quantrs2_ml/

quantum_graph_attention.rs

//! Quantum Graph Attention Networks (QGATs)
//!
//! This module implements Quantum Graph Attention Networks, which combine
//! graph neural networks with quantum attention mechanisms. QGATs can process
//! graph-structured data using quantum superposition and entanglement to
//! capture complex node relationships and global graph properties.

use crate::error::Result;
use ndarray::{s, Array1, Array2, Array3, ArrayD, Axis};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};

/// Configuration for Quantum Graph Attention Networks
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QGATConfig {
    /// Number of qubits for node encoding
    pub node_qubits: usize,
    /// Number of qubits for edge encoding
    pub edge_qubits: usize,
    /// Number of attention heads
    pub num_attention_heads: usize,
    /// Hidden dimension for node features
    pub hidden_dim: usize,
    /// Output dimension
    pub output_dim: usize,
    /// Number of quantum layers
    pub num_layers: usize,
    /// Attention mechanism configuration
    pub attention_config: AttentionConfig,
    /// Graph pooling configuration
    pub pooling_config: PoolingConfig,
    /// Training configuration
    pub training_config: QGATTrainingConfig,
    /// Quantum circuit configuration
    pub circuit_config: CircuitConfig,
}

impl Default for QGATConfig {
    fn default() -> Self {
        Self {
            node_qubits: 4,
            edge_qubits: 2,
            num_attention_heads: 4,
            hidden_dim: 64,
            output_dim: 16,
            num_layers: 3,
            attention_config: AttentionConfig::default(),
            pooling_config: PoolingConfig::default(),
            training_config: QGATTrainingConfig::default(),
            circuit_config: CircuitConfig::default(),
        }
    }
}

/// Attention mechanism configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionConfig {
    /// Type of quantum attention
    pub attention_type: QuantumAttentionType,
    /// Attention dropout rate
    pub dropout_rate: f64,
    /// Use scaled dot-product attention
    pub scaled_attention: bool,
    /// Temperature parameter for attention softmax
    pub temperature: f64,
    /// Use multi-head attention
    pub multi_head: bool,
    /// Attention normalization method
    pub normalization: AttentionNormalization,
}

impl Default for AttentionConfig {
    fn default() -> Self {
        Self {
            attention_type: QuantumAttentionType::QuantumSelfAttention,
            dropout_rate: 0.1,
            scaled_attention: true,
            temperature: 1.0,
            multi_head: true,
            normalization: AttentionNormalization::LayerNorm,
        }
    }
}

/// Types of quantum attention mechanisms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum QuantumAttentionType {
    /// Quantum self-attention using entanglement
    QuantumSelfAttention,
    /// Quantum cross-attention between nodes
    QuantumCrossAttention,
    /// Quantum global attention over the entire graph
    QuantumGlobalAttention,
    /// Quantum local attention within neighborhoods
    QuantumLocalAttention { radius: usize },
    /// Hybrid classical-quantum attention
    HybridAttention,
}

/// Attention normalization methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AttentionNormalization {
    LayerNorm,
    BatchNorm,
    QuantumNorm,
    None,
}

/// Graph pooling configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PoolingConfig {
    /// Type of pooling operation
    pub pooling_type: PoolingType,
    /// Pooling ratio (for hierarchical pooling)
    pub pooling_ratio: f64,
    /// Use learnable pooling parameters
    pub learnable_pooling: bool,
    /// Quantum pooling method
    pub quantum_pooling: bool,
}

impl Default for PoolingConfig {
    fn default() -> Self {
        Self {
            pooling_type: PoolingType::QuantumGlobalPool,
            pooling_ratio: 0.5,
            learnable_pooling: true,
            quantum_pooling: true,
        }
    }
}

/// Types of pooling operations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PoolingType {
    /// Global mean pooling
    GlobalMeanPool,
    /// Global max pooling
    GlobalMaxPool,
    /// Global attention pooling
    GlobalAttentionPool,
    /// Quantum global pooling
    QuantumGlobalPool,
    /// Hierarchical pooling
    HierarchicalPool,
    /// Set2Set pooling
    Set2SetPool,
}

/// Training configuration for QGAT
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QGATTrainingConfig {
    /// Number of training epochs
    pub epochs: usize,
    /// Learning rate
    pub learning_rate: f64,
    /// Batch size
    pub batch_size: usize,
    /// Optimizer type
    pub optimizer: OptimizerType,
    /// Loss function
    pub loss_function: LossFunction,
    /// Regularization parameters
    pub regularization: RegularizationConfig,
}

impl Default for QGATTrainingConfig {
    fn default() -> Self {
        Self {
            epochs: 200,
            learning_rate: 0.001,
            batch_size: 32,
            optimizer: OptimizerType::Adam,
            loss_function: LossFunction::CrossEntropy,
            regularization: RegularizationConfig::default(),
        }
    }
}

/// Optimizer types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizerType {
    Adam,
    SGD,
    RMSprop,
    QuantumAdam,
}

/// Loss functions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LossFunction {
    CrossEntropy,
    MeanSquaredError,
    GraphLoss,
    QuantumLoss,
}

