2 => scirs2_core::ndarray::array![
                // Y
                [Complex64::new(0.0, 0.0), Complex64::new(0.0, -1.0)],
                [Complex64::new(0.0, 1.0), Complex64::new(0.0, 0.0)]
            ],
            3 => scirs2_core::ndarray::array![
                // Z
                [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)],
                [Complex64::new(0.0, 0.0), Complex64::new(-1.0, 0.0)]
            ],
            0 | _ => scirs2_core::ndarray::array![
                // I (identity) or default
                [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)],
                [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)]
            ],
        }
    }

    fn hadamard_matrix(&self) -> Array2<Complex64> {
        let inv_sqrt2 = 1.0 / 2.0_f64.sqrt();
        scirs2_core::ndarray::array![
            [
                Complex64::new(inv_sqrt2, 0.0),
                Complex64::new(inv_sqrt2, 0.0)
            ],
            [
                Complex64::new(inv_sqrt2, 0.0),
                Complex64::new(-inv_sqrt2, 0.0)
            ]
        ]
    }

    fn cnot_matrix(&self) -> Array2<Complex64> {
        scirs2_core::ndarray::array![
            [
                Complex64::new(1.0, 0.0),
                Complex64::new(0.0, 0.0),
                Complex64::new(0.0, 0.0),
                Complex64::new(0.0, 0.0)
            ],
            [
                Complex64::new(0.0, 0.0),
                Complex64::new(1.0, 0.0),
                Complex64::new(0.0, 0.0),
                Complex64::new(0.0, 0.0)
            ],
            [
                Complex64::new(0.0, 0.0),
                Complex64::new(0.0, 0.0),
                Complex64::new(0.0, 0.0),
                Complex64::new(1.0, 0.0)
            ],
            [
                Complex64::new(0.0, 0.0),
                Complex64::new(0.0, 0.0),
                Complex64::new(1.0, 0.0),
                Complex64::new(0.0, 0.0)
            ]
        ]
    }

    fn compute_fidelity(&self, achieved_unitary: &Array2<Complex64>) -> f64 {
        let n = achieved_unitary.nrows();
        let overlap = self
            .target_unitary
            .t()
            .mapv(|x| x.conj())
            .dot(achieved_unitary);
        let trace = overlap.diag().sum();
        trace.norm_sqr() / (n as f64).powi(2)
    }
}

/// Gate operation enumeration
#[derive(Debug, Clone)]
pub enum GateOperation {
    RX(f64),
    RY(f64),
    RZ(f64),
    X(usize),
    Y(usize),
    Z(usize),
    Hadamard(usize),
    CNOT(usize, usize),
    Delay(f64),
}

/// Error-suppressed gate sequence
#[derive(Debug, Clone)]
pub struct ErrorSuppressedSequence {
    pub gate_sequence: Vec<GateOperation>,
    pub estimated_fidelity: f64,
    pub error_suppression_factor: f64,
}

/// Ultra-high-fidelity gate synthesis orchestrator
#[derive(Debug)]
pub struct UltraHighFidelitySynthesis {
    pub grape_optimizer: Option<GrapeOptimizer>,
    pub rl_optimizer: Option<QuantumGateRL>,
    pub error_suppression: Option<ErrorSuppressionSynthesis>,
    pub synthesis_config: SynthesisConfig,
}

#[derive(Debug, Clone)]
pub struct SynthesisConfig {
    pub use_grape: bool,
    pub use_rl: bool,
    pub use_error_suppression: bool,
    pub fidelity_threshold: f64,
    pub max_gate_count: usize,
}

impl Default for SynthesisConfig {
    fn default() -> Self {
        Self {
            use_grape: true,
            use_rl: true,
            use_error_suppression: true,
            fidelity_threshold: 0.9999,
            max_gate_count: 100,
        }
    }
}

