quantrs2-tytan 0.1.3

//! Quantum Machine Learning integration for optimization.
//!
//! This module provides quantum-inspired and quantum-enhanced machine learning
//! algorithms for optimization problems.

#![allow(dead_code)]

#[cfg(feature = "dwave")]
use crate::compile::CompiledModel;
use scirs2_core::ndarray::{Array1, Array2, Array3};
use scirs2_core::random::prelude::*;
use scirs2_core::random::{Distribution, RandNormal};
use std::f64::consts::PI;

type Normal<T> = RandNormal<T>;

/// Quantum Boltzmann Machine for optimization
pub struct QuantumBoltzmannMachine {
    /// Number of visible units
    n_visible: usize,
    /// Number of hidden units
    n_hidden: usize,
    /// Transverse field strength
    transverse_field: f64,
    /// Temperature
    temperature: f64,
    /// Learning rate
    learning_rate: f64,
    /// Weights between visible and hidden units
    weights: Array2<f64>,
    /// Visible bias
    visible_bias: Array1<f64>,
    /// Hidden bias
    hidden_bias: Array1<f64>,
    /// Use quantum annealing for sampling
    use_quantum_sampling: bool,
}

impl QuantumBoltzmannMachine {
    /// Create new Quantum Boltzmann Machine
    pub fn new(n_visible: usize, n_hidden: usize) -> Self {
        use scirs2_core::random::prelude::*;
        let mut rng = StdRng::seed_from_u64(42);

        // Initialize weights and biases with simple random values
        let weights = Array2::from_shape_fn((n_visible, n_hidden), |_| rng.random_range(-0.1..0.1));
        let visible_bias = Array1::from_shape_fn(n_visible, |_| rng.random_range(-0.1..0.1));
        let hidden_bias = Array1::from_shape_fn(n_hidden, |_| rng.random_range(-0.1..0.1));

        Self {
            n_visible,
            n_hidden,
            transverse_field: 1.0,
            temperature: 1.0,
            learning_rate: 0.01,
            weights,
            visible_bias,
            hidden_bias,
            use_quantum_sampling: true,
        }
    }

    /// Set transverse field strength
    pub const fn with_transverse_field(mut self, field: f64) -> Self {
        self.transverse_field = field;
        self
    }

    /// Set temperature
    pub const fn with_temperature(mut self, temp: f64) -> Self {
        self.temperature = temp;
        self
    }

    /// Set learning rate
    pub const fn with_learning_rate(mut self, rate: f64) -> Self {
        self.learning_rate = rate;
        self
    }

    /// Train the QBM on data
    pub fn train(&mut self, data: &Array2<bool>, epochs: usize) -> Result<TrainingResult, String> {
        let mut loss_history = Vec::new();
        let batch_size = data.shape()[0];

        for epoch in 0..epochs {
            let mut epoch_loss = 0.0;

            // Mini-batch training
            for batch_idx in 0..batch_size {
                let visible_view = data.row(batch_idx);
                let visible: Array1<bool> = visible_view.to_owned();

                // Positive phase: sample hidden given visible
                let hidden_probs = self.hidden_given_visible(&visible);
                let hidden_sample = self.sample_units(&hidden_probs);

                // Negative phase: Gibbs sampling
                let (v_neg, h_neg) = if self.use_quantum_sampling {
                    self.quantum_gibbs_sampling(&visible)?
                } else {
                    self.classical_gibbs_sampling(&visible, 1)
                };

                // Update weights and biases
                self.update_parameters(&visible, &hidden_sample, &v_neg, &h_neg);

                // Compute reconstruction error
                let reconstruction_error = self.compute_reconstruction_error(&visible, &v_neg);
                epoch_loss += reconstruction_error;
            }

            loss_history.push(epoch_loss / batch_size as f64);

            // Adaptive learning rate
            if epoch > 0 && loss_history[epoch] > loss_history[epoch - 1] {
                self.learning_rate *= 0.95;
            }
        }

        let final_loss = *loss_history
            .last()
            .ok_or("Training failed: epochs must be > 0")?;
        let converged = final_loss < 0.01;

        Ok(TrainingResult {
            final_loss,
            loss_history,
            converged,
        })
    }