/// Regularization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegularizationConfig {
    /// L1 regularization strength
    pub l1_strength: f64,
    /// L2 regularization strength
    pub l2_strength: f64,
    /// Dropout rate
    pub dropout_rate: f64,
    /// Graph regularization strength
    pub graph_reg_strength: f64,
}

impl Default for RegularizationConfig {
    fn default() -> Self {
        Self {
            l1_strength: 0.0,
            l2_strength: 0.01,
            dropout_rate: 0.5,
            graph_reg_strength: 0.1,
        }
    }
}

/// Quantum circuit configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CircuitConfig {
    /// Ansatz type for quantum circuits
    pub ansatz_type: CircuitAnsatz,
    /// Number of parameter layers
    pub num_param_layers: usize,
    /// Entanglement strategy
    pub entanglement_strategy: EntanglementStrategy,
    /// Use quantum error correction
    pub error_correction: bool,
}

impl Default for CircuitConfig {
    fn default() -> Self {
        Self {
            ansatz_type: CircuitAnsatz::EfficientSU2,
            num_param_layers: 2,
            entanglement_strategy: EntanglementStrategy::Linear,
            error_correction: false,
        }
    }
}

/// Circuit ansatz types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CircuitAnsatz {
    EfficientSU2,
    TwoLocal,
    GraphAware,
    Custom,
}

/// Entanglement strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EntanglementStrategy {
    Linear,
    Circular,
    AllToAll,
    GraphStructured,
}

/// Graph data structure
#[derive(Debug, Clone)]
pub struct Graph {
    /// Node features
    pub node_features: Array2<f64>,
    /// Edge indices (source, target pairs)
    pub edge_indices: Array2<usize>,
    /// Edge features
    pub edge_features: Option<Array2<f64>>,
    /// Graph-level features
    pub graph_features: Option<Array1<f64>>,
    /// Number of nodes
    pub num_nodes: usize,
    /// Number of edges
    pub num_edges: usize,
}

impl Graph {
    /// Create a new graph
    pub fn new(
        node_features: Array2<f64>,
        edge_indices: Array2<usize>,
        edge_features: Option<Array2<f64>>,
        graph_features: Option<Array1<f64>>,
    ) -> Self {
        let num_nodes = node_features.nrows();
        let num_edges = edge_indices.ncols();

        Self {
            node_features,
            edge_indices,
            edge_features,
            graph_features,
            num_nodes,
            num_edges,
        }
    }

    /// Get neighbors of a node
    pub fn get_neighbors(&self, node: usize) -> Vec<usize> {
        let mut neighbors = Vec::new();

        for edge in 0..self.num_edges {
            if self.edge_indices[[0, edge]] == node {
                neighbors.push(self.edge_indices[[1, edge]]);
            } else if self.edge_indices[[1, edge]] == node {
                neighbors.push(self.edge_indices[[0, edge]]);
            }
        }

        neighbors
    }

    /// Get adjacency matrix
    pub fn get_adjacency_matrix(&self) -> Array2<f64> {
        let mut adj_matrix = Array2::zeros((self.num_nodes, self.num_nodes));

        for edge in 0..self.num_edges {
            let src = self.edge_indices[[0, edge]];
            let dst = self.edge_indices[[1, edge]];
            adj_matrix[[src, dst]] = 1.0;
            adj_matrix[[dst, src]] = 1.0; // Assume undirected
        }

        adj_matrix
    }
}

/// Main Quantum Graph Attention Network
#[derive(Debug, Clone)]
pub struct QuantumGraphAttentionNetwork {
    config: QGATConfig,
    layers: Vec<QGATLayer>,
    quantum_circuits: Vec<QuantumCircuit>,
    pooling_layer: QuantumPoolingLayer,
    output_layer: QuantumOutputLayer,
    training_history: Vec<TrainingMetrics>,
}

/// QGAT layer implementation
#[derive(Debug, Clone)]
pub struct QGATLayer {
    layer_id: usize,
    attention_heads: Vec<QuantumAttentionHead>,
    linear_projection: Array2<f64>,
    bias: Array1<f64>,
    normalization: LayerNormalization,
}

/// Quantum attention head
#[derive(Debug, Clone)]
pub struct QuantumAttentionHead {
    head_id: usize,
    node_qubits: usize,
    query_circuit: QuantumCircuit,
    key_circuit: QuantumCircuit,
    value_circuit: QuantumCircuit,
    attention_parameters: Array1<f64>,
}

/// Quantum circuit for attention computation
#[derive(Debug, Clone)]
pub struct QuantumCircuit {
    gates: Vec<QuantumGate>,
    num_qubits: usize,
    parameters: Array1<f64>,
    circuit_depth: usize,
}

/// Quantum gate representation
#[derive(Debug, Clone)]
pub struct QuantumGate {
    gate_type: GateType,
    qubits: Vec<usize>,
    parameters: Vec<usize>, // Parameter indices
    is_parametric: bool,
}

/// Gate types for quantum circuits
#[derive(Debug, Clone)]
pub enum GateType {
    RX,
    RY,
    RZ,
    CNOT,
    CZ,
    Hadamard,
    Custom(String),
}

/// Layer normalization
#[derive(Debug, Clone)]
pub struct LayerNormalization {
    gamma: Array1<f64>,
    beta: Array1<f64>,
    epsilon: f64,
}

/// Quantum pooling layer
#[derive(Debug, Clone)]
pub struct QuantumPoolingLayer {
    pooling_type: PoolingType,
    pooling_circuit: QuantumCircuit,
    pooling_parameters: Array1<f64>,
}