impl UltraHighFidelitySynthesis {
    /// Create a new ultra-high-fidelity synthesis engine
    pub fn new(target_unitary: Array2<Complex64>, config: SynthesisConfig) -> Self {
        let grape_optimizer = if config.use_grape {
            // Simplified control Hamiltonians for demonstration
            let control_h = vec![
                scirs2_core::ndarray::array![
                    [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
                    [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)]
                ],
                scirs2_core::ndarray::array![
                    [Complex64::new(0.0, 0.0), Complex64::new(0.0, -1.0)],
                    [Complex64::new(0.0, 1.0), Complex64::new(0.0, 0.0)]
                ],
            ];
            let drift_h = scirs2_core::ndarray::array![
                [Complex64::new(0.0, 0.0), Complex64::new(0.0, 0.0)],
                [Complex64::new(0.0, 0.0), Complex64::new(0.0, 0.0)]
            ];

            Some(GrapeOptimizer::new(
                control_h,
                drift_h,
                target_unitary.clone(),
                100,
                1.0,
            ))
        } else {
            None
        };

        let rl_optimizer = if config.use_rl {
            let gate_library = vec![
                scirs2_core::ndarray::array![
                    [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)],
                    [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)]
                ], // I
                scirs2_core::ndarray::array![
                    [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
                    [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)]
                ], // X
                scirs2_core::ndarray::array![
                    [Complex64::new(0.0, 0.0), Complex64::new(0.0, -1.0)],
                    [Complex64::new(0.0, 1.0), Complex64::new(0.0, 0.0)]
                ], // Y
                scirs2_core::ndarray::array![
                    [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)],
                    [Complex64::new(0.0, 0.0), Complex64::new(-1.0, 0.0)]
                ], // Z
            ];
            Some(QuantumGateRL::new(gate_library, target_unitary.clone()))
        } else {
            None
        };

        let error_suppression = if config.use_error_suppression {
            let noise_model = NoiseModel::Depolarizing { error_rate: 0.001 };
            Some(ErrorSuppressionSynthesis::new(
                target_unitary,
                noise_model,
                0.1,
            ))
        } else {
            None
        };

        Self {
            grape_optimizer,
            rl_optimizer,
            error_suppression,
            synthesis_config: config,
        }
    }

    /// Synthesize ultra-high-fidelity gate sequence
    pub fn synthesize(&mut self) -> Result<UltraFidelityResult, QuantRS2Error> {
        let mut results = Vec::new();

        // GRAPE optimization
        if let Some(ref mut grape) = self.grape_optimizer {
            let grape_result = grape.optimize(1000, 0.01)?;
            results.push(SynthesisMethod::GRAPE(grape_result));
        }

        // Reinforcement learning optimization
        if let Some(ref mut rl) = self.rl_optimizer {
            let rl_result = rl.train(5000, 20)?;
            results.push(SynthesisMethod::ReinforcementLearning(rl_result));
        }

        // Error suppression synthesis
        if let Some(ref error_supp) = self.error_suppression {
            let error_supp_result = error_supp.synthesize()?;
            results.push(SynthesisMethod::ErrorSuppression(error_supp_result));
        }

        // Select best result
        let best_result = self.select_best_result(results)?;

        Ok(UltraFidelityResult {
            synthesis_method: best_result.clone(),
            achieved_fidelity: self.get_result_fidelity(&best_result),
            gate_count: self.get_result_gate_count(&best_result),
            synthesis_time: std::time::Duration::from_secs(1), // Placeholder
        })
    }

    /// Select the best synthesis result
    fn select_best_result(
        &self,
        results: Vec<SynthesisMethod>,
    ) -> Result<SynthesisMethod, QuantRS2Error> {
        if results.is_empty() {
            return Err(QuantRS2Error::OptimizationFailed(
                "No synthesis methods produced results".to_string(),
            ));
        }

        // Select based on fidelity and gate count
        let best = results
            .into_iter()
            .max_by(|a, b| {
                let fidelity_a = self.get_method_fidelity(a);
                let fidelity_b = self.get_method_fidelity(b);
                fidelity_a
                    .partial_cmp(&fidelity_b)
                    .unwrap_or(std::cmp::Ordering::Equal)
            })
            .ok_or_else(|| {
                QuantRS2Error::OptimizationFailed("No synthesis results to compare".to_string())
            })?;

