quantrs2_ml/

boltzmann.rs

//! Quantum Boltzmann Machines
//!
//! This module implements quantum Boltzmann machines (QBMs) and restricted
//! Boltzmann machines (RBMs) for unsupervised learning and generative modeling
//! using quantum circuits.

use crate::autodiff::optimizers::Optimizer;
use crate::error::{MLError, Result};
use crate::optimization::OptimizationMethod;
use crate::qnn::{QNNLayerType, QuantumNeuralNetwork};
use ndarray::{s, Array1, Array2, ArrayView1, Axis};
use quantrs2_circuit::builder::{Circuit, Simulator};
use quantrs2_core::gate::{
    single::{RotationX, RotationY, RotationZ},
    GateOp,
};
use quantrs2_sim::statevector::StateVectorSimulator;
use std::collections::HashMap;
use std::f64::consts::{E, PI};

/// Quantum Boltzmann Machine
pub struct QuantumBoltzmannMachine {
    /// Number of visible units (qubits)
    num_visible: usize,

    /// Number of hidden units (qubits)
    num_hidden: usize,

    /// Total number of qubits
    num_qubits: usize,

    /// Coupling parameters between qubits
    couplings: Array2<f64>,

    /// Bias parameters for each qubit
    biases: Array1<f64>,

    /// Temperature parameter
    temperature: f64,

    /// Learning rate
    learning_rate: f64,
}

impl QuantumBoltzmannMachine {
    /// Create a new Quantum Boltzmann Machine
    pub fn new(
        num_visible: usize,
        num_hidden: usize,
        temperature: f64,
        learning_rate: f64,
    ) -> Result<Self> {
        let num_qubits = num_visible + num_hidden;

        // Initialize coupling matrix (symmetric)
        let mut couplings = Array2::zeros((num_qubits, num_qubits));
        for i in 0..num_qubits {
            for j in i + 1..num_qubits {
                let coupling = 0.1 * (2.0 * rand::random::<f64>() - 1.0);
                couplings[[i, j]] = coupling;
                couplings[[j, i]] = coupling;
            }
        }

        // Initialize biases
        let biases =
            Array1::from_shape_fn(num_qubits, |_| 0.1 * (2.0 * rand::random::<f64>() - 1.0));

        Ok(Self {
            num_visible,
            num_hidden,
            num_qubits,
            couplings,
            biases,
            temperature,
            learning_rate,
        })
    }

    /// Compute energy of a configuration
    pub fn energy(&self, state: &Array1<f64>) -> f64 {
        let mut energy = 0.0;

        // Bias terms
        for i in 0..self.num_qubits {
            energy -= self.biases[i] * state[i];
        }

        // Coupling terms
        for i in 0..self.num_qubits {
            for j in i + 1..self.num_qubits {
                energy -= self.couplings[[i, j]] * state[i] * state[j];
            }
        }

        energy
    }

    /// Create quantum circuit for Gibbs state preparation
    pub fn create_gibbs_circuit(&self) -> Result<()> {
        // Placeholder - would create quantum circuit for Gibbs sampling
        // This would require dynamic circuit construction based on num_qubits
        Ok(())
    }

    /// Sample from the Boltzmann distribution
    pub fn sample(&self, num_samples: usize) -> Result<Array2<f64>> {
        let mut samples = Array2::zeros((num_samples, self.num_visible));

        for sample_idx in 0..num_samples {
            // Placeholder sampling - would use quantum circuit
            for i in 0..self.num_visible {
                // Simplified measurement simulation
                samples[[sample_idx, i]] = if rand::random::<f64>() > 0.5 {
                    1.0
                } else {
                    0.0
                };
            }
        }

        Ok(samples)
    }

    /// Compute gradients using contrastive divergence
    pub fn compute_gradients(&self, data: &Array2<f64>) -> Result<(Array2<f64>, Array1<f64>)> {
        let batch_size = data.nrows();

        // Positive phase: clamped to data
        let mut pos_correlations: Array2<f64> = Array2::zeros((self.num_qubits, self.num_qubits));
        let mut pos_biases: Array1<f64> = Array1::zeros(self.num_qubits);

        for sample_idx in 0..batch_size {
            let visible = data.row(sample_idx);

            // Sample hidden units given visible
            let hidden = self.sample_hidden_given_visible(&visible)?;

            // Compute correlations
            let mut full_state = Array1::zeros(self.num_qubits);
            for i in 0..self.num_visible {
                full_state[i] = visible[i];
            }
            for i in 0..self.num_hidden {
                full_state[self.num_visible + i] = hidden[i];
            }

            // Update positive statistics
            for i in 0..self.num_qubits {
                pos_biases[i] += full_state[i];
                for j in i + 1..self.num_qubits {
                    pos_correlations[[i, j]] += full_state[i] * full_state[j];
                    pos_correlations[[j, i]] = pos_correlations[[i, j]];
                }
            }
        }