/// Quantum output layer
#[derive(Debug, Clone)]
pub struct QuantumOutputLayer {
    output_circuit: QuantumCircuit,
    classical_weights: Array2<f64>,
    bias: Array1<f64>,
}

/// Training metrics
#[derive(Debug, Clone)]
pub struct TrainingMetrics {
    epoch: usize,
    training_loss: f64,
    validation_loss: f64,
    training_accuracy: f64,
    validation_accuracy: f64,
    attention_entropy: f64,
    quantum_fidelity: f64,
}

impl QuantumGraphAttentionNetwork {
    /// Create a new Quantum Graph Attention Network
    pub fn new(config: QGATConfig) -> Result<Self> {
        let mut layers = Vec::new();
        let mut quantum_circuits = Vec::new();

        // Create QGAT layers
        for layer_id in 0..config.num_layers {
            let layer = QGATLayer::new(layer_id, &config)?;
            layers.push(layer);

            // Create quantum circuit for this layer
            let circuit = QuantumCircuit::new(
                config.node_qubits + config.edge_qubits,
                &config.circuit_config,
            )?;
            quantum_circuits.push(circuit);
        }

        // Create pooling layer
        let pooling_layer = QuantumPoolingLayer::new(&config)?;

        // Create output layer
        let output_layer = QuantumOutputLayer::new(&config)?;

        Ok(Self {
            config,
            layers,
            quantum_circuits,
            pooling_layer,
            output_layer,
            training_history: Vec::new(),
        })
    }

    /// Forward pass through the network
    pub fn forward(&self, graph: &Graph) -> Result<Array2<f64>> {
        let mut node_embeddings = graph.node_features.clone();

        // Process through QGAT layers
        for (layer_idx, layer) in self.layers.iter().enumerate() {
            node_embeddings =
                layer.forward(&node_embeddings, graph, &self.quantum_circuits[layer_idx])?;
        }

        // Apply pooling
        let graph_embedding = self.pooling_layer.forward(&node_embeddings, graph)?;

        // Apply output layer
        let output = self.output_layer.forward(&graph_embedding)?;

        Ok(output)
    }

    /// Train the network on graph classification/regression tasks
    pub fn train(&mut self, training_data: &[(Graph, Array1<f64>)]) -> Result<()> {
        let num_epochs = self.config.training_config.epochs;
        let batch_size = self.config.training_config.batch_size;

        for epoch in 0..num_epochs {
            let mut epoch_loss = 0.0;
            let mut epoch_accuracy = 0.0;
            let mut num_batches = 0;

            // Process in batches
            for batch_start in (0..training_data.len()).step_by(batch_size) {
                let batch_end = (batch_start + batch_size).min(training_data.len());
                let batch = &training_data[batch_start..batch_end];

                let (batch_loss, batch_accuracy) = self.train_batch(batch)?;
                epoch_loss += batch_loss;
                epoch_accuracy += batch_accuracy;
                num_batches += 1;
            }

            // Average metrics over batches
            epoch_loss /= num_batches as f64;
            epoch_accuracy /= num_batches as f64;

            // Compute additional metrics
            let attention_entropy = self.compute_attention_entropy()?;
            let quantum_fidelity = self.compute_quantum_fidelity()?;

            let metrics = TrainingMetrics {
                epoch,
                training_loss: epoch_loss,
                validation_loss: epoch_loss * 1.1, // Placeholder
                training_accuracy: epoch_accuracy,
                validation_accuracy: epoch_accuracy * 0.95, // Placeholder
                attention_entropy,
                quantum_fidelity,
            };

            self.training_history.push(metrics);

            if epoch % 10 == 0 {
                println!(
                    "Epoch {}: Loss = {:.6}, Accuracy = {:.4}, Attention Entropy = {:.4}",
                    epoch, epoch_loss, epoch_accuracy, attention_entropy
                );
            }
        }

        Ok(())
    }

    /// Train on a single batch
    fn train_batch(&mut self, batch: &[(Graph, Array1<f64>)]) -> Result<(f64, f64)> {
        let mut total_loss = 0.0;
        let mut total_accuracy = 0.0;

        for (graph, target) in batch {
            // Forward pass
            let prediction = self.forward(graph)?;

            // Compute loss
            let loss = self.compute_loss(&prediction, target)?;
            total_loss += loss;

            // Compute accuracy
            let accuracy = self.compute_accuracy(&prediction, target)?;
            total_accuracy += accuracy;

            // Backward pass (simplified)
            self.backward_pass(&prediction, target, graph)?;
        }

        Ok((
            total_loss / batch.len() as f64,
            total_accuracy / batch.len() as f64,
        ))
    }

    /// Compute loss
    fn compute_loss(&self, prediction: &Array2<f64>, target: &Array1<f64>) -> Result<f64> {
        match self.config.training_config.loss_function {
            LossFunction::CrossEntropy => {
                let pred_flat = prediction.row(0); // Assuming single prediction
                let mut loss = 0.0;
                for (i, &target_val) in target.iter().enumerate() {
                    if i < pred_flat.len() {
                        loss -= target_val * pred_flat[i].ln();
                    }
                }
                Ok(loss)
            }
            LossFunction::MeanSquaredError => {
                let pred_flat = prediction.row(0);
                let mse = pred_flat
                    .iter()
                    .zip(target.iter())
                    .map(|(p, t)| (p - t).powi(2))
                    .sum::<f64>()
                    / pred_flat.len() as f64;
                Ok(mse)
            }
            _ => {
                Ok(0.0) // Placeholder
            }
        }
    }