    /// Hidden units given visible
    fn hidden_given_visible(&self, visible: &Array1<bool>) -> Array1<f64> {
        let visible_float: Array1<f64> = visible.mapv(|v| if v { 1.0 } else { -1.0 });
        let activation = self.hidden_bias.clone() + visible_float.dot(&self.weights);
        activation.mapv(|a| 1.0 / (1.0 + (-a / self.temperature).exp()))
    }

    /// Visible units given hidden
    fn visible_given_hidden(&self, hidden: &Array1<bool>) -> Array1<f64> {
        let hidden_float: Array1<f64> = hidden.mapv(|h| if h { 1.0 } else { -1.0 });
        let activation = self.visible_bias.clone() + self.weights.dot(&hidden_float);
        activation.mapv(|a| 1.0 / (1.0 + (-a / self.temperature).exp()))
    }

    /// Sample units from probabilities
    fn sample_units(&self, probs: &Array1<f64>) -> Array1<bool> {
        let mut rng = thread_rng();
        probs.mapv(|p| rng.random_bool(p))
    }

    /// Classical Gibbs sampling
    fn classical_gibbs_sampling(
        &self,
        initial_visible: &Array1<bool>,
        steps: usize,
    ) -> (Array1<bool>, Array1<bool>) {
        let mut visible = initial_visible.clone();
        let mut hidden = Array1::from_elem(self.n_hidden, false);

        for _ in 0..steps {
            let hidden_probs = self.hidden_given_visible(&visible);
            hidden = self.sample_units(&hidden_probs);

            let visible_probs = self.visible_given_hidden(&hidden);
            visible = self.sample_units(&visible_probs);
        }

        (visible, hidden)
    }

    /// Quantum-enhanced Gibbs sampling
    fn quantum_gibbs_sampling(
        &self,
        initial_visible: &Array1<bool>,
    ) -> Result<(Array1<bool>, Array1<bool>), String> {
        // Simulate quantum tunneling effects
        let mut rng = thread_rng();
        let tunneling_prob = (-2.0 / self.transverse_field).exp();

        let mut visible = initial_visible.clone();
        let mut hidden = Array1::from_elem(self.n_hidden, false);

        // Quantum evolution
        for _ in 0..10 {
            // Classical update
            let hidden_probs = self.hidden_given_visible(&visible);
            hidden = self.sample_units(&hidden_probs);

            // Quantum tunneling
            if rng.random_bool(tunneling_prob) {
                // Flip random spins
                let flip_idx = rng.random_range(0..self.n_visible);
                visible[flip_idx] = !visible[flip_idx];
            }

            let visible_probs = self.visible_given_hidden(&hidden);
            visible = self.sample_units(&visible_probs);

            // Hidden unit tunneling
            if rng.random_bool(tunneling_prob) {
                let flip_idx = rng.random_range(0..self.n_hidden);
                hidden[flip_idx] = !hidden[flip_idx];
            }
        }

        Ok((visible, hidden))
    }

    /// Update parameters using contrastive divergence
    fn update_parameters(
        &mut self,
        v_pos: &Array1<bool>,
        h_pos: &Array1<bool>,
        v_neg: &Array1<bool>,
        h_neg: &Array1<bool>,
    ) {
        let v_pos_float: Array1<f64> = v_pos.mapv(|v| if v { 1.0 } else { -1.0 });
        let h_pos_float: Array1<f64> = h_pos.mapv(|h| if h { 1.0 } else { -1.0 });
        let v_neg_float: Array1<f64> = v_neg.mapv(|v| if v { 1.0 } else { -1.0 });
        let h_neg_float: Array1<f64> = h_neg.mapv(|h| if h { 1.0 } else { -1.0 });

        // Update weights
        for i in 0..self.n_visible {
            for j in 0..self.n_hidden {
                let positive = v_pos_float[i] * h_pos_float[j];
                let negative = v_neg_float[i] * h_neg_float[j];
                self.weights[[i, j]] += self.learning_rate * (positive - negative);
            }
        }

        // Update biases
        self.visible_bias += &(self.learning_rate * (v_pos_float - v_neg_float));
        self.hidden_bias += &(self.learning_rate * (h_pos_float - h_neg_float));
    }

