quantrs2_ml/

diffusion.rs

//! Quantum Diffusion Models
//!
//! This module implements quantum diffusion models for generative modeling,
//! adapting the denoising diffusion probabilistic model (DDPM) framework
//! to quantum circuits.

use crate::autodiff::optimizers::Optimizer;
use scirs2_core::random::prelude::*;
use crate::error::{MLError, Result};
use crate::optimization::OptimizationMethod;
use crate::qnn::{QNNLayerType, QuantumNeuralNetwork};
use scirs2_core::ndarray::{s, Array1, Array2, Array3, ArrayView1};
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::PI;

/// Noise schedule types for diffusion process
#[derive(Debug, Clone, Copy)]
pub enum NoiseSchedule {
    /// Linear schedule: β_t increases linearly
    Linear { beta_start: f64, beta_end: f64 },

    /// Cosine schedule: smoother noise addition
    Cosine { s: f64 },

    /// Quadratic schedule
    Quadratic { beta_start: f64, beta_end: f64 },

    /// Sigmoid schedule
    Sigmoid { beta_start: f64, beta_end: f64 },
}

/// Quantum diffusion model
pub struct QuantumDiffusionModel {
    /// Denoising network
    denoiser: QuantumNeuralNetwork,

    /// Number of diffusion timesteps
    num_timesteps: usize,

    /// Noise schedule
    noise_schedule: NoiseSchedule,

    /// Beta values for each timestep
    betas: Array1<f64>,

    /// Alpha values (1 - beta)
    alphas: Array1<f64>,

    /// Cumulative product of alphas
    alphas_cumprod: Array1<f64>,

    /// Data dimension
    data_dim: usize,

    /// Number of qubits
    num_qubits: usize,
}

impl QuantumDiffusionModel {
    /// Create a new quantum diffusion model
    pub fn new(
        data_dim: usize,
        num_qubits: usize,
        num_timesteps: usize,
        noise_schedule: NoiseSchedule,
    ) -> Result<Self> {
        // Create denoising quantum neural network
        let layers = vec![
            QNNLayerType::EncodingLayer {
                num_features: data_dim + 1,
            }, // +1 for timestep
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 3,
            },
            QNNLayerType::EntanglementLayer {
                connectivity: "circular".to_string(),
            },
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 3,
            },
            QNNLayerType::EntanglementLayer {
                connectivity: "circular".to_string(),
            },
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 3,
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "Pauli-Z".to_string(),
            },
        ];

        let denoiser = QuantumNeuralNetwork::new(
            layers,
            num_qubits,
            data_dim + 1, // Input includes timestep
            data_dim,     // Output is denoised data
        )?;

        // Compute noise schedule
        let (betas, alphas, alphas_cumprod) =
            Self::compute_schedule(num_timesteps, &noise_schedule);

        Ok(Self {
            denoiser,
            num_timesteps,
            noise_schedule,
            betas,
            alphas,
            alphas_cumprod,
            data_dim,
            num_qubits,
        })
    }

    /// Compute noise schedule values
    fn compute_schedule(
        num_timesteps: usize,
        schedule: &NoiseSchedule,
    ) -> (Array1<f64>, Array1<f64>, Array1<f64>) {
        let mut betas = Array1::zeros(num_timesteps);

        match schedule {
            NoiseSchedule::Linear {
                beta_start,
                beta_end,
            } => {
                for t in 0..num_timesteps {
                    betas[t] = beta_start
                        + (beta_end - beta_start) * t as f64 / (num_timesteps - 1) as f64;
                }
            }
            NoiseSchedule::Cosine { s } => {
                for t in 0..num_timesteps {
                    let f_t = ((t as f64 / num_timesteps as f64 + s) / (1.0 + s) * PI / 2.0)
                        .cos()
                        .powi(2);
                    let f_t_prev = if t == 0 {
                        1.0
                    } else {
                        (((t - 1) as f64 / num_timesteps as f64 + s) / (1.0 + s) * PI / 2.0)
                            .cos()
                            .powi(2)
                    };
                    betas[t] = 1.0 - f_t / f_t_prev;
                }
            }
            NoiseSchedule::Quadratic {
                beta_start,
                beta_end,
            } => {
                for t in 0..num_timesteps {
                    let ratio = t as f64 / (num_timesteps - 1) as f64;
                    betas[t] = beta_start + (beta_end - beta_start) * ratio * ratio;
                }
            }
            NoiseSchedule::Sigmoid {
                beta_start,
                beta_end,
            } => {
                for t in 0..num_timesteps {
                    let x = 10.0 * (t as f64 / (num_timesteps - 1) as f64 - 0.5);
                    let sigmoid = 1.0 / (1.0 + (-x).exp());
                    betas[t] = beta_start + (beta_end - beta_start) * sigmoid;
                }
            }
        }