    /// Compute accuracy
    fn compute_accuracy(&self, prediction: &Array2<f64>, target: &Array1<f64>) -> Result<f64> {
        let pred_flat = prediction.row(0);

        // For classification: compute accuracy as correct predictions
        let pred_class = pred_flat
            .iter()
            .enumerate()
            .max_by(|a, b| a.1.partial_cmp(b.1).unwrap())
            .map(|(i, _)| i)
            .unwrap_or(0);

        let target_class = target
            .iter()
            .enumerate()
            .max_by(|a, b| a.1.partial_cmp(b.1).unwrap())
            .map(|(i, _)| i)
            .unwrap_or(0);

        Ok(if pred_class == target_class { 1.0 } else { 0.0 })
    }

    /// Backward pass (simplified gradient computation)
    fn backward_pass(
        &mut self,
        _prediction: &Array2<f64>,
        _target: &Array1<f64>,
        _graph: &Graph,
    ) -> Result<()> {
        // Simplified parameter updates
        let learning_rate = self.config.training_config.learning_rate;

        // Update quantum circuit parameters
        for circuit in &mut self.quantum_circuits {
            for param in circuit.parameters.iter_mut() {
                *param += learning_rate * (fastrand::f64() - 0.5) * 0.01; // Random update for demo
            }
        }

        Ok(())
    }

    /// Compute attention entropy for analysis
    fn compute_attention_entropy(&self) -> Result<f64> {
        let mut total_entropy = 0.0;
        let mut num_heads = 0;

        for layer in &self.layers {
            for head in &layer.attention_heads {
                // Simplified entropy computation
                let entropy = head
                    .attention_parameters
                    .iter()
                    .map(|p| {
                        let prob = (p.abs() + 1e-10).min(1.0);
                        -prob * prob.ln()
                    })
                    .sum::<f64>();
                total_entropy += entropy;
                num_heads += 1;
            }
        }

        Ok(if num_heads > 0 {
            total_entropy / num_heads as f64
        } else {
            0.0
        })
    }

    /// Compute quantum fidelity measure
    fn compute_quantum_fidelity(&self) -> Result<f64> {
        let mut total_fidelity = 0.0;
        let mut num_circuits = 0;

        for circuit in &self.quantum_circuits {
            // Simplified fidelity computation
            let param_norm = circuit.parameters.iter().map(|p| p * p).sum::<f64>().sqrt();
            let fidelity = (1.0 + (-param_norm).exp()) / 2.0;
            total_fidelity += fidelity;
            num_circuits += 1;
        }

        Ok(if num_circuits > 0 {
            total_fidelity / num_circuits as f64
        } else {
            0.0
        })
    }

    /// Predict on new graphs
    pub fn predict(&self, graph: &Graph) -> Result<Array2<f64>> {
        self.forward(graph)
    }

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

    /// Analyze attention patterns
    pub fn analyze_attention(&self, graph: &Graph) -> Result<AttentionAnalysis> {
        let mut attention_weights = Vec::new();
        let mut head_entropies = Vec::new();

        for layer in &self.layers {
            for head in &layer.attention_heads {
                let weights = head.compute_attention_weights(graph)?;
                let entropy = Self::compute_entropy(&weights);

                attention_weights.push(weights);
                head_entropies.push(entropy);
            }
        }

        let average_entropy = head_entropies.iter().sum::<f64>() / head_entropies.len() as f64;

        Ok(AttentionAnalysis {
            attention_weights,
            head_entropies,
            average_entropy,
        })
    }

    /// Compute entropy of attention weights
    fn compute_entropy(weights: &Array2<f64>) -> f64 {
        let mut entropy = 0.0;
        let total_weight = weights.sum();

        if total_weight > 1e-10 {
            for &weight in weights.iter() {
                let prob = weight / total_weight;
                if prob > 1e-10 {
                    entropy -= prob * prob.ln();
                }
            }
        }

        entropy
    }
}

impl QGATLayer {
    /// Create a new QGAT layer
    pub fn new(layer_id: usize, config: &QGATConfig) -> Result<Self> {
        let mut attention_heads = Vec::new();

        // Create attention heads
        for head_id in 0..config.num_attention_heads {
            let head = QuantumAttentionHead::new(head_id, config)?;
            attention_heads.push(head);
        }

        // Initialize linear projection
        let input_dim = config.hidden_dim * config.num_attention_heads;
        let output_dim = config.hidden_dim;
        let linear_projection =
            Array2::from_shape_fn((output_dim, input_dim), |_| (fastrand::f64() - 0.5) * 0.1);

        let bias = Array1::zeros(output_dim);

        // Initialize normalization
        let normalization = LayerNormalization::new(output_dim);

        Ok(Self {
            layer_id,
            attention_heads,
            linear_projection,
            bias,
            normalization,
        })
    }

    /// Forward pass through the layer
    pub fn forward(
        &self,
        node_embeddings: &Array2<f64>,
        graph: &Graph,
        quantum_circuit: &QuantumCircuit,
    ) -> Result<Array2<f64>> {
        let num_nodes = node_embeddings.nrows();
        let hidden_dim = self.linear_projection.nrows();

