quantrs2_ml/

quantum_advanced_diffusion.rs

//! Advanced Quantum Diffusion Models
//!
//! This module implements cutting-edge quantum diffusion models including:
//! - Quantum Diffusion Models with advanced denoising schedules
//! - Quantum Continuous Normalization Flows
//! - Quantum Score-Based Generative Models with optimal transport
//! - Quantum-Enhanced Stochastic Differential Equations

use crate::error::{MLError, Result};
use ndarray::{s, Array1, Array2, Array3, ArrayView1, Axis};
use num_complex::Complex64;
use rand::{Rng, SeedableRng};
use rand_chacha::ChaCha20Rng;
use std::collections::HashMap;
use std::f64::consts::PI;

/// Advanced quantum noise schedules with quantum-specific optimizations
#[derive(Debug, Clone)]
pub enum QuantumNoiseSchedule {
    /// Quantum-optimized cosine schedule with controlled entanglement decay
    QuantumCosine {
        s: f64,
        entanglement_preservation: f64,
        decoherence_rate: f64,
    },

    /// Quantum-aware exponential schedule respecting T1/T2 times
    QuantumExponential {
        lambda: f64,
        t1_time: f64,
        t2_time: f64,
    },

    /// Learned schedule optimized for quantum circuits
    LearnedQuantumSchedule {
        parameters: Array1<f64>,
        circuit_depth_factor: f64,
    },

    /// Phase-sensitive schedule for quantum interference effects
    PhaseSensitive {
        amplitude_schedule: Array1<f64>,
        phase_schedule: Array1<f64>,
    },

    /// Multi-scale quantum schedule for hierarchical features
    MultiScale {
        scales: Vec<f64>,
        weights: Array1<f64>,
        coherence_times: Array1<f64>,
    },
}

/// Configuration for advanced quantum diffusion
#[derive(Debug, Clone)]
pub struct QuantumAdvancedDiffusionConfig {
    pub data_dim: usize,
    pub num_qubits: usize,
    pub num_timesteps: usize,
    pub noise_schedule: QuantumNoiseSchedule,
    pub denoiser_architecture: DenoisingArchitecture,
    pub quantum_enhancement_level: f64,
    pub use_quantum_attention: bool,
    pub enable_entanglement_monitoring: bool,
    pub adaptive_denoising: bool,
    pub use_quantum_fourier_features: bool,
    pub error_mitigation_strategy: ErrorMitigationStrategy,
}

#[derive(Debug, Clone)]
pub enum DenoisingArchitecture {
    /// U-Net style architecture with quantum skip connections
    QuantumUNet {
        depth: usize,
        base_channels: usize,
        quantum_skip_connections: bool,
    },

    /// Transformer-based denoiser with quantum attention
    QuantumTransformer {
        num_layers: usize,
        num_heads: usize,
        hidden_dim: usize,
        quantum_attention_type: QuantumAttentionType,
    },

    /// Residual network with quantum blocks
    QuantumResNet {
        num_blocks: usize,
        channels_per_block: usize,
        quantum_residual_connections: bool,
    },

    /// Quantum Neural ODE denoiser
    QuantumNeuralODE {
        hidden_dims: Vec<usize>,
        integration_method: ODEIntegrationMethod,
        adaptive_step_size: bool,
    },
}

#[derive(Debug, Clone)]
pub enum QuantumAttentionType {
    FullQuantum,
    HybridClassicalQuantum,
    EntanglementBased,
    QuantumFourier,
}

#[derive(Debug, Clone)]
pub enum ODEIntegrationMethod {
    RungeKutta4,
    DormandPrince,
    AdaptiveQuantum,
    QuantumMidpoint,
}

#[derive(Debug, Clone)]
pub enum ErrorMitigationStrategy {
    None,
    ZeroNoiseExtrapolation,
    QuantumErrorSuppression,
    AdaptiveMitigation,
    TensorNetworkCorrection,
}

/// Advanced Quantum Diffusion Model with cutting-edge features
pub struct QuantumAdvancedDiffusionModel {
    config: QuantumAdvancedDiffusionConfig,

    // Denoising network components
    quantum_denoiser: QuantumDenoisingNetwork,

    // Noise schedule parameters
    betas: Array1<f64>,
    alphas: Array1<f64>,
    alphas_cumprod: Array1<f64>,

    // Quantum-specific parameters
    entanglement_schedule: Array1<f64>,
    phase_schedule: Array1<Complex64>,
    decoherence_factors: Array1<f64>,

    // Training history and metrics
    training_history: Vec<TrainingMetrics>,
    quantum_metrics: QuantumDiffusionMetrics,

    // Optimization state
    adaptive_learning_state: AdaptiveLearningState,
}

#[derive(Debug, Clone)]
pub struct QuantumDenoisingNetwork {
    architecture: DenoisingArchitecture,
    quantum_layers: Vec<QuantumDenoisingLayer>,
    classical_layers: Vec<ClassicalLayer>,
    quantum_parameters: Array1<f64>,
    hybrid_connections: Vec<HybridConnection>,
    data_dim: usize,
}

#[derive(Debug, Clone)]
pub struct QuantumDenoisingLayer {
    layer_type: DenoisingLayerType,
    num_qubits: usize,
    parameters: Array1<f64>,
    entanglement_pattern: EntanglementPattern,
    quantum_gates: Vec<QuantumGate>,
}

#[derive(Debug, Clone)]
pub enum DenoisingLayerType {
    QuantumConvolutional {
        kernel_size: usize,
        stride: usize,
        padding: usize,
    },
    QuantumSelfAttention {
        num_heads: usize,
        head_dim: usize,
    },
    QuantumCrossAttention {
        condition_dim: usize,
        num_heads: usize,
    },
    QuantumFeedForward {
        hidden_dim: usize,
        activation: QuantumActivation,
    },
    QuantumResidual {
        inner_layers: Vec<Box<QuantumDenoisingLayer>>,
    },
    QuantumPooling {
        pool_type: QuantumPoolingType,
        kernel_size: usize,
    },
}

#[derive(Debug, Clone)]
pub enum QuantumActivation {
    QuantumReLU,
    QuantumSigmoid,
    QuantumTanh,
    QuantumSoftmax,
    QuantumGELU,
    EntanglementActivation,
}

#[derive(Debug, Clone)]
pub enum QuantumPoolingType {
    MaxEntanglement,
    AverageCoherence,
    QuantumGlobal,
    AdaptiveQuantum,
}

#[derive(Debug, Clone)]
pub enum EntanglementPattern {
    Linear,
    Circular,
    AllToAll,
    Hierarchical { levels: usize },
    Adaptive { threshold: f64 },
    QuantumFourier,
}

#[derive(Debug, Clone)]
pub struct QuantumGate {
    gate_type: QuantumGateType,
    target_qubits: Vec<usize>,
    parameters: Array1<f64>,
    control_qubits: Vec<usize>,
}

#[derive(Debug, Clone)]
pub enum QuantumGateType {
    Rotation { axis: RotationAxis },
    Entangling { gate_name: String },
    Measurement { basis: MeasurementBasis },
    Conditional { condition: QuantumCondition },
    Parameterized { gate_name: String },
}

#[derive(Debug, Clone)]
pub enum RotationAxis {
    X,
    Y,
    Z,
    Arbitrary { direction: Array1<f64> },
}

#[derive(Debug, Clone)]
pub enum MeasurementBasis {
    Computational,
    PauliX,
    PauliY,
    PauliZ,
    Bell,
    Custom { basis_vectors: Array2<Complex64> },
}

#[derive(Debug, Clone)]
pub enum QuantumCondition {
    MeasurementOutcome { qubit: usize, outcome: bool },
    EntanglementThreshold { threshold: f64 },
    PhaseCondition { phase_threshold: f64 },
}

#[derive(Debug, Clone)]
pub struct ClassicalLayer {
    layer_type: ClassicalLayerType,
    parameters: Array2<f64>,
    activation: ClassicalActivation,
}

#[derive(Debug, Clone)]
pub enum ClassicalLayerType {
    Dense { input_dim: usize, output_dim: usize },
    Convolutional { channels: usize, kernel_size: usize },
    Normalization { epsilon: f64 },
    Dropout { rate: f64 },
}

#[derive(Debug, Clone)]
pub enum ClassicalActivation {
    ReLU,
    Sigmoid,
    Tanh,
    GELU,
    Swish,
    None,
}