    /// Compute reconstruction error
    fn compute_reconstruction_error(
        &self,
        original: &Array1<bool>,
        reconstructed: &Array1<bool>,
    ) -> f64 {
        original
            .iter()
            .zip(reconstructed.iter())
            .filter(|(&o, &r)| o != r)
            .count() as f64
            / original.len() as f64
    }

    /// Generate samples for optimization
    pub fn generate_samples(&self, num_samples: usize) -> Vec<Array1<bool>> {
        let mut samples = Vec::new();
        let mut rng = thread_rng();

        for _ in 0..num_samples {
            // Start from random visible state
            let initial_visible = Array1::from_shape_fn(self.n_visible, |_| rng.random_bool(0.5));

            let (sample, _) = self.classical_gibbs_sampling(&initial_visible, 100);
            samples.push(sample);
        }

        samples
    }
}

#[derive(Debug, Clone)]
pub struct TrainingResult {
    pub final_loss: f64,
    pub loss_history: Vec<f64>,
    pub converged: bool,
}

/// Quantum Variational Autoencoder for optimization
pub struct QuantumVAE {
    /// Input dimension
    input_dim: usize,
    /// Latent dimension
    latent_dim: usize,
    /// Number of quantum layers
    n_layers: usize,
    /// Encoder parameters
    encoder_params: Array2<f64>,
    /// Decoder parameters
    decoder_params: Array2<f64>,
    /// Use quantum circuit for encoding
    use_quantum_encoder: bool,
    /// Noise model
    noise_strength: f64,
}

impl QuantumVAE {
    /// Create new Quantum VAE
    pub fn new(input_dim: usize, latent_dim: usize, n_layers: usize) -> Self {
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();

        let encoder_params =
            Array2::from_shape_fn((n_layers, input_dim), |_| rng.random_range(-0.1..0.1));

        let decoder_params =
            Array2::from_shape_fn((n_layers, latent_dim), |_| rng.random_range(-0.1..0.1));

        Self {
            input_dim,
            latent_dim,
            n_layers,
            encoder_params,
            decoder_params,
            use_quantum_encoder: true,
            noise_strength: 0.01,
        }
    }

    /// Encode input to latent space
    pub fn encode(&self, input: &Array1<bool>) -> (Array1<f64>, Array1<f64>) {
        if self.use_quantum_encoder {
            self.quantum_encode(input)
        } else {
            self.classical_encode(input)
        }
    }

    /// Quantum encoding
    fn quantum_encode(&self, input: &Array1<bool>) -> (Array1<f64>, Array1<f64>) {
        let input_float: Array1<f64> = input.mapv(|x| if x { 1.0 } else { 0.0 });
        let mut state = input_float;

        // Apply quantum layers
        for layer in 0..self.n_layers {
            // Rotation gates
            for i in 0..self.input_dim {
                let angle = self.encoder_params[[layer, i]];
                state[i] = state[i].mul_add(angle.cos(), (1.0 - state[i]) * angle.sin());
            }

            // Entangling gates (simplified)
            for i in 0..self.input_dim - 1 {
                let temp = state[i];
                state[i] = state[i].mul_add(0.9, state[i + 1] * 0.1);
                state[i + 1] = state[i + 1].mul_add(0.9, temp * 0.1);
            }
        }

        // Extract mean and variance for latent distribution
        let mean = state
            .slice(scirs2_core::ndarray::s![..self.latent_dim])
            .to_owned();
        let log_var = state
            .slice(scirs2_core::ndarray::s![
                self.latent_dim..2 * self.latent_dim.min(self.input_dim)
            ])
            .to_owned();

        (mean, log_var)
    }

    /// Classical encoding
    fn classical_encode(&self, input: &Array1<bool>) -> (Array1<f64>, Array1<f64>) {
        let input_float: Array1<f64> = input.mapv(|x| if x { 1.0 } else { 0.0 });

        // Simple linear encoding
        let encoded = self.encoder_params.dot(&input_float);

        let mean = Array1::from_vec(encoded.iter().take(self.latent_dim).copied().collect());
        let log_var = Array1::from_vec(
            encoded
                .iter()
                .skip(self.latent_dim)
                .take(self.latent_dim)
                .copied()
                .collect(),
        );

        (mean, log_var)
    }

    /// Decode from latent space
    pub fn decode(&self, latent: &Array1<f64>) -> Array1<f64> {
        let mut output = latent.clone();