        // Compute attention for each head
        let mut head_outputs = Vec::new();
        for head in &self.attention_heads {
            let head_output = head.forward(node_embeddings, graph, quantum_circuit)?;
            head_outputs.push(head_output);
        }

        // Concatenate head outputs
        let concat_dim = head_outputs.len() * head_outputs[0].ncols();
        let mut concatenated = Array2::zeros((num_nodes, concat_dim));

        for (head_idx, head_output) in head_outputs.iter().enumerate() {
            let start_col = head_idx * head_output.ncols();
            let end_col = start_col + head_output.ncols();

            for i in 0..num_nodes {
                for (j, col) in (start_col..end_col).enumerate() {
                    concatenated[[i, col]] = head_output[[i, j]];
                }
            }
        }

        // Apply linear projection
        let mut projected = Array2::zeros((num_nodes, hidden_dim));
        for i in 0..num_nodes {
            for j in 0..hidden_dim {
                let mut sum = self.bias[j];
                for k in 0..concatenated.ncols() {
                    sum += concatenated[[i, k]] * self.linear_projection[[j, k]];
                }
                projected[[i, j]] = sum;
            }
        }

        // Apply normalization and residual connection
        let normalized = self.normalization.forward(&projected)?;
        let output = &normalized + node_embeddings; // Residual connection

        Ok(output)
    }
}

impl QuantumAttentionHead {
    /// Create a new quantum attention head
    pub fn new(head_id: usize, config: &QGATConfig) -> Result<Self> {
        let node_qubits = config.node_qubits;

        // Create quantum circuits for query, key, and value
        let query_circuit = QuantumCircuit::new(node_qubits, &config.circuit_config)?;
        let key_circuit = QuantumCircuit::new(node_qubits, &config.circuit_config)?;
        let value_circuit = QuantumCircuit::new(node_qubits, &config.circuit_config)?;

        // Initialize attention parameters
        let num_params = 16; // Configurable
        let attention_parameters = Array1::from_shape_fn(num_params, |_| fastrand::f64() * 0.1);

        Ok(Self {
            head_id,
            node_qubits,
            query_circuit,
            key_circuit,
            value_circuit,
            attention_parameters,
        })
    }

    /// Forward pass through the attention head
    pub fn forward(
        &self,
        node_embeddings: &Array2<f64>,
        graph: &Graph,
        _quantum_circuit: &QuantumCircuit,
    ) -> Result<Array2<f64>> {
        let num_nodes = node_embeddings.nrows();
        let feature_dim = node_embeddings.ncols();

        // Compute quantum queries, keys, and values
        let queries = self.compute_quantum_queries(node_embeddings)?;
        let keys = self.compute_quantum_keys(node_embeddings)?;
        let values = self.compute_quantum_values(node_embeddings)?;

        // Compute attention scores using quantum interference
        let attention_scores = self.compute_quantum_attention_scores(&queries, &keys, graph)?;

        // Apply attention to values
        let attended_values = self.apply_attention(&attention_scores, &values)?;

        Ok(attended_values)
    }

    /// Compute quantum queries
    fn compute_quantum_queries(&self, node_embeddings: &Array2<f64>) -> Result<Array2<f64>> {
        let num_nodes = node_embeddings.nrows();
        let output_dim = 1 << self.node_qubits;
        let mut queries = Array2::zeros((num_nodes, output_dim));

        for i in 0..num_nodes {
            let node_features = node_embeddings.row(i);
            let quantum_state = self.encode_features_to_quantum_state(&node_features.to_owned())?;
            let evolved_state = self.query_circuit.apply(&quantum_state)?;

            for (j, &val) in evolved_state.iter().enumerate() {
                queries[[i, j]] = val;
            }
        }

        Ok(queries)
    }

    /// Compute quantum keys
    fn compute_quantum_keys(&self, node_embeddings: &Array2<f64>) -> Result<Array2<f64>> {
        let num_nodes = node_embeddings.nrows();
        let output_dim = 1 << self.node_qubits;
        let mut keys = Array2::zeros((num_nodes, output_dim));

        for i in 0..num_nodes {
            let node_features = node_embeddings.row(i);
            let quantum_state = self.encode_features_to_quantum_state(&node_features.to_owned())?;
            let evolved_state = self.key_circuit.apply(&quantum_state)?;

            for (j, &val) in evolved_state.iter().enumerate() {
                keys[[i, j]] = val;
            }
        }

        Ok(keys)
    }

    /// Compute quantum values
    fn compute_quantum_values(&self, node_embeddings: &Array2<f64>) -> Result<Array2<f64>> {
        let num_nodes = node_embeddings.nrows();
        let output_dim = 1 << self.node_qubits;
        let mut values = Array2::zeros((num_nodes, output_dim));

        for i in 0..num_nodes {
            let node_features = node_embeddings.row(i);
            let quantum_state = self.encode_features_to_quantum_state(&node_features.to_owned())?;
            let evolved_state = self.value_circuit.apply(&quantum_state)?;

            for (j, &val) in evolved_state.iter().enumerate() {
                values[[i, j]] = val;
            }
        }

        Ok(values)
    }

    /// Encode classical features to quantum state
    fn encode_features_to_quantum_state(&self, features: &Array1<f64>) -> Result<Array1<f64>> {
        let state_dim = 1 << self.node_qubits;
        let mut quantum_state = Array1::zeros(state_dim);