#[derive(Debug, Clone)]
pub struct HybridConnection {
    quantum_layer_idx: usize,
    classical_layer_idx: usize,
    connection_type: HybridConnectionType,
    transformation_matrix: Array2<f64>,
}

#[derive(Debug, Clone)]
pub enum HybridConnectionType {
    MeasurementFeedback,
    ParameterControl,
    StateInjection,
    GradientCoupling,
}

#[derive(Debug, Clone)]
pub struct TrainingMetrics {
    pub epoch: usize,
    pub loss: f64,
    pub quantum_fidelity: f64,
    pub entanglement_measure: f64,
    pub denoising_accuracy: f64,
    pub quantum_advantage_ratio: f64,
    pub convergence_rate: f64,
    pub decoherence_impact: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumDiffusionMetrics {
    pub average_entanglement: f64,
    pub coherence_time: f64,
    pub quantum_volume_utilization: f64,
    pub circuit_depth_efficiency: f64,
    pub noise_resilience: f64,
    pub quantum_speedup_factor: f64,
    pub fidelity_preservation: f64,
}

#[derive(Debug, Clone)]
pub struct AdaptiveLearningState {
    pub learning_rate: f64,
    pub momentum: f64,
    pub adaptive_schedule_parameters: Array1<f64>,
    pub entanglement_decay_rate: f64,
    pub decoherence_compensation: f64,
    pub quantum_error_rate: f64,
}

impl QuantumAdvancedDiffusionModel {
    /// Create a new advanced quantum diffusion model
    pub fn new(config: QuantumAdvancedDiffusionConfig) -> Result<Self> {
        // Initialize quantum denoising network
        let quantum_denoiser = QuantumDenoisingNetwork::new(&config)?;

        // Compute advanced noise schedules
        let (betas, alphas, alphas_cumprod) = Self::compute_quantum_schedule(&config)?;

        // Initialize quantum-specific schedules
        let entanglement_schedule = Self::compute_entanglement_schedule(&config)?;
        let phase_schedule = Self::compute_phase_schedule(&config)?;
        let decoherence_factors = Self::compute_decoherence_factors(&config)?;

        // Initialize metrics and learning state
        let quantum_metrics = QuantumDiffusionMetrics::default();
        let adaptive_learning_state = AdaptiveLearningState::default();

        Ok(Self {
            config,
            quantum_denoiser,
            betas,
            alphas,
            alphas_cumprod,
            entanglement_schedule,
            phase_schedule,
            decoherence_factors,
            training_history: Vec::new(),
            quantum_metrics,
            adaptive_learning_state,
        })
    }

    /// Compute quantum-optimized noise schedule
    fn compute_quantum_schedule(
        config: &QuantumAdvancedDiffusionConfig,
    ) -> Result<(Array1<f64>, Array1<f64>, Array1<f64>)> {
        let num_timesteps = config.num_timesteps;
        let mut betas = Array1::zeros(num_timesteps);

        match &config.noise_schedule {
            QuantumNoiseSchedule::QuantumCosine {
                s,
                entanglement_preservation,
                decoherence_rate,
            } => {
                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)
                    };

                    // Quantum enhancement: preserve entanglement and account for decoherence
                    let quantum_factor =
                        entanglement_preservation * (-decoherence_rate * t as f64).exp();
                    betas[t] = (1.0 - f_t / f_t_prev) * quantum_factor;
                }
            }

            QuantumNoiseSchedule::QuantumExponential {
                lambda,
                t1_time,
                t2_time,
            } => {
                for t in 0..num_timesteps {
                    let time_factor = t as f64 / num_timesteps as f64;

                    // Account for T1 (amplitude damping) and T2 (dephasing) times
                    let t1_factor = (-time_factor / t1_time).exp();
                    let t2_factor = (-time_factor / t2_time).exp();
                    let quantum_decoherence = t1_factor * t2_factor;

                    betas[t] = lambda * time_factor * quantum_decoherence;
                }
            }

            QuantumNoiseSchedule::LearnedQuantumSchedule {
                parameters,
                circuit_depth_factor,
            } => {
                // Use learned parameters optimized for quantum circuits
                for t in 0..num_timesteps {
                    let normalized_t = t as f64 / num_timesteps as f64;
                    let param_idx = (normalized_t * (parameters.len() - 1) as f64) as usize;
                    let circuit_penalty = circuit_depth_factor * (t as f64).sqrt();
                    betas[t] = parameters[param_idx] * (1.0 + circuit_penalty);
                }
            }

            QuantumNoiseSchedule::PhaseSensitive {
                amplitude_schedule,
                phase_schedule: _,
            } => {
                for t in 0..num_timesteps {
                    let idx = t * amplitude_schedule.len() / num_timesteps;
                    betas[t] = amplitude_schedule[idx.min(amplitude_schedule.len() - 1)];
                }
            }

            QuantumNoiseSchedule::MultiScale {
                scales,
                weights,
                coherence_times,
            } => {
                for t in 0..num_timesteps {
                    let mut beta_t = 0.0;
                    for (i, &scale) in scales.iter().enumerate() {
                        let weight = weights[i.min(weights.len() - 1)];
                        let coherence_time = coherence_times[i.min(coherence_times.len() - 1)];
                        let time_factor = t as f64 / num_timesteps as f64;

                        // Multi-scale contribution with coherence decay
                        let scale_contribution =
                            scale * (2.0 * PI * time_factor / scale).sin().powi(2);
                        let coherence_factor = (-time_factor / coherence_time).exp();
                        beta_t += weight * scale_contribution * coherence_factor;
                    }
                    betas[t] = beta_t / scales.len() as f64;
                }
            }
        }

        // 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];
        }

        Ok((betas, alphas, alphas_cumprod))
    }

    /// Compute entanglement preservation schedule
    fn compute_entanglement_schedule(
        config: &QuantumAdvancedDiffusionConfig,
    ) -> Result<Array1<f64>> {
        let num_timesteps = config.num_timesteps;
        let mut schedule = Array1::zeros(num_timesteps);

        for t in 0..num_timesteps {
            let normalized_t = t as f64 / num_timesteps as f64;

            // Exponential decay of entanglement with quantum enhancement
            let base_decay = (-2.0 * normalized_t).exp();
            let quantum_enhancement = 1.0 + config.quantum_enhancement_level * (1.0 - normalized_t);
            schedule[t] = base_decay * quantum_enhancement;
        }

        Ok(schedule)
    }

    /// Compute phase evolution schedule for quantum interference
    fn compute_phase_schedule(
        config: &QuantumAdvancedDiffusionConfig,
    ) -> Result<Array1<Complex64>> {
        let num_timesteps = config.num_timesteps;
        let mut schedule = Array1::zeros(num_timesteps);

        for t in 0..num_timesteps {
            let normalized_t = t as f64 / num_timesteps as f64;

            // Complex phase evolution respecting quantum mechanics
            let phase =
                2.0 * PI * normalized_t + config.quantum_enhancement_level * normalized_t.sin();
            schedule[t] = Complex64::from_polar(1.0, phase);
        }

        Ok(schedule)
    }

    /// Compute decoherence compensation factors
    fn compute_decoherence_factors(config: &QuantumAdvancedDiffusionConfig) -> Result<Array1<f64>> {
        let num_timesteps = config.num_timesteps;
        let mut factors = Array1::zeros(num_timesteps);

        // Model realistic decoherence based on circuit depth and gate count
        let base_decoherence_rate = 0.001; // per gate operation
        let circuit_depth_per_timestep = 10.0; // estimated

        for t in 0..num_timesteps {
            let total_operations = circuit_depth_per_timestep * (t + 1) as f64;
            let decoherence_probability = 1.0 - (-base_decoherence_rate * total_operations).exp();
            factors[t] = 1.0 - decoherence_probability;
        }

        Ok(factors)
    }

    /// Advanced quantum forward diffusion with entanglement preservation
    pub fn quantum_forward_diffusion(
        &self,
        x0: &Array1<f64>,
        t: usize,
    ) -> Result<(Array1<f64>, Array1<Complex64>, QuantumState)> {
        if t >= self.config.num_timesteps {
            return Err(MLError::ModelCreationError("Invalid timestep".to_string()));
        }

        // Generate quantum-correlated noise
        let quantum_noise = self.generate_quantum_noise(t)?;

        // Apply quantum diffusion with entanglement preservation
        let alpha_cumprod = self.alphas_cumprod[t];
        let entanglement_factor = self.entanglement_schedule[t];
        let phase_factor = self.phase_schedule[t];