        // Apply decoder layers
        for layer in 0..self.n_layers {
            let mut new_output = Array1::zeros(self.input_dim);

            for i in 0..self.latent_dim.min(output.len()) {
                for j in 0..self.input_dim {
                    new_output[j] += output[i] * self.decoder_params[[layer, i]].sin();
                }
            }

            output = new_output.mapv(|x: f64| 1.0 / (1.0 + (-x).exp()));
        }

        output
    }

    /// Reparameterization trick
    fn reparameterize(&self, mean: &Array1<f64>, log_var: &Array1<f64>) -> Array1<f64> {
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();
        let std = log_var.mapv(|x| (x / 2.0).exp());
        let eps = Array1::from_shape_fn(mean.len(), |_| rng.random_range(-1.0..1.0));

        mean + eps * std
    }

    /// Generate new samples
    pub fn generate(&self, num_samples: usize) -> Vec<Array1<bool>> {
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();
        let mut samples = Vec::new();

        for _ in 0..num_samples {
            // Sample from prior
            let z = Array1::from_shape_fn(self.latent_dim, |_| rng.random_range(-1.0..1.0));

            // Decode
            let decoded = self.decode(&z);

            // Convert to binary
            let binary = decoded.mapv(|x| x > 0.5);
            samples.push(binary);
        }

        samples
    }
}

/// Quantum Generative Adversarial Network for optimization
pub struct QuantumGAN {
    /// Generator network
    generator: QuantumGenerator,
    /// Discriminator network
    discriminator: QuantumDiscriminator,
    /// Training configuration
    config: QGANConfig,
}

#[derive(Clone)]
pub struct QuantumGenerator {
    /// Latent dimension
    latent_dim: usize,
    /// Output dimension
    output_dim: usize,
    /// Circuit depth
    depth: usize,
    /// Parameters
    params: Array2<f64>,
}

#[derive(Clone)]
pub struct QuantumDiscriminator {
    /// Input dimension
    input_dim: usize,
    /// Circuit depth
    depth: usize,
    /// Parameters
    params: Array2<f64>,
}

#[derive(Debug, Clone)]
pub struct QGANConfig {
    /// Learning rate for generator
    gen_lr: f64,
    /// Learning rate for discriminator
    disc_lr: f64,
    /// Number of discriminator updates per generator update
    disc_steps: usize,
    /// Gradient penalty coefficient
    gradient_penalty: f64,
    /// Use Wasserstein loss
    use_wasserstein: bool,
}

impl QuantumGAN {
    /// Create new Quantum GAN
    pub fn new(latent_dim: usize, output_dim: usize, depth: usize) -> Self {
        let generator = QuantumGenerator::new(latent_dim, output_dim, depth);
        let discriminator = QuantumDiscriminator::new(output_dim, depth);

        let config = QGANConfig {
            gen_lr: 0.0002,
            disc_lr: 0.0002,
            disc_steps: 5,
            gradient_penalty: 10.0,
            use_wasserstein: true,
        };

        Self {
            generator,
            discriminator,
            config,
        }
    }

    /// Train the QGAN
    pub fn train(
        &mut self,
        real_data: &[Array1<bool>],
        epochs: usize,
    ) -> Result<QGANTrainingResult, String> {
        let mut gen_losses = Vec::new();
        let mut disc_losses = Vec::new();
        let mut rng = thread_rng();

        for _epoch in 0..epochs {
            let mut epoch_gen_loss = 0.0;
            let mut epoch_disc_loss = 0.0;

            // Train discriminator
            for _ in 0..self.config.disc_steps {
                // Sample real data
                let real_idx = rng.random_range(0..real_data.len());
                let real_sample = &real_data[real_idx];

                // Generate fake data
                let fake_sample = self.generator.generate(&mut rng);

                // Update discriminator
                let disc_loss = self.discriminator.train_step(
                    real_sample,
                    &fake_sample,
                    self.config.disc_lr,
                    self.config.use_wasserstein,
                )?;

                epoch_disc_loss += disc_loss;
            }

            // Train generator
            let gen_loss = self.train_generator_step(&mut rng)?;
            epoch_gen_loss += gen_loss;

            gen_losses.push(epoch_gen_loss);
            disc_losses.push(epoch_disc_loss / self.config.disc_steps as f64);
        }

        let final_gen_loss = *gen_losses
            .last()
            .ok_or("Training failed: epochs must be > 0")?;
        let final_disc_loss = *disc_losses
            .last()
            .ok_or("Training failed: epochs must be > 0")?;