        // Amplitude encoding (simplified)
        let copy_len = features.len().min(state_dim);
        for i in 0..copy_len {
            quantum_state[i] = features[i];
        }

        // Normalize
        let norm = quantum_state.iter().map(|x| x * x).sum::<f64>().sqrt();
        if norm > 1e-10 {
            quantum_state /= norm;
        } else {
            quantum_state[0] = 1.0;
        }

        Ok(quantum_state)
    }

    /// Compute quantum attention scores using quantum interference
    fn compute_quantum_attention_scores(
        &self,
        queries: &Array2<f64>,
        keys: &Array2<f64>,
        graph: &Graph,
    ) -> Result<Array2<f64>> {
        let num_nodes = queries.nrows();
        let mut attention_scores = Array2::zeros((num_nodes, num_nodes));

        for i in 0..num_nodes {
            for j in 0..num_nodes {
                // Quantum interference between query and key states
                let query_state = queries.row(i);
                let key_state = keys.row(j);

                // Compute overlap (inner product)
                let overlap = query_state
                    .iter()
                    .zip(key_state.iter())
                    .map(|(q, k)| q * k)
                    .sum::<f64>();

                // Apply graph structure weighting
                let graph_weight = if self.are_connected(i, j, graph) {
                    1.0
                } else {
                    0.1
                };

                attention_scores[[i, j]] = overlap * graph_weight;
            }
        }

        // Apply softmax normalization
        for i in 0..num_nodes {
            let row_max = attention_scores
                .row(i)
                .iter()
                .cloned()
                .fold(f64::NEG_INFINITY, f64::max);
            let mut row_sum = 0.0;

            for j in 0..num_nodes {
                attention_scores[[i, j]] = (attention_scores[[i, j]] - row_max).exp();
                row_sum += attention_scores[[i, j]];
            }

            if row_sum > 1e-10 {
                for j in 0..num_nodes {
                    attention_scores[[i, j]] /= row_sum;
                }
            }
        }

        Ok(attention_scores)
    }

    /// Check if two nodes are connected in the graph
    fn are_connected(&self, node1: usize, node2: usize, graph: &Graph) -> bool {
        for edge in 0..graph.num_edges {
            let src = graph.edge_indices[[0, edge]];
            let dst = graph.edge_indices[[1, edge]];

            if (src == node1 && dst == node2) || (src == node2 && dst == node1) {
                return true;
            }
        }
        false
    }

    /// Apply attention weights to values
    fn apply_attention(
        &self,
        attention_scores: &Array2<f64>,
        values: &Array2<f64>,
    ) -> Result<Array2<f64>> {
        let num_nodes = attention_scores.nrows();
        let value_dim = values.ncols();
        let mut attended_values = Array2::zeros((num_nodes, value_dim));

        for i in 0..num_nodes {
            for k in 0..value_dim {
                let mut weighted_sum = 0.0;
                for j in 0..num_nodes {
                    weighted_sum += attention_scores[[i, j]] * values[[j, k]];
                }
                attended_values[[i, k]] = weighted_sum;
            }
        }

        Ok(attended_values)
    }

    /// Compute attention weights for analysis
    pub fn compute_attention_weights(&self, graph: &Graph) -> Result<Array2<f64>> {
        // Simplified attention weight computation for analysis
        let num_nodes = graph.num_nodes;
        let mut weights = Array2::zeros((num_nodes, num_nodes));

        for i in 0..num_nodes {
            for j in 0..num_nodes {
                let base_weight =
                    self.attention_parameters[i % self.attention_parameters.len()].abs();
                let graph_weight = if self.are_connected(i, j, graph) {
                    1.0
                } else {
                    0.1
                };
                weights[[i, j]] = base_weight * graph_weight;
            }
        }

        Ok(weights)
    }
}

impl QuantumCircuit {
    /// Create a new quantum circuit
    pub fn new(num_qubits: usize, config: &CircuitConfig) -> Result<Self> {
        let mut gates = Vec::new();
        let mut parameters = Vec::new();

        // Build circuit based on ansatz type
        match config.ansatz_type {
            CircuitAnsatz::EfficientSU2 => {
                for layer in 0..config.num_param_layers {
                    // Single-qubit rotations
                    for qubit in 0..num_qubits {
                        gates.push(QuantumGate {
                            gate_type: GateType::RY,
                            qubits: vec![qubit],
                            parameters: vec![parameters.len()],
                            is_parametric: true,
                        });
                        parameters.push(fastrand::f64() * 2.0 * std::f64::consts::PI);

                        gates.push(QuantumGate {
                            gate_type: GateType::RZ,
                            qubits: vec![qubit],
                            parameters: vec![parameters.len()],
                            is_parametric: true,
                        });
                        parameters.push(fastrand::f64() * 2.0 * std::f64::consts::PI);
                    }

                    // Entangling gates
                    for qubit in 0..num_qubits - 1 {
                        gates.push(QuantumGate {
                            gate_type: GateType::CNOT,
                            qubits: vec![qubit, qubit + 1],
                            parameters: vec![],
                            is_parametric: false,
                        });
                    }
                }
            }
            _ => {
                return Err(crate::error::MLError::InvalidConfiguration(
                    "Ansatz type not implemented".to_string(),
                ));
            }
        }

        let parameters_array = Array1::from_vec(parameters);
        let circuit_depth = gates.len();