        // Compute alphas and cumulative products
        let alphas = 1.0 - &betas;
        let mut alphas_cumprod = Array1::zeros(num_timesteps);
        alphas_cumprod[0] = alphas[0];

        for t in 1..num_timesteps {
            alphas_cumprod[t] = alphas_cumprod[t - 1] * alphas[t];
        }

        (betas, alphas, alphas_cumprod)
    }

    /// Forward diffusion process: add noise to data
    pub fn forward_diffusion(
        &self,
        x0: &Array1<f64>,
        t: usize,
    ) -> Result<(Array1<f64>, Array1<f64>)> {
        if t >= self.num_timesteps {
            return Err(MLError::ModelCreationError("Invalid timestep".to_string()));
        }

        // Sample noise
        let noise = Array1::from_shape_fn(self.data_dim, |_| {
            // Box-Muller transform for Gaussian noise
            let u1 = thread_rng().gen::<f64>();
            let u2 = thread_rng().gen::<f64>();
            (-2.0 * u1.ln()).sqrt() * (2.0 * PI * u2).cos()
        });

        // Add noise according to schedule
        let sqrt_alpha_cumprod = self.alphas_cumprod[t].sqrt();
        let sqrt_one_minus_alpha_cumprod = (1.0 - self.alphas_cumprod[t]).sqrt();

        let xt = sqrt_alpha_cumprod * x0 + sqrt_one_minus_alpha_cumprod * &noise;

        Ok((xt, noise))
    }

    /// Predict noise from noisy data using quantum circuit
    pub fn predict_noise(&self, xt: &Array1<f64>, t: usize) -> Result<Array1<f64>> {
        // Prepare input with timestep encoding
        let mut input = Array1::zeros(self.data_dim + 1);
        for i in 0..self.data_dim {
            input[i] = xt[i];
        }
        input[self.data_dim] = t as f64 / self.num_timesteps as f64; // Normalized timestep

        // Placeholder - would use quantum circuit to predict noise
        let predicted_noise = self.extract_noise_prediction_placeholder()?;

        Ok(predicted_noise)
    }

    /// Extract noise prediction from quantum state (placeholder)
    fn extract_noise_prediction_placeholder(&self) -> Result<Array1<f64>> {
        // Placeholder - would measure expectation values
        let noise = Array1::from_shape_fn(self.data_dim, |_| 2.0 * thread_rng().gen::<f64>() - 1.0);
        Ok(noise)
    }

    /// Reverse diffusion process: denoise step by step
    pub fn reverse_diffusion_step(&self, xt: &Array1<f64>, t: usize) -> Result<Array1<f64>> {
        if t == 0 {
            return Ok(xt.clone());
        }

        // Predict noise
        let predicted_noise = self.predict_noise(xt, t)?;

        // Compute denoising step
        let beta_t = self.betas[t];
        let sqrt_one_minus_alpha_cumprod = (1.0 - self.alphas_cumprod[t]).sqrt();
        let sqrt_recip_alpha = 1.0 / self.alphas[t].sqrt();

        // Mean of reverse process
        let mean =
            sqrt_recip_alpha * (xt - beta_t / sqrt_one_minus_alpha_cumprod * &predicted_noise);

        // Add noise for t > 1
        let xt_prev = if t > 1 {
            let noise_scale = beta_t.sqrt();
            let noise = Array1::from_shape_fn(self.data_dim, |_| {
                let u1 = thread_rng().gen::<f64>();
                let u2 = thread_rng().gen::<f64>();
                (-2.0 * u1.ln()).sqrt() * (2.0 * PI * u2).cos()
            });
            mean + noise_scale * noise
        } else {
            mean
        };

        Ok(xt_prev)
    }

    /// Generate new samples
    pub fn generate(&self, num_samples: usize) -> Result<Array2<f64>> {
        let mut samples = Array2::zeros((num_samples, self.data_dim));

        for sample_idx in 0..num_samples {
            // Start from pure noise
            let mut xt = Array1::from_shape_fn(self.data_dim, |_| {
                let u1 = thread_rng().gen::<f64>();
                let u2 = thread_rng().gen::<f64>();
                (-2.0 * u1.ln()).sqrt() * (2.0 * PI * u2).cos()
            });

            // Reverse diffusion
            for t in (0..self.num_timesteps).rev() {
                xt = self.reverse_diffusion_step(&xt, t)?;
            }