        // Quantum-enhanced forward process
        let sqrt_alpha_cumprod = alpha_cumprod.sqrt() * entanglement_factor;
        let sqrt_one_minus_alpha_cumprod = (1.0 - alpha_cumprod).sqrt();

        // Apply phase-sensitive transformation
        let mut xt = Array1::zeros(x0.len());
        for i in 0..x0.len() {
            let phase_corrected_noise = quantum_noise[i] * phase_factor;
            xt[i] = sqrt_alpha_cumprod * x0[i]
                + sqrt_one_minus_alpha_cumprod * phase_corrected_noise.re;
        }

        // Construct quantum state representation
        let quantum_state = QuantumState::new(xt.clone(), entanglement_factor, phase_factor)?;

        Ok((xt, quantum_noise, quantum_state))
    }

    /// Generate quantum-correlated noise with proper entanglement structure
    fn generate_quantum_noise(&self, t: usize) -> Result<Array1<Complex64>> {
        let mut rng = rand::thread_rng();
        let data_dim = self.config.data_dim;
        let mut noise = Array1::<Complex64>::zeros(data_dim);

        // Generate correlated quantum noise
        for i in 0..data_dim {
            // Box-Muller for real and imaginary parts
            let u1 = rng.gen::<f64>();
            let u2 = rng.gen::<f64>();
            let real_part = (-2.0f64 * u1.ln()).sqrt() * (2.0f64 * std::f64::consts::PI * u2).cos();
            let imaginary_part =
                (-2.0f64 * u1.ln()).sqrt() * (2.0f64 * std::f64::consts::PI * u2).sin();

            // Apply quantum correlations based on entanglement schedule
            let entanglement_strength = self.entanglement_schedule[t];
            let correlation_factor = if i > 0 {
                entanglement_strength * noise[i - 1].norm()
            } else {
                1.0
            };

            noise[i] = Complex64::new(
                real_part * correlation_factor,
                imaginary_part * entanglement_strength,
            );
        }

        Ok(noise)
    }

    /// Advanced quantum denoising with adaptive architecture
    pub fn quantum_denoise(
        &self,
        xt: &Array1<f64>,
        t: usize,
        condition: Option<&Array1<f64>>,
    ) -> Result<DenoiseOutput> {
        // Prepare input with timestep embedding and optional conditioning
        let input = self.prepare_denoising_input(xt, t, condition)?;

        // Apply quantum denoising network
        let denoised_output = self.quantum_denoiser.forward(&input, t)?;

        // Post-process with quantum error mitigation
        let mitigated_output = self.apply_error_mitigation(&denoised_output, t)?;

        // Compute denoising metrics
        let metrics = self.compute_denoising_metrics(&mitigated_output, t)?;

        Ok(DenoiseOutput {
            denoised_data: mitigated_output.predicted_noise,
            quantum_state: mitigated_output.quantum_state,
            confidence: mitigated_output.confidence,
            entanglement_measure: metrics.entanglement_measure,
            quantum_fidelity: metrics.quantum_fidelity,
        })
    }

    /// Prepare input for denoising network with advanced feature encoding
    fn prepare_denoising_input(
        &self,
        xt: &Array1<f64>,
        t: usize,
        condition: Option<&Array1<f64>>,
    ) -> Result<DenoisingInput> {
        let mut features = Vec::new();

        // Add main data
        features.extend_from_slice(xt.as_slice().unwrap());

        // Add timestep embedding with quantum features
        let timestep_embedding = self.compute_quantum_timestep_embedding(t)?;
        features.extend_from_slice(timestep_embedding.as_slice().unwrap());

        // Add conditioning if provided
        if let Some(cond) = condition {
            features.extend_from_slice(cond.as_slice().unwrap());
        }

        // Add quantum Fourier features if enabled
        if self.config.use_quantum_fourier_features {
            let fourier_features = self.compute_quantum_fourier_features(xt, t)?;
            features.extend_from_slice(fourier_features.as_slice().unwrap());
        }

        Ok(DenoisingInput {
            features: Array1::from_vec(features),
            timestep: t,
            quantum_phase: self.phase_schedule[t],
            entanglement_strength: self.entanglement_schedule[t],
        })
    }

    /// Compute quantum timestep embedding with phase information
    fn compute_quantum_timestep_embedding(&self, t: usize) -> Result<Array1<f64>> {
        let embedding_dim = 32; // Configurable
        let mut embedding = Array1::zeros(embedding_dim);

        let normalized_t = t as f64 / self.config.num_timesteps as f64;

        for i in 0..embedding_dim {
            let freq = 2.0_f64.powi(i as i32);
            let phase = self.phase_schedule[t].arg();

            if i % 2 == 0 {
                embedding[i] = (freq * normalized_t * PI + phase).sin();
            } else {
                embedding[i] = (freq * normalized_t * PI + phase).cos();
            }
        }

        Ok(embedding)
    }

    /// Compute quantum Fourier features for enhanced representation
    fn compute_quantum_fourier_features(&self, x: &Array1<f64>, t: usize) -> Result<Array1<f64>> {
        let num_features = 16; // Configurable
        let mut features = Array1::zeros(num_features);

        // Generate random Fourier features with quantum correlations
        let mut rng = rand::thread_rng();

        for i in 0..num_features {
            let frequency = rng.gen_range(0.1..10.0);
            let phase = rng.gen_range(0.0..2.0 * PI);

            // Apply quantum phase from schedule
            let quantum_phase = self.phase_schedule[t].arg();
            let total_phase = phase + quantum_phase;

            // Compute Fourier feature with quantum enhancement
            let feature_value = x
                .iter()
                .enumerate()
                .map(|(j, &x_j)| (frequency * x_j + total_phase).cos())
                .sum::<f64>()
                / x.len() as f64;

            features[i] = feature_value;
        }

        Ok(features)
    }

    /// Apply quantum error mitigation strategies
    fn apply_error_mitigation(
        &self,
        output: &RawDenoiseOutput,
        t: usize,
    ) -> Result<MitigatedDenoiseOutput> {
        match self.config.error_mitigation_strategy {
            ErrorMitigationStrategy::None => Ok(MitigatedDenoiseOutput {
                predicted_noise: output.predicted_noise.clone(),
                quantum_state: output.quantum_state.clone(),
                confidence: output.confidence,
            }),

            ErrorMitigationStrategy::ZeroNoiseExtrapolation => {
                self.apply_zero_noise_extrapolation(output, t)
            }

            ErrorMitigationStrategy::QuantumErrorSuppression => {
                self.apply_quantum_error_suppression(output, t)
            }

            ErrorMitigationStrategy::AdaptiveMitigation => {
                self.apply_adaptive_mitigation(output, t)
            }

            ErrorMitigationStrategy::TensorNetworkCorrection => {
                self.apply_tensor_network_correction(output, t)
            }
        }
    }

    /// Apply zero noise extrapolation for error mitigation
    fn apply_zero_noise_extrapolation(
        &self,
        output: &RawDenoiseOutput,
        t: usize,
    ) -> Result<MitigatedDenoiseOutput> {
        // Simulate measurements at different noise levels
        let noise_factors = vec![1.0, 1.5, 2.0];
        let mut extrapolated_output = output.predicted_noise.clone();

        // Simple linear extrapolation (in practice would be more sophisticated)
        for (i, &noise_factor) in noise_factors.iter().enumerate() {
            if i > 0 {
                let noise_scaling = 1.0 / noise_factor;
                extrapolated_output = &extrapolated_output * noise_scaling;
            }
        }

        Ok(MitigatedDenoiseOutput {
            predicted_noise: extrapolated_output,
            quantum_state: output.quantum_state.clone(),
            confidence: output.confidence * 0.95, // Slightly reduced due to extrapolation
        })
    }

    /// Apply quantum error suppression techniques
    fn apply_quantum_error_suppression(
        &self,
        output: &RawDenoiseOutput,
        t: usize,
    ) -> Result<MitigatedDenoiseOutput> {
        // Apply decoherence compensation
        let decoherence_factor = self.decoherence_factors[t];
        let compensated_output = &output.predicted_noise / decoherence_factor;

        Ok(MitigatedDenoiseOutput {
            predicted_noise: compensated_output,
            quantum_state: output.quantum_state.clone(),
            confidence: output.confidence * decoherence_factor,
        })
    }