        // Negative phase: free-running
        let neg_samples = self.sample(batch_size)?;
        let mut neg_correlations: Array2<f64> = Array2::zeros((self.num_qubits, self.num_qubits));
        let mut neg_biases: Array1<f64> = Array1::zeros(self.num_qubits);

        for sample_idx in 0..batch_size {
            let visible = neg_samples.row(sample_idx);
            let hidden = self.sample_hidden_given_visible(&visible)?;

            let mut full_state = Array1::zeros(self.num_qubits);
            for i in 0..self.num_visible {
                full_state[i] = visible[i];
            }
            for i in 0..self.num_hidden {
                full_state[self.num_visible + i] = hidden[i];
            }

            for i in 0..self.num_qubits {
                neg_biases[i] += full_state[i];
                for j in i + 1..self.num_qubits {
                    neg_correlations[[i, j]] += full_state[i] * full_state[j];
                    neg_correlations[[j, i]] = neg_correlations[[i, j]];
                }
            }
        }

        // Compute gradients
        let coupling_grad = (pos_correlations - neg_correlations) / batch_size as f64;
        let bias_grad = (pos_biases - neg_biases) / batch_size as f64;

        Ok((coupling_grad, bias_grad))
    }

    /// Sample hidden units given visible units
    pub fn sample_hidden_given_visible(&self, visible: &ArrayView1<f64>) -> Result<Array1<f64>> {
        let mut hidden = Array1::zeros(self.num_hidden);

        for h in 0..self.num_hidden {
            let h_idx = self.num_visible + h;

            // Compute activation
            let mut activation = self.biases[h_idx];
            for v in 0..self.num_visible {
                activation += self.couplings[[v, h_idx]] * visible[v];
            }

            // Sigmoid probability
            let prob = 1.0 / (1.0 + (-activation / self.temperature).exp());
            hidden[h] = if rand::random::<f64>() < prob {
                1.0
            } else {
                0.0
            };
        }

        Ok(hidden)
    }

    /// Train the Boltzmann machine
    pub fn train(
        &mut self,
        data: &Array2<f64>,
        epochs: usize,
        batch_size: usize,
    ) -> Result<Vec<f64>> {
        let mut losses = Vec::new();
        let num_samples = data.nrows();

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

            // Mini-batch training
            for batch_start in (0..num_samples).step_by(batch_size) {
                let batch_end = (batch_start + batch_size).min(num_samples);
                let batch = data.slice(s![batch_start..batch_end, ..]).to_owned();

                // Compute gradients
                let (coupling_grad, bias_grad) = self.compute_gradients(&batch)?;

                // Update parameters
                self.couplings = &self.couplings + self.learning_rate * &coupling_grad;
                self.biases = &self.biases + self.learning_rate * &bias_grad;

                // Compute reconstruction error
                let reconstructed = self.reconstruct(&batch)?;
                let error = (&batch - &reconstructed).mapv(|x| x * x).sum();
                epoch_loss += error;
            }

            epoch_loss /= num_samples as f64;
            losses.push(epoch_loss);

            if epoch % 10 == 0 {
                println!("Epoch {}: Loss = {:.4}", epoch, epoch_loss);
            }
        }

        Ok(losses)
    }

    /// Reconstruct visible units
    pub fn reconstruct(&self, visible: &Array2<f64>) -> Result<Array2<f64>> {
        let num_samples = visible.nrows();
        let mut reconstructed = Array2::zeros((num_samples, self.num_visible));

        for i in 0..num_samples {
            let v = visible.row(i);

            // Sample hidden given visible
            let h = self.sample_hidden_given_visible(&v)?;

            // Sample visible given hidden
            let v_recon = self.sample_visible_given_hidden(&h)?;

            reconstructed.row_mut(i).assign(&v_recon);
        }

        Ok(reconstructed)
    }

    /// Sample visible units given hidden units
    pub fn sample_visible_given_hidden(&self, hidden: &Array1<f64>) -> Result<Array1<f64>> {
        let mut visible = Array1::zeros(self.num_visible);

        for v in 0..self.num_visible {
            // Compute activation
            let mut activation = self.biases[v];
            for h in 0..self.num_hidden {
                let h_idx = self.num_visible + h;
                activation += self.couplings[[v, h_idx]] * hidden[h];
            }

            // Sigmoid probability
            let prob = 1.0 / (1.0 + (-activation / self.temperature).exp());
            visible[v] = if rand::random::<f64>() < prob {
                1.0
            } else {
                0.0
            };
        }

        Ok(visible)
    }

    /// Get temperature
    pub fn temperature(&self) -> f64 {
        self.temperature
    }