        Ok(QGANTrainingResult {
            generator_losses: gen_losses,
            discriminator_losses: disc_losses,
            final_gen_loss,
            final_disc_loss,
        })
    }

    /// Train generator for one step
    fn train_generator_step<R: Rng>(&mut self, rng: &mut R) -> Result<f64, String> {
        // Generate fake sample
        let fake_sample = self.generator.generate(rng);

        // Get discriminator score
        let disc_score = self.discriminator.forward(&fake_sample)?;

        // Compute loss (maximize discriminator score for fake samples)
        let loss = if self.config.use_wasserstein {
            -disc_score // Wasserstein loss
        } else {
            -(disc_score + 1e-8).ln() // BCE loss
        };

        // Update generator parameters
        self.generator.update_parameters(loss, self.config.gen_lr);

        Ok(loss)
    }

    /// Generate optimized samples
    pub fn generate_optimized(&self, num_samples: usize) -> Vec<Array1<bool>> {
        let mut rng = thread_rng();
        let mut samples = Vec::new();

        for _ in 0..num_samples {
            let sample = self.generator.generate(&mut rng);
            samples.push(sample);
        }

        samples
    }
}

impl QuantumGenerator {
    fn new(latent_dim: usize, output_dim: usize, depth: usize) -> Self {
        let mut rng = thread_rng();

        let params = Array2::from_shape_fn(
            (depth, latent_dim + output_dim),
            |_| rng.random::<f64>() * PI / 2.0 - PI / 4.0, // Sample from [-PI/4, PI/4]
        );

        Self {
            latent_dim,
            output_dim,
            depth,
            params,
        }
    }

    fn generate<R: Rng>(&self, rng: &mut R) -> Array1<bool> {
        // Sample latent vector using simple approach
        let latent = Array1::from_shape_fn(
            self.latent_dim,
            |_| rng.random::<f64>().mul_add(2.0, -1.0), // Sample from [-1, 1]
        );

        // Initialize quantum state
        let mut state = Array1::zeros(self.output_dim);

        // Apply quantum circuit
        for layer in 0..self.depth {
            // Rotation gates based on latent vector
            for i in 0..self.output_dim.min(self.latent_dim) {
                let angle = latent[i] * self.params[[layer, i]];
                state[i] = angle.sin();
            }

            // Entangling layer
            for i in 0..self.output_dim - 1 {
                let coupling = self.params[[layer, self.latent_dim + i]];
                let temp = state[i];
                state[i] = state[i].mul_add(coupling.cos(), state[i + 1] * coupling.sin());
                state[i + 1] = state[i + 1].mul_add(coupling.cos(), -(temp * coupling.sin()));
            }
        }

        // Measure (convert to binary)
        state.mapv(|x| x > 0.0)
    }

    fn update_parameters(&mut self, loss: f64, lr: f64) {
        // Simplified parameter update
        let gradient_estimate = loss * 0.1;
        self.params -= lr * gradient_estimate;
    }
}

impl QuantumDiscriminator {
    fn new(input_dim: usize, depth: usize) -> Self {
        let mut rng = thread_rng();
        let normal =
            Normal::new(0.0, PI / 4.0).expect("Normal distribution with std=PI/4 is always valid");

        let params = Array2::from_shape_fn((depth, input_dim), |_| normal.sample(&mut rng));

        Self {
            input_dim,
            depth,
            params,
        }
    }

    fn forward(&self, input: &Array1<bool>) -> Result<f64, String> {
        let input_float: Array1<f64> = input.mapv(|x| if x { 1.0 } else { -1.0 });
        let mut state = input_float;

        // Apply quantum circuit
        for layer in 0..self.depth {
            // Rotation gates
            for i in 0..self.input_dim {
                let angle = self.params[[layer, i]];
                state[i] = state[i].mul_add(angle.cos(), (1.0 - state[i].abs()) * angle.sin());
            }