    /// Apply adaptive error mitigation based on current quantum state
    fn apply_adaptive_mitigation(
        &self,
        output: &RawDenoiseOutput,
        t: usize,
    ) -> Result<MitigatedDenoiseOutput> {
        // Analyze quantum state to determine optimal mitigation strategy
        let entanglement_level = output.quantum_state.entanglement_measure;
        let coherence_level = output.quantum_state.coherence_time;

        // Choose mitigation strategy based on quantum metrics
        if entanglement_level > 0.7 && coherence_level > 0.5 {
            // High-quality quantum state: minimal mitigation
            self.apply_quantum_error_suppression(output, t)
        } else if entanglement_level < 0.3 {
            // Low entanglement: aggressive mitigation
            self.apply_zero_noise_extrapolation(output, t)
        } else {
            // Medium quality: balanced approach
            let suppressed = self.apply_quantum_error_suppression(output, t)?;
            let extrapolated = self.apply_zero_noise_extrapolation(output, t)?;

            // Weighted combination
            let weight = entanglement_level;
            let combined_output = weight * &suppressed.predicted_noise
                + (1.0 - weight) * &extrapolated.predicted_noise;

            Ok(MitigatedDenoiseOutput {
                predicted_noise: combined_output,
                quantum_state: output.quantum_state.clone(),
                confidence: weight * suppressed.confidence
                    + (1.0 - weight) * extrapolated.confidence,
            })
        }
    }

    /// Apply tensor network-based error correction
    fn apply_tensor_network_correction(
        &self,
        output: &RawDenoiseOutput,
        t: usize,
    ) -> Result<MitigatedDenoiseOutput> {
        // Placeholder for tensor network correction
        // Would decompose quantum state into tensor network and apply corrections
        Ok(MitigatedDenoiseOutput {
            predicted_noise: output.predicted_noise.clone(),
            quantum_state: output.quantum_state.clone(),
            confidence: output.confidence * 0.9,
        })
    }

    /// Compute comprehensive denoising metrics
    fn compute_denoising_metrics(
        &self,
        output: &MitigatedDenoiseOutput,
        t: usize,
    ) -> Result<DenoisingMetrics> {
        Ok(DenoisingMetrics {
            entanglement_measure: output.quantum_state.entanglement_measure,
            quantum_fidelity: output.quantum_state.fidelity,
            coherence_time: output.quantum_state.coherence_time,
            circuit_depth: t as f64 * 10.0, // Estimated
            noise_level: 1.0 - self.decoherence_factors[t],
            quantum_advantage: self.estimate_quantum_advantage(output, t)?,
        })
    }

    /// Estimate quantum advantage compared to classical methods
    fn estimate_quantum_advantage(&self, output: &MitigatedDenoiseOutput, t: usize) -> Result<f64> {
        // Simplified quantum advantage estimation
        let entanglement_bonus = output.quantum_state.entanglement_measure * 2.0;
        let coherence_bonus = output.quantum_state.coherence_time;
        let phase_bonus = output.quantum_state.quantum_phase.norm() * 0.5;

        Ok(1.0 + entanglement_bonus + coherence_bonus + phase_bonus)
    }

    /// Advanced reverse diffusion with quantum acceleration
    pub fn quantum_reverse_diffusion(
        &self,
        xt: &Array1<f64>,
        t: usize,
        guidance_scale: Option<f64>,
        condition: Option<&Array1<f64>>,
    ) -> Result<ReverseDiffusionOutput> {
        if t == 0 {
            return Ok(ReverseDiffusionOutput {
                xt_prev: xt.clone(),
                predicted_x0: xt.clone(),
                quantum_state: QuantumState::default(),
                step_metrics: StepMetrics::default(),
            });
        }

        // Quantum denoising with optional guidance
        let denoise_output = if let Some(scale) = guidance_scale {
            self.classifier_free_guidance_denoise(xt, t, scale, condition)?
        } else {
            self.quantum_denoise(xt, t, condition)?
        };

        // Compute reverse diffusion step
        let reverse_step = self.compute_quantum_reverse_step(xt, t, &denoise_output)?;

        // Apply quantum acceleration if beneficial
        let accelerated_step = if self.should_apply_quantum_acceleration(t)? {
            self.apply_quantum_acceleration(&reverse_step, t)?
        } else {
            reverse_step
        };

        Ok(accelerated_step)
    }

    /// Classifier-free guidance for conditional generation
    fn classifier_free_guidance_denoise(
        &self,
        xt: &Array1<f64>,
        t: usize,
        guidance_scale: f64,
        condition: Option<&Array1<f64>>,
    ) -> Result<DenoiseOutput> {
        // Conditional denoising
        let conditional_output = self.quantum_denoise(xt, t, condition)?;

        // Unconditional denoising
        let unconditional_output = self.quantum_denoise(xt, t, None)?;

        // Apply classifier-free guidance
        let guided_noise = &unconditional_output.denoised_data
            + guidance_scale
                * (&conditional_output.denoised_data - &unconditional_output.denoised_data);

        Ok(DenoiseOutput {
            denoised_data: guided_noise,
            quantum_state: conditional_output.quantum_state,
            confidence: conditional_output.confidence,
            entanglement_measure: conditional_output.entanglement_measure,
            quantum_fidelity: conditional_output.quantum_fidelity,
        })
    }

    /// Compute quantum-enhanced reverse diffusion step
    fn compute_quantum_reverse_step(
        &self,
        xt: &Array1<f64>,
        t: usize,
        denoise_output: &DenoiseOutput,
    ) -> Result<ReverseDiffusionOutput> {
        // Standard DDPM reverse step with quantum enhancements
        let beta_t = self.betas[t];
        let alpha_t = self.alphas[t];
        let alpha_cumprod_t = self.alphas_cumprod[t];
        let alpha_cumprod_prev = if t > 0 {
            self.alphas_cumprod[t - 1]
        } else {
            1.0
        };

        // Quantum-enhanced coefficients
        let entanglement_factor = self.entanglement_schedule[t];
        let phase_factor = self.phase_schedule[t];

        // Predicted x0 with quantum corrections
        // Note: denoised_data actually contains predicted noise, not denoised data
        let sqrt_alpha_cumprod_t = alpha_cumprod_t.sqrt();
        let sqrt_one_minus_alpha_cumprod = (1.0 - alpha_cumprod_t).sqrt();

        let predicted_x0 = (xt - sqrt_one_minus_alpha_cumprod * &denoise_output.denoised_data)
            / sqrt_alpha_cumprod_t;

        // Mean of reverse process
        let sqrt_alpha_cumprod_prev = alpha_cumprod_prev.sqrt() * entanglement_factor;
        let sqrt_one_minus_alpha_cumprod_prev = (1.0 - alpha_cumprod_prev).sqrt();

        let coeff1 = beta_t * sqrt_alpha_cumprod_prev / (1.0 - alpha_cumprod_t);
        let coeff2 = (1.0 - alpha_cumprod_prev) * alpha_t.sqrt() / (1.0 - alpha_cumprod_t);

        let mean = coeff1 * &predicted_x0 + coeff2 * xt;

        // Add quantum-correlated noise for t > 1
        let xt_prev = if t > 1 {
            let variance = beta_t * (1.0 - alpha_cumprod_prev) / (1.0 - alpha_cumprod_t);
            let std_dev = variance.sqrt() * entanglement_factor;

            // Generate quantum-correlated noise
            let noise = self.generate_quantum_noise(t - 1)?;
            let real_noise = noise.mapv(|x| x.re);

            &mean + std_dev * &real_noise
        } else {
            mean
        };

        Ok(ReverseDiffusionOutput {
            xt_prev,
            predicted_x0,
            quantum_state: denoise_output.quantum_state.clone(),
            step_metrics: StepMetrics {
                entanglement_preservation: entanglement_factor,
                phase_coherence: phase_factor.norm(),
                denoising_confidence: denoise_output.confidence,
                quantum_advantage: denoise_output.quantum_fidelity,
            },
        })
    }

    /// Determine if quantum acceleration should be applied
    fn should_apply_quantum_acceleration(&self, t: usize) -> Result<bool> {
        // Apply acceleration when quantum state quality is high
        let entanglement_threshold = 0.6;
        let coherence_threshold = 0.5;

        let current_entanglement = self.entanglement_schedule[t];
        let current_coherence = self.decoherence_factors[t];

        Ok(
            current_entanglement > entanglement_threshold
                && current_coherence > coherence_threshold,
        )
    }

    /// Apply quantum acceleration to diffusion step
    fn apply_quantum_acceleration(
        &self,
        step: &ReverseDiffusionOutput,
        t: usize,
    ) -> Result<ReverseDiffusionOutput> {
        // Quantum-inspired acceleration using entanglement correlations
        let acceleration_factor = 1.0 + self.entanglement_schedule[t] * 0.5;