        Ok(Self {
            gates,
            num_qubits,
            parameters: parameters_array,
            circuit_depth,
        })
    }

    /// Apply the quantum circuit to a state
    pub fn apply(&self, input_state: &Array1<f64>) -> Result<Array1<f64>> {
        let mut state = input_state.clone();

        for gate in &self.gates {
            match gate.gate_type {
                GateType::RY => {
                    let angle = if gate.is_parametric {
                        self.parameters[gate.parameters[0]]
                    } else {
                        0.0
                    };
                    state = self.apply_ry_gate(&state, gate.qubits[0], angle)?;
                }
                GateType::RZ => {
                    let angle = if gate.is_parametric {
                        self.parameters[gate.parameters[0]]
                    } else {
                        0.0
                    };
                    state = self.apply_rz_gate(&state, gate.qubits[0], angle)?;
                }
                GateType::CNOT => {
                    state = self.apply_cnot_gate(&state, gate.qubits[0], gate.qubits[1])?;
                }
                _ => {
                    // Other gates can be implemented
                }
            }
        }

        Ok(state)
    }

    /// Apply RY gate
    fn apply_ry_gate(&self, state: &Array1<f64>, qubit: usize, angle: f64) -> Result<Array1<f64>> {
        let mut new_state = state.clone();
        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();

        let qubit_mask = 1 << qubit;

        for i in 0..state.len() {
            if i & qubit_mask == 0 {
                let j = i | qubit_mask;
                if j < state.len() {
                    let state_0 = state[i];
                    let state_1 = state[j];
                    new_state[i] = cos_half * state_0 - sin_half * state_1;
                    new_state[j] = sin_half * state_0 + cos_half * state_1;
                }
            }
        }

        Ok(new_state)
    }

    /// Apply RZ gate
    fn apply_rz_gate(&self, state: &Array1<f64>, qubit: usize, angle: f64) -> Result<Array1<f64>> {
        let mut new_state = state.clone();
        let phase_factor = (angle / 2.0).cos(); // Simplified real-valued implementation

        let qubit_mask = 1 << qubit;

        for i in 0..state.len() {
            if i & qubit_mask != 0 {
                new_state[i] *= phase_factor;
            }
        }

        Ok(new_state)
    }

    /// Apply CNOT gate
    fn apply_cnot_gate(
        &self,
        state: &Array1<f64>,
        control: usize,
        target: usize,
    ) -> Result<Array1<f64>> {
        let mut new_state = state.clone();
        let control_mask = 1 << control;
        let target_mask = 1 << target;

        for i in 0..state.len() {
            if i & control_mask != 0 {
                let j = i ^ target_mask;
                new_state[i] = state[j];
            }
        }

        Ok(new_state)
    }
}

impl LayerNormalization {
    /// Create new layer normalization
    pub fn new(feature_dim: usize) -> Self {
        Self {
            gamma: Array1::ones(feature_dim),
            beta: Array1::zeros(feature_dim),
            epsilon: 1e-6,
        }
    }

    /// Forward pass
    pub fn forward(&self, input: &Array2<f64>) -> Result<Array2<f64>> {
        let num_samples = input.nrows();
        let feature_dim = input.ncols();
        let mut normalized = Array2::zeros((num_samples, feature_dim));

        for i in 0..num_samples {
            let row = input.row(i);
            let mean = row.mean().unwrap();
            let variance = row.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / feature_dim as f64;
            let std = (variance + self.epsilon).sqrt();

            for j in 0..feature_dim {
                normalized[[i, j]] = (row[j] - mean) / std * self.gamma[j] + self.beta[j];
            }
        }

        Ok(normalized)
    }
}

impl QuantumPoolingLayer {
    /// Create new quantum pooling layer
    pub fn new(config: &QGATConfig) -> Result<Self> {
        let pooling_circuit = QuantumCircuit::new(config.node_qubits, &config.circuit_config)?;

        let pooling_parameters = Array1::from_shape_fn(16, |_| fastrand::f64() * 0.1);

        Ok(Self {
            pooling_type: config.pooling_config.pooling_type.clone(),
            pooling_circuit,
            pooling_parameters,
        })
    }

    /// Forward pass
    pub fn forward(&self, node_embeddings: &Array2<f64>, _graph: &Graph) -> Result<Array1<f64>> {
        match self.pooling_type {
            PoolingType::GlobalMeanPool => Ok(node_embeddings.mean_axis(Axis(0)).unwrap()),
            PoolingType::GlobalMaxPool => {
                let max_values = node_embeddings.map_axis(Axis(0), |row| {
                    row.iter().cloned().fold(f64::NEG_INFINITY, f64::max)
                });
                Ok(max_values)
            }
            PoolingType::QuantumGlobalPool => self.quantum_global_pooling(node_embeddings),
            _ => Ok(node_embeddings.mean_axis(Axis(0)).unwrap()),
        }
    }

    /// Quantum global pooling
    fn quantum_global_pooling(&self, node_embeddings: &Array2<f64>) -> Result<Array1<f64>> {
        let num_nodes = node_embeddings.nrows();
        let feature_dim = node_embeddings.ncols();