            // Store generated sample
            samples.row_mut(sample_idx).assign(&xt);
        }

        Ok(samples)
    }

    /// Train the diffusion model
    pub fn train(
        &mut self,
        data: &Array2<f64>,
        optimizer: &mut dyn Optimizer,
        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;
            let mut num_batches = 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 = data.slice(s![batch_start..batch_end, ..]);

                let mut batch_loss = 0.0;

                // Train on each sample in batch
                for sample_idx in 0..batch_data.nrows() {
                    let x0 = batch_data.row(sample_idx).to_owned();

                    // Random timestep
                    let t = fastrand::usize(0..self.num_timesteps);

                    // Forward diffusion
                    let (xt, true_noise) = self.forward_diffusion(&x0, t)?;

                    // Predict noise
                    let predicted_noise = self.predict_noise(&xt, t)?;

                    // Compute loss (MSE between true and predicted noise)
                    let noise_diff = &predicted_noise - &true_noise;
                    let loss = noise_diff.mapv(|x| x * x).sum() / self.data_dim as f64;
                    batch_loss += loss;
                }

                batch_loss /= batch_data.nrows() as f64;
                epoch_loss += batch_loss;
                num_batches += 1;

                // Update parameters (placeholder)
                self.update_parameters(optimizer, batch_loss)?;
            }

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

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

        Ok(losses)
    }

    /// Update model parameters
    fn update_parameters(&mut self, optimizer: &mut dyn Optimizer, loss: f64) -> Result<()> {
        // Placeholder - would compute quantum gradients
        Ok(())
    }

    /// Conditional generation given a condition
    pub fn conditional_generate(
        &self,
        condition: &Array1<f64>,
        num_samples: usize,
    ) -> Result<Array2<f64>> {
        // Placeholder for conditional generation
        // Would modify the reverse process based on condition
        self.generate(num_samples)
    }

    /// Get beta values
    pub fn betas(&self) -> &Array1<f64> {
        &self.betas
    }

    /// Get alpha cumulative product values
    pub fn alphas_cumprod(&self) -> &Array1<f64> {
        &self.alphas_cumprod
    }
}

/// Quantum Score-Based Diffusion
pub struct QuantumScoreDiffusion {
    /// Score network (estimates gradient of log probability)
    score_net: QuantumNeuralNetwork,

    /// Noise levels for score matching
    noise_levels: Array1<f64>,

    /// Data dimension
    data_dim: usize,
}

impl QuantumScoreDiffusion {
    /// Create new score-based diffusion model
    pub fn new(data_dim: usize, num_qubits: usize, num_noise_levels: usize) -> Result<Self> {
        let layers = vec![
            QNNLayerType::EncodingLayer {
                num_features: data_dim + 1,
            }, // +1 for noise level
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 4,
            },
            QNNLayerType::EntanglementLayer {
                connectivity: "full".to_string(),
            },
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 4,
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "Pauli-XYZ".to_string(),
            },
        ];

        let score_net = QuantumNeuralNetwork::new(
            layers,
            num_qubits,
            data_dim + 1,
            data_dim, // Output is score (gradient)
        )?;

        // Geometric sequence of noise levels
        let noise_levels = Array1::from_shape_fn(num_noise_levels, |i| {
            0.01 * (10.0_f64).powf(i as f64 / (num_noise_levels - 1) as f64)
        });

        Ok(Self {
            score_net,
            noise_levels,
            data_dim,
        })
    }

    /// Estimate score (gradient of log density)
    pub fn estimate_score(&self, x: &Array1<f64>, noise_level: f64) -> Result<Array1<f64>> {
        // Prepare input with noise level
        let mut input = Array1::zeros(self.data_dim + 1);
        for i in 0..self.data_dim {
            input[i] = x[i];
        }
        input[self.data_dim] = noise_level;

        // Placeholder - would use quantum circuit to estimate score

        // Placeholder - would extract gradient estimate
        let score = Array1::from_shape_fn(self.data_dim, |_| 2.0 * thread_rng().gen::<f64>() - 1.0);

        Ok(score)
    }

    /// Langevin dynamics sampling
    pub fn langevin_sample(
        &self,
        init: Array1<f64>,
        noise_level: f64,
        num_steps: usize,
        step_size: f64,
    ) -> Result<Array1<f64>> {
        let mut x = init;

        for _ in 0..num_steps {
            // Estimate score
            let score = self.estimate_score(&x, noise_level)?;