        // Accelerate the step while preserving quantum properties
        let accelerated_xt_prev = &step.xt_prev * acceleration_factor;
        let accelerated_x0 = &step.predicted_x0 * acceleration_factor;

        Ok(ReverseDiffusionOutput {
            xt_prev: accelerated_xt_prev,
            predicted_x0: accelerated_x0,
            quantum_state: step.quantum_state.clone(),
            step_metrics: StepMetrics {
                entanglement_preservation: step.step_metrics.entanglement_preservation
                    * acceleration_factor,
                phase_coherence: step.step_metrics.phase_coherence,
                denoising_confidence: step.step_metrics.denoising_confidence,
                quantum_advantage: step.step_metrics.quantum_advantage * acceleration_factor,
            },
        })
    }

    /// Generate samples using advanced quantum diffusion
    pub fn quantum_generate(
        &self,
        num_samples: usize,
        condition: Option<&Array2<f64>>,
        guidance_scale: Option<f64>,
    ) -> Result<QuantumGenerationOutput> {
        let mut samples = Array2::zeros((num_samples, self.config.data_dim));
        let mut generation_metrics = Vec::new();

        for sample_idx in 0..num_samples {
            let sample_condition = condition.map(|c| c.row(sample_idx).to_owned());

            // Start from quantum noise
            let mut xt = self.generate_initial_quantum_noise()?;
            let mut step_metrics = Vec::new();

            // Reverse diffusion with quantum enhancements
            for t in (0..self.config.num_timesteps).rev() {
                let reverse_output = self.quantum_reverse_diffusion(
                    &xt,
                    t,
                    guidance_scale,
                    sample_condition.as_ref(),
                )?;

                xt = reverse_output.xt_prev;
                step_metrics.push(reverse_output.step_metrics);
            }

            samples.row_mut(sample_idx).assign(&xt);
            generation_metrics.push(GenerationMetrics {
                sample_idx,
                final_entanglement: step_metrics.last().unwrap().entanglement_preservation,
                average_confidence: step_metrics
                    .iter()
                    .map(|m| m.denoising_confidence)
                    .sum::<f64>()
                    / step_metrics.len() as f64,
                quantum_advantage: step_metrics
                    .iter()
                    .map(|m| m.quantum_advantage)
                    .sum::<f64>()
                    / step_metrics.len() as f64,
                step_metrics,
            });
        }

        Ok(QuantumGenerationOutput {
            samples,
            generation_metrics,
            overall_quantum_metrics: self.quantum_metrics.clone(),
        })
    }

    /// Generate initial quantum noise with proper entanglement structure
    fn generate_initial_quantum_noise(&self) -> Result<Array1<f64>> {
        let noise = self.generate_quantum_noise(self.config.num_timesteps - 1)?;
        Ok(noise.mapv(|x| x.re))
    }

    /// Train the advanced quantum diffusion model
    pub fn train(
        &mut self,
        data: &Array2<f64>,
        validation_data: Option<&Array2<f64>>,
        training_config: &QuantumTrainingConfig,
    ) -> Result<QuantumTrainingOutput> {
        let mut training_losses = Vec::new();
        let mut validation_losses = Vec::new();
        let mut quantum_metrics_history = Vec::new();

        println!("🚀 Starting Advanced Quantum Diffusion Training in UltraThink Mode");

        for epoch in 0..training_config.epochs {
            let epoch_metrics = self.train_epoch(data, training_config, epoch)?;
            training_losses.push(epoch_metrics.loss);

            // Validation
            if let Some(val_data) = validation_data {
                let val_metrics = self.validate_epoch(val_data, training_config)?;
                validation_losses.push(val_metrics.loss);
            }

            // Update quantum metrics
            self.update_quantum_metrics(&epoch_metrics)?;
            quantum_metrics_history.push(self.quantum_metrics.clone());

            // Adaptive learning rate and quantum parameter updates
            self.update_adaptive_learning_state(&epoch_metrics)?;

            // Logging
            if epoch % training_config.log_interval == 0 {
                println!(
                    "Epoch {}: Loss = {:.6}, Quantum Fidelity = {:.4}, Entanglement = {:.4}, Quantum Advantage = {:.2}x",
                    epoch,
                    epoch_metrics.loss,
                    epoch_metrics.quantum_fidelity,
                    epoch_metrics.entanglement_measure,
                    epoch_metrics.quantum_advantage_ratio,
                );
            }
        }

        Ok(QuantumTrainingOutput {
            training_losses: training_losses.clone(),
            validation_losses,
            quantum_metrics_history,
            final_model_state: self.export_model_state()?,
            convergence_analysis: self.analyze_convergence(&training_losses)?,
        })
    }

    /// Train single epoch with quantum enhancements
    fn train_epoch(
        &mut self,
        data: &Array2<f64>,
        config: &QuantumTrainingConfig,
        epoch: usize,
    ) -> Result<TrainingMetrics> {
        let mut epoch_loss = 0.0;
        let mut quantum_fidelity_sum = 0.0;
        let mut entanglement_sum = 0.0;
        let mut quantum_advantage_sum = 0.0;
        let mut num_batches = 0;

        let num_samples = data.nrows();

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

            let batch_metrics = self.train_batch(&batch_data, config)?;

            epoch_loss += batch_metrics.loss;
            quantum_fidelity_sum += batch_metrics.quantum_fidelity;
            entanglement_sum += batch_metrics.entanglement_measure;
            quantum_advantage_sum += batch_metrics.quantum_advantage_ratio;
            num_batches += 1;
        }

        Ok(TrainingMetrics {
            epoch,
            loss: epoch_loss / num_batches as f64,
            quantum_fidelity: quantum_fidelity_sum / num_batches as f64,
            entanglement_measure: entanglement_sum / num_batches as f64,
            denoising_accuracy: 0.0, // Will be computed separately
            quantum_advantage_ratio: quantum_advantage_sum / num_batches as f64,
            convergence_rate: 0.0, // Will be computed from loss history
            decoherence_impact: 1.0 - self.quantum_metrics.noise_resilience,
        })
    }

    /// Train single batch with quantum loss computation
    fn train_batch(
        &mut self,
        batch_data: &ndarray::ArrayView2<f64>,
        config: &QuantumTrainingConfig,
    ) -> Result<TrainingMetrics> {
        let mut batch_loss = 0.0;
        let mut quantum_metrics_sum = QuantumBatchMetrics::default();

        for sample_idx in 0..batch_data.nrows() {
            let x0 = batch_data.row(sample_idx).to_owned();

            // Random timestep
            let mut rng = rand::thread_rng();
            let t = rng.gen_range(0..self.config.num_timesteps);

            // Forward diffusion with quantum noise
            let (xt, quantum_noise, quantum_state) = self.quantum_forward_diffusion(&x0, t)?;

            // Predict noise using quantum denoising
            let denoise_output = self.quantum_denoise(&xt, t, None)?;

            // Compute quantum loss
            let loss_output = self.compute_quantum_loss(
                &quantum_noise.mapv(|x| x.re),
                &denoise_output.denoised_data,
                &quantum_state,
                t,
            )?;

            batch_loss += loss_output.total_loss;
            quantum_metrics_sum.accumulate(&loss_output.quantum_metrics);

            // Backward pass and parameter update (placeholder)
            self.update_parameters(&loss_output, config)?;
        }

        let num_samples = batch_data.nrows() as f64;
        Ok(TrainingMetrics {
            epoch: 0, // Will be set by caller
            loss: batch_loss / num_samples,
            quantum_fidelity: quantum_metrics_sum.quantum_fidelity / num_samples,
            entanglement_measure: quantum_metrics_sum.entanglement_measure / num_samples,
            denoising_accuracy: quantum_metrics_sum.denoising_accuracy / num_samples,
            quantum_advantage_ratio: quantum_metrics_sum.quantum_advantage_ratio / num_samples,
            convergence_rate: 0.0,
            decoherence_impact: quantum_metrics_sum.decoherence_impact / num_samples,
        })
    }

    /// Compute quantum-enhanced loss function
    fn compute_quantum_loss(
        &self,
        true_noise: &Array1<f64>,
        predicted_noise: &Array1<f64>,
        quantum_state: &QuantumState,
        t: usize,
    ) -> Result<QuantumLossOutput> {
        // Base denoising loss (MSE)
        let noise_diff = predicted_noise - true_noise;
        let mse_loss = noise_diff.mapv(|x| x * x).sum() / true_noise.len() as f64;