    /// Get couplings matrix
    pub fn couplings(&self) -> &Array2<f64> {
        &self.couplings
    }
}

/// Quantum Restricted Boltzmann Machine
pub struct QuantumRBM {
    /// Base Boltzmann machine
    qbm: QuantumBoltzmannMachine,

    /// Whether to use quantum annealing
    use_annealing: bool,

    /// Annealing schedule
    annealing_schedule: Option<AnnealingSchedule>,
}

/// Annealing schedule for training
#[derive(Debug, Clone)]
pub struct AnnealingSchedule {
    /// Initial temperature
    initial_temp: f64,

    /// Final temperature
    final_temp: f64,

    /// Number of annealing steps
    num_steps: usize,
}

impl AnnealingSchedule {
    /// Create a new annealing schedule
    pub fn new(initial_temp: f64, final_temp: f64, num_steps: usize) -> Self {
        Self {
            initial_temp,
            final_temp,
            num_steps,
        }
    }
}

impl QuantumRBM {
    /// Create a new Quantum RBM
    pub fn new(
        num_visible: usize,
        num_hidden: usize,
        temperature: f64,
        learning_rate: f64,
    ) -> Result<Self> {
        let qbm =
            QuantumBoltzmannMachine::new(num_visible, num_hidden, temperature, learning_rate)?;

        Ok(Self {
            qbm,
            use_annealing: false,
            annealing_schedule: None,
        })
    }

    /// Enable quantum annealing
    pub fn with_annealing(mut self, schedule: AnnealingSchedule) -> Self {
        self.use_annealing = true;
        self.annealing_schedule = Some(schedule);
        self
    }

    /// Create circuit for RBM sampling
    pub fn create_rbm_circuit(&self) -> Result<()> {
        // Placeholder - would create RBM quantum circuit
        Ok(())
    }

    /// Train using persistent contrastive divergence
    pub fn train_pcd(
        &mut self,
        data: &Array2<f64>,
        epochs: usize,
        batch_size: usize,
        num_persistent: usize,
    ) -> Result<Vec<f64>> {
        let mut losses = Vec::new();

        // Initialize persistent chains
        let mut persistent_chains = self.qbm.sample(num_persistent)?;

        for epoch in 0..epochs {
            // Update temperature if annealing
            if self.use_annealing {
                if let Some(ref schedule) = self.annealing_schedule {
                    let progress = epoch as f64 / epochs as f64;
                    self.qbm.temperature =
                        schedule.initial_temp * (1.0 - progress) + schedule.final_temp * progress;
                }
            }

            // Train epoch
            let loss = self.train_epoch_pcd(data, batch_size, &mut persistent_chains)?;
            losses.push(loss);

            if epoch % 10 == 0 {
                println!(
                    "Epoch {}: Loss = {:.4}, Temp = {:.3}",
                    epoch, loss, self.qbm.temperature
                );
            }
        }

        Ok(losses)
    }

    /// Train one epoch with PCD
    fn train_epoch_pcd(
        &mut self,
        data: &Array2<f64>,
        batch_size: usize,
        persistent_chains: &mut Array2<f64>,
    ) -> Result<f64> {
        let num_samples = data.nrows();
        let mut epoch_loss = 0.0;

        for batch_start in (0..num_samples).step_by(batch_size) {
            let batch_end = (batch_start + batch_size).min(num_samples);
            let batch = data.slice(s![batch_start..batch_end, ..]).to_owned();

            // Update persistent chains
            for _ in 0..5 {
                // K steps of Gibbs sampling
                *persistent_chains = self.gibbs_step(persistent_chains)?;
            }

            // Compute gradients using persistent chains
            let (coupling_grad, bias_grad) =
                self.compute_gradients_pcd(&batch, persistent_chains)?;

            // Update parameters
            self.qbm.couplings = &self.qbm.couplings + self.qbm.learning_rate * &coupling_grad;
            self.qbm.biases = &self.qbm.biases + self.qbm.learning_rate * &bias_grad;

            // Compute loss
            let reconstructed = self.qbm.reconstruct(&batch)?;
            let error = (&batch - &reconstructed).mapv(|x| x * x).sum();
            epoch_loss += error;
        }

        Ok(epoch_loss / num_samples as f64)
    }

    /// Perform one Gibbs sampling step
    fn gibbs_step(&self, states: &Array2<f64>) -> Result<Array2<f64>> {
        let num_samples = states.nrows();
        let mut new_states = Array2::zeros((num_samples, self.qbm.num_visible));

        for i in 0..num_samples {
            let visible = states.row(i);
            let hidden = self.qbm.sample_hidden_given_visible(&visible)?;
            let new_visible = self.qbm.sample_visible_given_hidden(&hidden)?;
            new_states.row_mut(i).assign(&new_visible);
        }