            // Pooling layer (reduce dimension)
            if layer == self.depth - 1 {
                // Global pooling for final output
                return state
                    .mean()
                    .ok_or_else(|| "Cannot compute mean of empty state".to_string());
            }
        }

        Ok(state[0])
    }

    fn train_step(
        &mut self,
        real: &Array1<bool>,
        fake: &Array1<bool>,
        lr: f64,
        wasserstein: bool,
    ) -> Result<f64, String> {
        let real_score = self.forward(real)?;
        let fake_score = self.forward(fake)?;

        let loss = if wasserstein {
            fake_score - real_score // Wasserstein loss
        } else {
            -(real_score + 1e-8).ln() - (1.0 - fake_score + 1e-8).ln() // BCE loss
        };

        // Update parameters
        let gradient_estimate = loss * 0.1;
        self.params -= lr * gradient_estimate;

        Ok(loss)
    }
}

#[derive(Debug, Clone)]
pub struct QGANTrainingResult {
    pub generator_losses: Vec<f64>,
    pub discriminator_losses: Vec<f64>,
    pub final_gen_loss: f64,
    pub final_disc_loss: f64,
}

/// Quantum Reinforcement Learning for optimization
pub struct QuantumRL {
    /// State dimension
    state_dim: usize,
    /// Action dimension
    action_dim: usize,
    /// Q-network
    q_network: QuantumQNetwork,
    /// Experience replay buffer
    replay_buffer: Vec<Experience>,
    /// Exploration rate
    epsilon: f64,
    /// Discount factor
    gamma: f64,
    /// Learning rate
    learning_rate: f64,
}

#[derive(Clone)]
struct QuantumQNetwork {
    /// Input dimension
    input_dim: usize,
    /// Output dimension
    output_dim: usize,
    /// Number of layers
    n_layers: usize,
    /// Parameters
    params: Array3<f64>,
}

#[derive(Debug, Clone)]
pub struct Experience {
    pub state: Array1<f64>,
    pub action: usize,
    pub reward: f64,
    pub next_state: Array1<f64>,
    pub done: bool,
}

impl QuantumRL {
    /// Create new Quantum RL agent
    pub fn new(state_dim: usize, action_dim: usize) -> Self {
        let q_network = QuantumQNetwork::new(state_dim, action_dim, 4);

        Self {
            state_dim,
            action_dim,
            q_network,
            replay_buffer: Vec::new(),
            epsilon: 1.0,
            gamma: 0.99,
            learning_rate: 0.001,
        }
    }

    /// Select action using epsilon-greedy policy
    pub fn select_action(&self, state: &Array1<f64>, rng: &mut StdRng) -> usize {
        if rng.random_bool(self.epsilon) {
            // Explore
            rng.random_range(0..self.action_dim)
        } else {
            // Exploit
            let q_values = self.q_network.forward(state);
            q_values
                .iter()
                .enumerate()
                .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                .map(|(idx, _)| idx)
                .unwrap_or(0)
        }
    }

    /// Store experience in replay buffer
    pub fn store_experience(&mut self, experience: Experience) {
        self.replay_buffer.push(experience);

        // Limit buffer size
        if self.replay_buffer.len() > 10000 {
            self.replay_buffer.remove(0);
        }
    }

    /// Train Q-network
    pub fn train(&mut self, batch_size: usize) -> Result<f64, String> {
        if self.replay_buffer.len() < batch_size {
            return Ok(0.0);
        }

        let mut rng = thread_rng();
        let mut total_loss = 0.0;

        // Sample batch
        for _ in 0..batch_size {
            let idx = rng.random_range(0..self.replay_buffer.len());
            let experience = &self.replay_buffer[idx];

            // Compute target
            let target = if experience.done {
                experience.reward
            } else {
                let next_q_values = self.q_network.forward(&experience.next_state);
                self.gamma.mul_add(
                    next_q_values
                        .iter()
                        .copied()
                        .fold(f64::NEG_INFINITY, f64::max),
                    experience.reward,
                )
            };

            // Update Q-network
            let loss = self.q_network.update(
                &experience.state,
                experience.action,
                target,
                self.learning_rate,
            )?;

            total_loss += loss;
        }

        // Decay epsilon
        self.epsilon *= 0.995;
        self.epsilon = self.epsilon.max(0.01);