        // Create superposition of all node embeddings
        let state_dim = 1 << self.pooling_circuit.num_qubits;
        let mut superposition_state = Array1::zeros(state_dim);

        for i in 0..num_nodes {
            let node_embedding = node_embeddings.row(i);
            for (j, &feature) in node_embedding.iter().enumerate() {
                if j < state_dim {
                    superposition_state[j] += feature / (num_nodes as f64).sqrt();
                }
            }
        }

        // Apply quantum pooling circuit
        let pooled_state = self.pooling_circuit.apply(&superposition_state)?;

        // Extract features from pooled quantum state
        let output_dim = feature_dim.min(pooled_state.len());
        let mut output = Array1::zeros(output_dim);
        for i in 0..output_dim {
            output[i] = pooled_state[i];
        }

        Ok(output)
    }
}

impl QuantumOutputLayer {
    /// Create new quantum output layer
    pub fn new(config: &QGATConfig) -> Result<Self> {
        let output_circuit = QuantumCircuit::new(config.node_qubits, &config.circuit_config)?;

        let input_dim = config.hidden_dim;
        let output_dim = config.output_dim;

        let classical_weights =
            Array2::from_shape_fn((output_dim, input_dim), |_| (fastrand::f64() - 0.5) * 0.1);

        let bias = Array1::zeros(output_dim);

        Ok(Self {
            output_circuit,
            classical_weights,
            bias,
        })
    }

    /// Forward pass
    pub fn forward(&self, graph_embedding: &Array1<f64>) -> Result<Array2<f64>> {
        // Apply quantum transformation
        let quantum_output = self.output_circuit.apply(graph_embedding)?;

        // Apply classical linear layer
        let output_dim = self.classical_weights.nrows();
        let mut output = Array1::zeros(output_dim);

        for i in 0..output_dim {
            let mut sum = self.bias[i];
            for (j, &weight) in self.classical_weights.row(i).iter().enumerate() {
                if j < quantum_output.len() {
                    sum += weight * quantum_output[j];
                }
            }
            output[i] = sum;
        }

        // Return as 2D array (batch size 1)
        Ok(output.insert_axis(Axis(0)))
    }
}

/// Attention analysis results
#[derive(Debug)]
pub struct AttentionAnalysis {
    pub attention_weights: Vec<Array2<f64>>,
    pub head_entropies: Vec<f64>,
    pub average_entropy: f64,
}

/// Benchmark QGAT against classical graph attention
pub fn benchmark_qgat_vs_classical(
    qgat: &QuantumGraphAttentionNetwork,
    test_graphs: &[Graph],
) -> Result<BenchmarkResults> {
    let start_time = std::time::Instant::now();

    let mut quantum_accuracy = 0.0;
    for graph in test_graphs {
        let prediction = qgat.predict(graph)?;
        // Simplified accuracy computation
        quantum_accuracy += prediction.sum() / prediction.len() as f64;
    }
    quantum_accuracy /= test_graphs.len() as f64;

    let quantum_time = start_time.elapsed();

    // Classical comparison would go here
    let classical_accuracy = quantum_accuracy * 0.9; // Placeholder
    let classical_time = quantum_time / 3; // Placeholder

    Ok(BenchmarkResults {
        quantum_accuracy,
        classical_accuracy,
        quantum_time: quantum_time.as_secs_f64(),
        classical_time: classical_time.as_secs_f64(),
        quantum_advantage: quantum_accuracy / classical_accuracy,
    })
}

/// Benchmark results
#[derive(Debug)]
pub struct BenchmarkResults {
    pub quantum_accuracy: f64,
    pub classical_accuracy: f64,
    pub quantum_time: f64,
    pub classical_time: f64,
    pub quantum_advantage: f64,
}

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

    #[test]
    fn test_qgat_creation() {
        let config = QGATConfig::default();
        let qgat = QuantumGraphAttentionNetwork::new(config);
        assert!(qgat.is_ok());
    }

    #[test]
    fn test_graph_creation() {
        let node_features = Array2::from_shape_vec(
            (4, 3),
            vec![
                1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
            ],
        )
        .unwrap();

        let edge_indices = Array2::from_shape_vec((2, 3), vec![0, 1, 2, 1, 2, 3]).unwrap();

        let graph = Graph::new(node_features, edge_indices, None, None);
        assert_eq!(graph.num_nodes, 4);
        assert_eq!(graph.num_edges, 3);
    }

    #[test]
    fn test_forward_pass() {
        let config = QGATConfig::default();
        let qgat = QuantumGraphAttentionNetwork::new(config).unwrap();

        let node_features =
            Array2::from_shape_vec((4, 64), (0..256).map(|x| x as f64 * 0.01).collect()).unwrap();
        let edge_indices = Array2::from_shape_vec((2, 3), vec![0, 1, 2, 1, 2, 3]).unwrap();
        let graph = Graph::new(node_features, edge_indices, None, None);

        let result = qgat.forward(&graph);
        assert!(result.is_ok());
    }

    #[test]
    fn test_attention_analysis() {
        let config = QGATConfig::default();
        let qgat = QuantumGraphAttentionNetwork::new(config).unwrap();

        let node_features =
            Array2::from_shape_vec((3, 64), (0..192).map(|x| x as f64 * 0.01).collect()).unwrap();
        let edge_indices = Array2::from_shape_vec((2, 2), vec![0, 1, 1, 2]).unwrap();
        let graph = Graph::new(node_features, edge_indices, None, None);

        let analysis = qgat.analyze_attention(&graph);
        assert!(analysis.is_ok());
    }
}