        // Quantum fidelity loss
        let fidelity_loss = 1.0 - quantum_state.fidelity;

        // Entanglement preservation loss
        let target_entanglement = self.entanglement_schedule[t];
        let entanglement_loss = (quantum_state.entanglement_measure - target_entanglement).powi(2);

        // Phase coherence loss
        let phase_coherence_loss = 1.0 - quantum_state.quantum_phase.norm();

        // Decoherence penalty
        let decoherence_penalty =
            (1.0 - self.decoherence_factors[t]) * quantum_state.coherence_time;

        // Combined quantum loss
        let quantum_loss_weight = 0.1; // Configurable
        let total_loss = mse_loss
            + quantum_loss_weight
                * (fidelity_loss + entanglement_loss + phase_coherence_loss + decoherence_penalty);

        Ok(QuantumLossOutput {
            total_loss,
            mse_loss,
            fidelity_loss,
            entanglement_loss,
            phase_coherence_loss,
            decoherence_penalty,
            quantum_metrics: QuantumBatchMetrics {
                quantum_fidelity: quantum_state.fidelity,
                entanglement_measure: quantum_state.entanglement_measure,
                denoising_accuracy: 1.0 / (1.0 + mse_loss), // Approximate accuracy
                quantum_advantage_ratio: self.estimate_quantum_advantage_ratio(quantum_state, t)?,
                decoherence_impact: 1.0 - self.decoherence_factors[t],
            },
        })
    }

    /// Estimate quantum advantage ratio for current state
    fn estimate_quantum_advantage_ratio(
        &self,
        quantum_state: &QuantumState,
        t: usize,
    ) -> Result<f64> {
        // Simplified quantum advantage estimation
        let entanglement_advantage = quantum_state.entanglement_measure * 2.0;
        let coherence_advantage = quantum_state.coherence_time * 1.5;
        let phase_advantage = quantum_state.quantum_phase.norm();

        Ok(1.0 + entanglement_advantage + coherence_advantage + phase_advantage)
    }

    /// Update model parameters using quantum gradients
    fn update_parameters(
        &mut self,
        loss_output: &QuantumLossOutput,
        config: &QuantumTrainingConfig,
    ) -> Result<()> {
        // Placeholder for quantum parameter update
        // In practice, would compute quantum gradients and apply optimization

        // Update adaptive learning state
        self.adaptive_learning_state.learning_rate *= config.learning_rate_decay;

        Ok(())
    }

    /// Validate model on validation data
    fn validate_epoch(
        &self,
        validation_data: &Array2<f64>,
        config: &QuantumTrainingConfig,
    ) -> Result<TrainingMetrics> {
        // Similar to train_epoch but without parameter updates
        let mut val_loss = 0.0;
        let mut quantum_fidelity_sum = 0.0;
        let mut entanglement_sum = 0.0;
        let mut num_samples = 0;

        for sample_idx in 0..validation_data.nrows() {
            let x0 = validation_data.row(sample_idx).to_owned();

            let mut rng = rand::thread_rng();
            let t = rng.gen_range(0..self.config.num_timesteps);

            let (xt, quantum_noise, quantum_state) = self.quantum_forward_diffusion(&x0, t)?;
            let denoise_output = self.quantum_denoise(&xt, t, None)?;

            let loss_output = self.compute_quantum_loss(
                &quantum_noise.mapv(|x| x.re),
                &denoise_output.denoised_data,
                &quantum_state,
                t,
            )?;

            val_loss += loss_output.total_loss;
            quantum_fidelity_sum += loss_output.quantum_metrics.quantum_fidelity;
            entanglement_sum += loss_output.quantum_metrics.entanglement_measure;
            num_samples += 1;
        }

        Ok(TrainingMetrics {
            epoch: 0,
            loss: val_loss / num_samples as f64,
            quantum_fidelity: quantum_fidelity_sum / num_samples as f64,
            entanglement_measure: entanglement_sum / num_samples as f64,
            denoising_accuracy: 0.0,
            quantum_advantage_ratio: 1.0,
            convergence_rate: 0.0,
            decoherence_impact: 0.0,
        })
    }

    /// Update quantum metrics tracking
    fn update_quantum_metrics(&mut self, epoch_metrics: &TrainingMetrics) -> Result<()> {
        self.quantum_metrics.average_entanglement = 0.9 * self.quantum_metrics.average_entanglement
            + 0.1 * epoch_metrics.entanglement_measure;

        self.quantum_metrics.fidelity_preservation =
            0.9 * self.quantum_metrics.fidelity_preservation + 0.1 * epoch_metrics.quantum_fidelity;

        self.quantum_metrics.quantum_speedup_factor = epoch_metrics.quantum_advantage_ratio;

        Ok(())
    }

    /// Update adaptive learning state based on quantum metrics
    fn update_adaptive_learning_state(&mut self, epoch_metrics: &TrainingMetrics) -> Result<()> {
        // Adapt learning rate based on quantum state quality
        if epoch_metrics.entanglement_measure < 0.3 {
            self.adaptive_learning_state.learning_rate *= 0.95; // Slow down if losing entanglement
        } else if epoch_metrics.quantum_fidelity > 0.8 {
            self.adaptive_learning_state.learning_rate *= 1.05; // Speed up if high fidelity
        }

        // Update decoherence compensation
        self.adaptive_learning_state.decoherence_compensation =
            1.0 - epoch_metrics.decoherence_impact;

        Ok(())
    }

    /// Export model state for checkpointing
    fn export_model_state(&self) -> Result<ModelState> {
        Ok(ModelState {
            config: self.config.clone(),
            quantum_parameters: self.quantum_denoiser.quantum_parameters.clone(),
            noise_schedule_parameters: self.betas.clone(),
            quantum_metrics: self.quantum_metrics.clone(),
            adaptive_state: self.adaptive_learning_state.clone(),
        })
    }

    /// Analyze convergence behavior
    fn analyze_convergence(&self, losses: &[f64]) -> Result<ConvergenceAnalysis> {
        if losses.len() < 10 {
            return Ok(ConvergenceAnalysis::default());
        }

        // Compute convergence rate
        let recent_losses = &losses[losses.len() - 10..];
        let early_losses = &losses[0..10];

        let recent_avg = recent_losses.iter().sum::<f64>() / recent_losses.len() as f64;
        let early_avg = early_losses.iter().sum::<f64>() / early_losses.len() as f64;

        let convergence_rate = (early_avg - recent_avg) / early_avg;

        // Detect if converged
        let variance = recent_losses
            .iter()
            .map(|&x| (x - recent_avg).powi(2))
            .sum::<f64>()
            / recent_losses.len() as f64;
        let is_converged = variance < 1e-6;

        Ok(ConvergenceAnalysis {
            convergence_rate,
            is_converged,
            final_loss: recent_avg,
            loss_variance: variance,
            epochs_to_convergence: if is_converged {
                Some(losses.len())
            } else {
                None
            },
        })
    }

    /// Get current quantum metrics
    pub fn quantum_metrics(&self) -> &QuantumDiffusionMetrics {
        &self.quantum_metrics
    }

    /// Get training history
    pub fn training_history(&self) -> &[TrainingMetrics] {
        &self.training_history
    }
}

// Supporting structures and implementations

impl QuantumDenoisingNetwork {
    pub fn new(config: &QuantumAdvancedDiffusionConfig) -> Result<Self> {
        // Initialize based on architecture
        let quantum_layers = Self::create_quantum_layers(config)?;
        let classical_layers = Self::create_classical_layers(config)?;
        let quantum_parameters = Array1::zeros(1000); // Placeholder size
        let hybrid_connections = Self::create_hybrid_connections(config)?;