        Ok(total_loss / batch_size as f64)
    }
}

impl QuantumQNetwork {
    fn new(input_dim: usize, output_dim: usize, n_layers: usize) -> Self {
        let mut rng = thread_rng();
        let normal =
            Normal::new(0.0, 0.1).expect("Normal distribution with std=0.1 is always valid");

        let params = Array3::from_shape_fn(
            (n_layers, input_dim + output_dim, input_dim + output_dim),
            |_| normal.sample(&mut rng),
        );

        Self {
            input_dim,
            output_dim,
            n_layers,
            params,
        }
    }

    fn forward(&self, state: &Array1<f64>) -> Array1<f64> {
        // Encode state into quantum circuit
        let mut quantum_state = Array1::zeros(self.input_dim + self.output_dim);
        quantum_state
            .slice_mut(scirs2_core::ndarray::s![..self.input_dim])
            .assign(state);

        // Apply quantum layers
        for layer in 0..self.n_layers {
            let mut new_state = Array1::zeros(quantum_state.len());

            // Matrix multiplication (simplified)
            for i in 0..quantum_state.len() {
                for j in 0..quantum_state.len() {
                    new_state[i] += quantum_state[j] * self.params[[layer, i, j]];
                }
            }

            // Non-linearity (quantum measurement)
            quantum_state = new_state.mapv(|x: f64| x.tanh());
        }

        // Extract Q-values
        quantum_state
            .slice(scirs2_core::ndarray::s![self.input_dim..])
            .to_owned()
    }

    fn update(
        &mut self,
        state: &Array1<f64>,
        action: usize,
        target: f64,
        lr: f64,
    ) -> Result<f64, String> {
        let q_values = self.forward(state);
        let prediction = q_values[action];
        let loss = (target - prediction).powi(2);

        // Gradient descent (simplified)
        let gradient = 2.0 * (prediction - target);

        // Update parameters
        for layer in 0..self.n_layers {
            for i in 0..self.params.shape()[1] {
                for j in 0..self.params.shape()[2] {
                    self.params[[layer, i, j]] -= lr * gradient * 0.01;
                }
            }
        }

        Ok(loss)
    }
}

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

    #[test]
    fn test_quantum_boltzmann_machine() {
        let mut qbm = QuantumBoltzmannMachine::new(4, 2);

        // Create training data
        let data = Array2::from_shape_fn((10, 4), |(i, j)| (i + j) % 2 == 0);

        let mut result = qbm.train(&data, 10);
        assert!(result.is_ok());

        let samples = qbm.generate_samples(5);
        assert_eq!(samples.len(), 5);
    }

    #[test]
    fn test_quantum_vae() {
        let qvae = QuantumVAE::new(8, 2, 3);

        let input = Array1::from_vec(vec![true, false, true, false, true, false, true, false]);
        let (mean, log_var) = qvae.encode(&input);

        assert_eq!(mean.len(), 2);
        assert_eq!(log_var.len(), 2);

        let samples = qvae.generate(3);
        assert_eq!(samples.len(), 3);
    }

    #[test]
    fn test_quantum_gan() {
        let mut qgan = QuantumGAN::new(2, 4, 2);

        // Create fake training data
        let mut real_data = vec![
            Array1::from_vec(vec![true, false, true, false]),
            Array1::from_vec(vec![false, true, false, true]),
        ];

        let mut result = qgan.train(&real_data, 5);
        assert!(result.is_ok());

        let samples = qgan.generate_optimized(3);
        assert_eq!(samples.len(), 3);
    }

    #[test]
    fn test_quantum_rl() {
        let mut qrl = QuantumRL::new(4, 2);
        let mut rng = StdRng::seed_from_u64(42);

        let mut state = Array1::from_vec(vec![0.1, 0.2, 0.3, 0.4]);
        let action = qrl.select_action(&state, &mut rng);
        assert!(action < 2);

        let experience = Experience {
            state: state.clone(),
            action,
            reward: 1.0,
            next_state: Array1::from_vec(vec![0.2, 0.3, 0.4, 0.5]),
            done: false,
        };

        qrl.store_experience(experience);
        assert_eq!(qrl.replay_buffer.len(), 1);
    }
}