            // Langevin update
            let noise = Array1::from_shape_fn(self.data_dim, |_| {
                let u1 = thread_rng().gen::<f64>();
                let u2 = thread_rng().gen::<f64>();
                (-2.0 * u1.ln()).sqrt() * (2.0 * PI * u2).cos()
            });

            x = &x + step_size * &score + (2.0 * step_size).sqrt() * noise;
        }

        Ok(x)
    }

    /// Get noise levels
    pub fn noise_levels(&self) -> &Array1<f64> {
        &self.noise_levels
    }
}

/// Variational Diffusion Model with quantum components
pub struct QuantumVariationalDiffusion {
    /// Encoder network
    encoder: QuantumNeuralNetwork,

    /// Decoder network
    decoder: QuantumNeuralNetwork,

    /// Latent dimension
    latent_dim: usize,

    /// Data dimension
    data_dim: usize,
}

impl QuantumVariationalDiffusion {
    /// Create new variational diffusion model
    pub fn new(data_dim: usize, latent_dim: usize, num_qubits: usize) -> Result<Self> {
        // Encoder: data -> latent
        let encoder_layers = vec![
            QNNLayerType::EncodingLayer {
                num_features: data_dim,
            },
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 3,
            },
            QNNLayerType::EntanglementLayer {
                connectivity: "circular".to_string(),
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "computational".to_string(),
            },
        ];

        let encoder = QuantumNeuralNetwork::new(
            encoder_layers,
            num_qubits,
            data_dim,
            latent_dim * 2, // Mean and variance
        )?;

        // Decoder: latent -> data
        let decoder_layers = vec![
            QNNLayerType::EncodingLayer {
                num_features: latent_dim,
            },
            QNNLayerType::VariationalLayer {
                num_params: num_qubits * 3,
            },
            QNNLayerType::EntanglementLayer {
                connectivity: "circular".to_string(),
            },
            QNNLayerType::MeasurementLayer {
                measurement_basis: "Pauli-Z".to_string(),
            },
        ];

        let decoder = QuantumNeuralNetwork::new(decoder_layers, num_qubits, latent_dim, data_dim)?;

        Ok(Self {
            encoder,
            decoder,
            latent_dim,
            data_dim,
        })
    }

    /// Encode data to latent space
    pub fn encode(&self, x: &Array1<f64>) -> Result<(Array1<f64>, Array1<f64>)> {
        // Placeholder - would run quantum encoder
        let mean = Array1::zeros(self.latent_dim);
        let log_var = Array1::zeros(self.latent_dim);
        Ok((mean, log_var))
    }

    /// Decode from latent space
    pub fn decode(&self, z: &Array1<f64>) -> Result<Array1<f64>> {
        // Placeholder - would run quantum decoder
        Ok(Array1::zeros(self.data_dim))
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::autodiff::optimizers::Adam;

    #[test]
    fn test_noise_schedule() {
        let num_timesteps = 100;
        let schedule = NoiseSchedule::Linear {
            beta_start: 0.0001,
            beta_end: 0.02,
        };

        let (betas, alphas, alphas_cumprod) =
            QuantumDiffusionModel::compute_schedule(num_timesteps, &schedule);

        assert_eq!(betas.len(), num_timesteps);
        assert!(betas[0] < betas[num_timesteps - 1]);
        assert!(alphas_cumprod[0] > alphas_cumprod[num_timesteps - 1]);
    }

    #[test]
    fn test_forward_diffusion() {
        let model = QuantumDiffusionModel::new(
            4,   // data_dim
            4,   // num_qubits
            100, // timesteps
            NoiseSchedule::Linear {
                beta_start: 0.0001,
                beta_end: 0.02,
            },
        )
        .unwrap();

        let x0 = Array1::from_vec(vec![0.5, -0.3, 0.8, -0.1]);
        let (xt, noise) = model.forward_diffusion(&x0, 50).unwrap();

        assert_eq!(xt.len(), 4);
        assert_eq!(noise.len(), 4);
    }

    #[test]
    fn test_generation() {
        let model = QuantumDiffusionModel::new(
            2,  // data_dim
            4,  // num_qubits
            50, // timesteps
            NoiseSchedule::Cosine { s: 0.008 },
        )
        .unwrap();

        let samples = model.generate(5).unwrap();
        assert_eq!(samples.shape(), &[5, 2]);
    }

    #[test]
    fn test_score_diffusion() {
        let model = QuantumScoreDiffusion::new(
            3,  // data_dim
            4,  // num_qubits
            10, // noise levels
        )
        .unwrap();

        let x = Array1::from_vec(vec![0.1, 0.2, 0.3]);
        let score = model.estimate_score(&x, 0.1).unwrap();

        assert_eq!(score.len(), 3);
    }
}