        Ok(Self {
            architecture: config.denoiser_architecture.clone(),
            quantum_layers,
            classical_layers,
            quantum_parameters,
            hybrid_connections,
            data_dim: config.data_dim,
        })
    }

    fn create_quantum_layers(
        config: &QuantumAdvancedDiffusionConfig,
    ) -> Result<Vec<QuantumDenoisingLayer>> {
        // Create layers based on architecture
        let mut layers = Vec::new();

        match &config.denoiser_architecture {
            DenoisingArchitecture::QuantumUNet {
                depth,
                base_channels,
                ..
            } => {
                for i in 0..*depth {
                    layers.push(QuantumDenoisingLayer {
                        layer_type: DenoisingLayerType::QuantumConvolutional {
                            kernel_size: 3,
                            stride: 1,
                            padding: 1,
                        },
                        num_qubits: config.num_qubits,
                        parameters: Array1::zeros(config.num_qubits * 3),
                        entanglement_pattern: EntanglementPattern::Circular,
                        quantum_gates: Vec::new(),
                    });
                }
            }
            _ => {
                // Default layer
                layers.push(QuantumDenoisingLayer {
                    layer_type: DenoisingLayerType::QuantumFeedForward {
                        hidden_dim: 64,
                        activation: QuantumActivation::QuantumReLU,
                    },
                    num_qubits: config.num_qubits,
                    parameters: Array1::zeros(config.num_qubits * 3),
                    entanglement_pattern: EntanglementPattern::Linear,
                    quantum_gates: Vec::new(),
                });
            }
        }

        Ok(layers)
    }

    fn create_classical_layers(
        config: &QuantumAdvancedDiffusionConfig,
    ) -> Result<Vec<ClassicalLayer>> {
        // Create classical layers for hybrid processing
        Ok(vec![ClassicalLayer {
            layer_type: ClassicalLayerType::Dense {
                input_dim: config.data_dim,
                output_dim: 64,
            },
            parameters: Array2::zeros((config.data_dim, 64)),
            activation: ClassicalActivation::ReLU,
        }])
    }

    fn create_hybrid_connections(
        config: &QuantumAdvancedDiffusionConfig,
    ) -> Result<Vec<HybridConnection>> {
        // Create connections between quantum and classical layers
        Ok(vec![HybridConnection {
            quantum_layer_idx: 0,
            classical_layer_idx: 0,
            connection_type: HybridConnectionType::MeasurementFeedback,
            transformation_matrix: Array2::eye(config.data_dim),
        }])
    }

    pub fn forward(&self, input: &DenoisingInput, t: usize) -> Result<RawDenoiseOutput> {
        // Forward pass through quantum denoising network
        let mut quantum_state = QuantumState::from_classical(&input.features)?;

        // Process through quantum layers
        for layer in &self.quantum_layers {
            quantum_state = self.process_quantum_layer(layer, &quantum_state, t)?;
        }

        // Extract prediction from quantum state
        let predicted_noise = self.extract_prediction(&quantum_state)?;

        Ok(RawDenoiseOutput {
            predicted_noise,
            quantum_state: quantum_state.clone(),
            confidence: quantum_state.fidelity,
        })
    }

    fn process_quantum_layer(
        &self,
        layer: &QuantumDenoisingLayer,
        quantum_state: &QuantumState,
        t: usize,
    ) -> Result<QuantumState> {
        // Process quantum state through layer
        match &layer.layer_type {
            DenoisingLayerType::QuantumFeedForward {
                hidden_dim,
                activation,
            } => self.apply_quantum_feedforward(quantum_state, *hidden_dim, activation, t),
            DenoisingLayerType::QuantumSelfAttention {
                num_heads,
                head_dim,
            } => self.apply_quantum_self_attention(quantum_state, *num_heads, *head_dim, t),
            _ => Ok(quantum_state.clone()), // Placeholder for other layer types
        }
    }

    fn apply_quantum_feedforward(
        &self,
        quantum_state: &QuantumState,
        hidden_dim: usize,
        activation: &QuantumActivation,
        t: usize,
    ) -> Result<QuantumState> {
        // Apply quantum feedforward transformation
        let mut new_state = quantum_state.clone();

        // Apply quantum gates (simplified)
        match activation {
            QuantumActivation::QuantumReLU => {
                // Apply ReLU-like quantum operation
                new_state.entanglement_measure *= 0.9; // Slightly reduce entanglement
            }
            _ => {
                // Default processing
                new_state.fidelity *= 0.95;
            }
        }

        Ok(new_state)
    }

    fn apply_quantum_self_attention(
        &self,
        quantum_state: &QuantumState,
        num_heads: usize,
        head_dim: usize,
        t: usize,
    ) -> Result<QuantumState> {
        // Apply quantum self-attention
        let mut new_state = quantum_state.clone();

        // Enhance entanglement through attention mechanism
        new_state.entanglement_measure = (new_state.entanglement_measure * 1.1).min(1.0);

        Ok(new_state)
    }

    fn extract_prediction(&self, quantum_state: &QuantumState) -> Result<Array1<f64>> {
        // Extract noise prediction from quantum state
        // This would involve quantum measurements in practice
        // Return only the first portion corresponding to original data dimension
        let data_dim = self.data_dim;

        if quantum_state.classical_data.len() >= data_dim {
            Ok(quantum_state
                .classical_data
                .slice(s![..data_dim])
                .to_owned())
        } else {
            Ok(quantum_state.classical_data.clone())
        }
    }
}

#[derive(Debug, Clone)]
pub struct QuantumState {
    pub classical_data: Array1<f64>,
    pub quantum_phase: Complex64,
    pub entanglement_measure: f64,
    pub coherence_time: f64,
    pub fidelity: f64,
}

impl QuantumState {
    pub fn new(data: Array1<f64>, entanglement: f64, phase: Complex64) -> Result<Self> {
        Ok(Self {
            classical_data: data,
            quantum_phase: phase,
            entanglement_measure: entanglement,
            coherence_time: 1.0, // Default value
            fidelity: 1.0,       // Default value
        })
    }

    pub fn from_classical(data: &Array1<f64>) -> Result<Self> {
        Ok(Self {
            classical_data: data.clone(),
            quantum_phase: Complex64::new(1.0, 0.0),
            entanglement_measure: 0.5,
            coherence_time: 1.0,
            fidelity: 1.0,
        })
    }
}

impl Default for QuantumState {
    fn default() -> Self {
        Self {
            classical_data: Array1::zeros(1),
            quantum_phase: Complex64::new(1.0, 0.0),
            entanglement_measure: 0.0,
            coherence_time: 0.0,
            fidelity: 0.0,
        }
    }
}

// Training configuration and output structures
#[derive(Debug, Clone)]
pub struct QuantumTrainingConfig {
    pub epochs: usize,
    pub batch_size: usize,
    pub learning_rate: f64,
    pub learning_rate_decay: f64,
    pub log_interval: usize,
    pub save_interval: usize,
    pub early_stopping_patience: usize,
    pub quantum_loss_weight: f64,
    pub entanglement_preservation_weight: f64,
}

impl Default for QuantumTrainingConfig {
    fn default() -> Self {
        Self {
            epochs: 100,
            batch_size: 32,
            learning_rate: 1e-4,
            learning_rate_decay: 0.99,
            log_interval: 10,
            save_interval: 50,
            early_stopping_patience: 20,
            quantum_loss_weight: 0.1,
            entanglement_preservation_weight: 0.05,
        }
    }
}

// Additional supporting structures (implementations would be similar)
#[derive(Debug, Clone)]
pub struct DenoisingInput {
    pub features: Array1<f64>,
    pub timestep: usize,
    pub quantum_phase: Complex64,
    pub entanglement_strength: f64,
}

#[derive(Debug, Clone)]
pub struct RawDenoiseOutput {
    pub predicted_noise: Array1<f64>,
    pub quantum_state: QuantumState,
    pub confidence: f64,
}

#[derive(Debug, Clone)]
pub struct MitigatedDenoiseOutput {
    pub predicted_noise: Array1<f64>,
    pub quantum_state: QuantumState,
    pub confidence: f64,
}

#[derive(Debug, Clone)]
pub struct DenoiseOutput {
    pub denoised_data: Array1<f64>,
    pub quantum_state: QuantumState,
    pub confidence: f64,
    pub entanglement_measure: f64,
    pub quantum_fidelity: f64,
}

#[derive(Debug, Clone)]
pub struct DenoisingMetrics {
    pub entanglement_measure: f64,
    pub quantum_fidelity: f64,
    pub coherence_time: f64,
    pub circuit_depth: f64,
    pub noise_level: f64,
    pub quantum_advantage: f64,
}

#[derive(Debug, Clone)]
pub struct ReverseDiffusionOutput {
    pub xt_prev: Array1<f64>,
    pub predicted_x0: Array1<f64>,
    pub quantum_state: QuantumState,
    pub step_metrics: StepMetrics,
}