        Ok(best)
    }

    const fn get_method_fidelity(&self, method: &SynthesisMethod) -> f64 {
        match method {
            SynthesisMethod::GRAPE(result) => result.final_fidelity,
            SynthesisMethod::ReinforcementLearning(result) => result.best_fidelity,
            SynthesisMethod::ErrorSuppression(result) => result.estimated_fidelity,
        }
    }

    const fn get_result_fidelity(&self, method: &SynthesisMethod) -> f64 {
        self.get_method_fidelity(method)
    }

    fn get_result_gate_count(&self, method: &SynthesisMethod) -> usize {
        match method {
            SynthesisMethod::GRAPE(result) => result.optimized_pulses.ncols(),
            SynthesisMethod::ReinforcementLearning(result) => result.best_sequence.len(),
            SynthesisMethod::ErrorSuppression(result) => result.gate_sequence.len(),
        }
    }
}

/// Synthesis method enumeration
#[derive(Debug, Clone)]
pub enum SynthesisMethod {
    GRAPE(GrapeResult),
    ReinforcementLearning(RLResult),
    ErrorSuppression(ErrorSuppressedSequence),
}

/// Ultra-fidelity synthesis result
#[derive(Debug, Clone)]
pub struct UltraFidelityResult {
    pub synthesis_method: SynthesisMethod,
    pub achieved_fidelity: f64,
    pub gate_count: usize,
    pub synthesis_time: std::time::Duration,
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_grape_optimizer() {
        let control_h = vec![
            array![
                [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
                [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)]
            ],
            array![
                [Complex64::new(0.0, 0.0), Complex64::new(0.0, -1.0)],
                [Complex64::new(0.0, 1.0), Complex64::new(0.0, 0.0)]
            ],
        ];
        let drift_h = array![
            [Complex64::new(0.0, 0.0), Complex64::new(0.0, 0.0)],
            [Complex64::new(0.0, 0.0), Complex64::new(0.0, 0.0)]
        ];
        let target = array![
            [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
            [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)]
        ];

        let mut grape = GrapeOptimizer::new(control_h, drift_h, target, 10, 1.0);

        // Test optimization (should complete without error)
        let result = grape.optimize(100, 0.01);
        assert!(result.is_ok() || matches!(result, Err(QuantRS2Error::OptimizationFailed(_))));
    }

    #[test]
    fn test_quantum_gate_rl() {
        let gate_library = vec![
            array![
                [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)],
                [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)]
            ],
            array![
                [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
                [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)]
            ],
        ];
        let target = array![
            [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
            [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)]
        ];

        let mut rl = QuantumGateRL::new(gate_library, target);

        let result = rl.train(100, 5);
        assert!(result.is_ok());

        let rl_result = result.expect("RL training failed");
        assert!(!rl_result.best_sequence.is_empty());
        assert!(rl_result.best_fidelity >= 0.0);
    }

    #[test]
    fn test_error_suppression_synthesis() {
        let target = array![
            [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
            [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)]
        ];
        let noise_model = NoiseModel::Depolarizing { error_rate: 0.01 };

        let error_supp = ErrorSuppressionSynthesis::new(target, noise_model, 0.1);
        let result = error_supp.synthesize();

        assert!(result.is_ok());
        let sequence = result.expect("Error suppression synthesis failed");
        assert!(!sequence.gate_sequence.is_empty());
        assert!(sequence.estimated_fidelity >= 0.0);
    }

    #[test]
    #[ignore]
    fn test_ultra_high_fidelity_synthesis() {
        let target = array![
            [Complex64::new(0.0, 0.0), Complex64::new(1.0, 0.0)],
            [Complex64::new(1.0, 0.0), Complex64::new(0.0, 0.0)]
        ];
        let config = SynthesisConfig::default();

        let mut synthesis = UltraHighFidelitySynthesis::new(target, config);
        let result = synthesis.synthesize();

        // Should either succeed or fail gracefully
        assert!(result.is_ok() || matches!(result, Err(QuantRS2Error::OptimizationFailed(_))));
    }
}