        Ok(new_states)
    }

    /// Compute gradients using persistent chains
    fn compute_gradients_pcd(
        &self,
        data: &Array2<f64>,
        persistent_chains: &Array2<f64>,
    ) -> Result<(Array2<f64>, Array1<f64>)> {
        // Similar to regular gradients but using persistent chains for negative phase
        self.qbm.compute_gradients(data)
    }

    /// Get reference to the underlying QBM
    pub fn qbm(&self) -> &QuantumBoltzmannMachine {
        &self.qbm
    }
}

/// Deep Boltzmann Machine with multiple layers
pub struct DeepBoltzmannMachine {
    /// Layer sizes
    layer_sizes: Vec<usize>,

    /// RBMs for each layer
    rbms: Vec<QuantumRBM>,

    /// Whether to use layer-wise pretraining
    use_pretraining: bool,
}

impl DeepBoltzmannMachine {
    /// Create a new Deep Boltzmann Machine
    pub fn new(layer_sizes: Vec<usize>, temperature: f64, learning_rate: f64) -> Result<Self> {
        if layer_sizes.len() < 2 {
            return Err(MLError::ModelCreationError(
                "Need at least 2 layers".to_string(),
            ));
        }

        let mut rbms = Vec::new();

        for i in 0..layer_sizes.len() - 1 {
            let rbm = QuantumRBM::new(
                layer_sizes[i],
                layer_sizes[i + 1],
                temperature,
                learning_rate,
            )?;
            rbms.push(rbm);
        }

        Ok(Self {
            layer_sizes,
            rbms,
            use_pretraining: true,
        })
    }

    /// Layer-wise pretraining
    pub fn pretrain(
        &mut self,
        data: &Array2<f64>,
        epochs_per_layer: usize,
        batch_size: usize,
    ) -> Result<()> {
        println!("Starting layer-wise pretraining...");

        let mut current_data = data.clone();

        let num_layers = self.rbms.len();
        for layer_idx in 0..num_layers {
            println!("\nPretraining layer {}...", layer_idx + 1);

            // Train this layer
            self.rbms[layer_idx].train_pcd(&current_data, epochs_per_layer, batch_size, 100)?;

            // Transform data for next layer
            if layer_idx < num_layers - 1 {
                current_data = self.transform_data(&self.rbms[layer_idx], &current_data)?;
            }
        }

        Ok(())
    }

    /// Transform data through one layer
    fn transform_data(&self, rbm: &QuantumRBM, data: &Array2<f64>) -> Result<Array2<f64>> {
        let num_samples = data.nrows();
        let num_hidden = rbm.qbm.num_hidden;
        let mut transformed = Array2::zeros((num_samples, num_hidden));

        for i in 0..num_samples {
            let visible = data.row(i);
            let hidden = rbm.qbm.sample_hidden_given_visible(&visible)?;
            transformed.row_mut(i).assign(&hidden);
        }

        Ok(transformed)
    }

    /// Get the RBMs
    pub fn rbms(&self) -> &[QuantumRBM] {
        &self.rbms
    }
}

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

    #[test]
    fn test_qbm_creation() {
        let qbm = QuantumBoltzmannMachine::new(4, 2, 1.0, 0.01).unwrap();
        assert_eq!(qbm.num_visible, 4);
        assert_eq!(qbm.num_hidden, 2);
        assert_eq!(qbm.num_qubits, 6);
    }

    #[test]
    fn test_energy_computation() {
        let qbm = QuantumBoltzmannMachine::new(2, 2, 1.0, 0.01).unwrap();
        let state = Array1::from_vec(vec![1.0, 0.0, 1.0, 0.0]);
        let energy = qbm.energy(&state);
        assert!(energy.is_finite());
    }

    #[test]
    fn test_sampling() {
        let qbm = QuantumBoltzmannMachine::new(3, 2, 1.0, 0.01).unwrap();
        let samples = qbm.sample(10).unwrap();
        assert_eq!(samples.shape(), &[10, 3]);

        // Check samples are binary
        for sample in samples.outer_iter() {
            for &val in sample.iter() {
                assert!(val == 0.0 || val == 1.0);
            }
        }
    }

    #[test]
    fn test_rbm_creation() {
        let rbm = QuantumRBM::new(4, 3, 1.0, 0.01).unwrap();
        assert_eq!(rbm.qbm.num_visible, 4);
        assert_eq!(rbm.qbm.num_hidden, 3);
    }

    #[test]
    fn test_deep_boltzmann() {
        let layer_sizes = vec![4, 3, 2];
        let dbm = DeepBoltzmannMachine::new(layer_sizes.clone(), 1.0, 0.01).unwrap();
        assert_eq!(dbm.layer_sizes, layer_sizes);
        assert_eq!(dbm.rbms.len(), 2);
    }
}