#[derive(Debug, Clone)]
pub struct StepMetrics {
    pub entanglement_preservation: f64,
    pub phase_coherence: f64,
    pub denoising_confidence: f64,
    pub quantum_advantage: f64,
}

impl Default for StepMetrics {
    fn default() -> Self {
        Self {
            entanglement_preservation: 0.0,
            phase_coherence: 0.0,
            denoising_confidence: 0.0,
            quantum_advantage: 1.0,
        }
    }
}

#[derive(Debug, Clone)]
pub struct GenerationMetrics {
    pub sample_idx: usize,
    pub final_entanglement: f64,
    pub average_confidence: f64,
    pub quantum_advantage: f64,
    pub step_metrics: Vec<StepMetrics>,
}

#[derive(Debug, Clone)]
pub struct QuantumGenerationOutput {
    pub samples: Array2<f64>,
    pub generation_metrics: Vec<GenerationMetrics>,
    pub overall_quantum_metrics: QuantumDiffusionMetrics,
}

#[derive(Debug, Clone)]
pub struct QuantumLossOutput {
    pub total_loss: f64,
    pub mse_loss: f64,
    pub fidelity_loss: f64,
    pub entanglement_loss: f64,
    pub phase_coherence_loss: f64,
    pub decoherence_penalty: f64,
    pub quantum_metrics: QuantumBatchMetrics,
}

#[derive(Debug, Clone, Default)]
pub struct QuantumBatchMetrics {
    pub quantum_fidelity: f64,
    pub entanglement_measure: f64,
    pub denoising_accuracy: f64,
    pub quantum_advantage_ratio: f64,
    pub decoherence_impact: f64,
}

impl QuantumBatchMetrics {
    pub fn accumulate(&mut self, other: &QuantumBatchMetrics) {
        self.quantum_fidelity += other.quantum_fidelity;
        self.entanglement_measure += other.entanglement_measure;
        self.denoising_accuracy += other.denoising_accuracy;
        self.quantum_advantage_ratio += other.quantum_advantage_ratio;
        self.decoherence_impact += other.decoherence_impact;
    }
}

#[derive(Debug, Clone)]
pub struct QuantumTrainingOutput {
    pub training_losses: Vec<f64>,
    pub validation_losses: Vec<f64>,
    pub quantum_metrics_history: Vec<QuantumDiffusionMetrics>,
    pub final_model_state: ModelState,
    pub convergence_analysis: ConvergenceAnalysis,
}

#[derive(Debug, Clone)]
pub struct ModelState {
    pub config: QuantumAdvancedDiffusionConfig,
    pub quantum_parameters: Array1<f64>,
    pub noise_schedule_parameters: Array1<f64>,
    pub quantum_metrics: QuantumDiffusionMetrics,
    pub adaptive_state: AdaptiveLearningState,
}

#[derive(Debug, Clone, Default)]
pub struct ConvergenceAnalysis {
    pub convergence_rate: f64,
    pub is_converged: bool,
    pub final_loss: f64,
    pub loss_variance: f64,
    pub epochs_to_convergence: Option<usize>,
}

// Default implementations
impl Default for QuantumDiffusionMetrics {
    fn default() -> Self {
        Self {
            average_entanglement: 0.5,
            coherence_time: 1.0,
            quantum_volume_utilization: 0.0,
            circuit_depth_efficiency: 1.0,
            noise_resilience: 0.9,
            quantum_speedup_factor: 1.0,
            fidelity_preservation: 1.0,
        }
    }
}

impl Default for AdaptiveLearningState {
    fn default() -> Self {
        Self {
            learning_rate: 1e-4,
            momentum: 0.9,
            adaptive_schedule_parameters: Array1::zeros(10),
            entanglement_decay_rate: 0.01,
            decoherence_compensation: 1.0,
            quantum_error_rate: 0.001,
        }
    }
}

impl Default for QuantumAdvancedDiffusionConfig {
    fn default() -> Self {
        Self {
            data_dim: 32,
            num_qubits: 16,
            num_timesteps: 1000,
            noise_schedule: QuantumNoiseSchedule::QuantumCosine {
                s: 0.008,
                entanglement_preservation: 0.9,
                decoherence_rate: 0.01,
            },
            denoiser_architecture: DenoisingArchitecture::QuantumUNet {
                depth: 4,
                base_channels: 32,
                quantum_skip_connections: true,
            },
            quantum_enhancement_level: 0.5,
            use_quantum_attention: true,
            enable_entanglement_monitoring: true,
            adaptive_denoising: true,
            use_quantum_fourier_features: true,
            error_mitigation_strategy: ErrorMitigationStrategy::AdaptiveMitigation,
        }
    }
}

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

    #[test]
    fn test_quantum_advanced_diffusion_creation() {
        let config = QuantumAdvancedDiffusionConfig::default();
        let model = QuantumAdvancedDiffusionModel::new(config);
        assert!(model.is_ok());
    }

    #[test]
    fn test_quantum_noise_schedule() {
        let config = QuantumAdvancedDiffusionConfig::default();
        let (betas, alphas, alphas_cumprod) =
            QuantumAdvancedDiffusionModel::compute_quantum_schedule(&config).unwrap();

        assert_eq!(betas.len(), config.num_timesteps);
        assert_eq!(alphas.len(), config.num_timesteps);
        assert_eq!(alphas_cumprod.len(), config.num_timesteps);

        // Check that alphas_cumprod is decreasing
        for i in 1..alphas_cumprod.len() {
            assert!(alphas_cumprod[i] <= alphas_cumprod[i - 1]);
        }
    }

    #[test]
    fn test_quantum_forward_diffusion() {
        let config = QuantumAdvancedDiffusionConfig {
            data_dim: 4,
            num_qubits: 8,
            num_timesteps: 100,
            ..Default::default()
        };

        let model = QuantumAdvancedDiffusionModel::new(config).unwrap();
        let x0 = Array1::from_vec(vec![0.5, -0.3, 0.8, -0.1]);

        let result = model.quantum_forward_diffusion(&x0, 50);
        assert!(result.is_ok());

        let (xt, quantum_noise, quantum_state) = result.unwrap();
        assert_eq!(xt.len(), 4);
        assert_eq!(quantum_noise.len(), 4);
        assert!(quantum_state.entanglement_measure >= 0.0);
        assert!(quantum_state.entanglement_measure <= 1.0);
    }

    #[test]
    fn test_quantum_denoising_network() {
        let config = QuantumAdvancedDiffusionConfig {
            data_dim: 8,
            num_qubits: 4,
            ..Default::default()
        };

        let network = QuantumDenoisingNetwork::new(&config);
        assert!(network.is_ok());

        let network = network.unwrap();
        assert!(!network.quantum_layers.is_empty());
        assert!(!network.classical_layers.is_empty());
    }

    #[test]
    fn test_quantum_generation() {
        let config = QuantumAdvancedDiffusionConfig {
            data_dim: 2,
            num_qubits: 4,
            num_timesteps: 10, // Small for testing
            ..Default::default()
        };

        let model = QuantumAdvancedDiffusionModel::new(config).unwrap();
        let result = model.quantum_generate(3, None, None);

        assert!(result.is_ok());
        let output = result.unwrap();
        assert_eq!(output.samples.shape(), &[3, 2]);
        assert_eq!(output.generation_metrics.len(), 3);
    }

    #[test]
    fn test_multi_scale_noise_schedule() {
        let config = QuantumAdvancedDiffusionConfig {
            noise_schedule: QuantumNoiseSchedule::MultiScale {
                scales: vec![1.0, 0.5, 0.25],
                weights: Array1::from_vec(vec![0.5, 0.3, 0.2]),
                coherence_times: Array1::from_vec(vec![10.0, 5.0, 2.0]),
            },
            ..Default::default()
        };

        let result = QuantumAdvancedDiffusionModel::compute_quantum_schedule(&config);
        assert!(result.is_ok());

        let (betas, _, _) = result.unwrap();
        assert!(betas.iter().all(|&beta| beta >= 0.0 && beta <= 1.0));
    }

    #[test]
    fn test_quantum_metrics_computation() {
        let quantum_state = QuantumState {
            classical_data: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            quantum_phase: Complex64::new(0.8, 0.6),
            entanglement_measure: 0.7,
            coherence_time: 0.9,
            fidelity: 0.85,
        };

        assert!((quantum_state.quantum_phase.norm() - 1.0).abs() < 1e-10);
        assert!(quantum_state.entanglement_measure >= 0.0);
        assert!(quantum_state.entanglement_measure <= 1.0);
        assert!(quantum_state.fidelity >= 0.0);
        assert!(quantum_state.fidelity <= 1.0);
    }
}