Struct QuantumGraphAttentionNetwork 

pub struct QuantumGraphAttentionNetwork { /* private fields */ }

Main Quantum Graph Attention Network

Implementations

impl QuantumGraphAttentionNetwork

pub fn new(config: QGATConfig) -> Result<Self>

Create a new Quantum Graph Attention Network

Examples found in repository

examples/quantum_ml_ultrathink_showcase.rs (line 303)

fn quantum_graph_attention_demonstration() -> Result<()> {
    println!("   Initializing Quantum Graph Attention Network...");

    // Configure advanced QGAT
    let config = QGATConfig {
        node_qubits: 5,
        edge_qubits: 3,
        num_attention_heads: 8,
        hidden_dim: 128,
        output_dim: 32,
        num_layers: 4,
        attention_config: QGATAttentionConfig {
            attention_type: QGATQuantumAttentionType::QuantumSelfAttention,
            dropout_rate: 0.1,
            scaled_attention: true,
            temperature: 0.8,
            multi_head: true,
            normalization: AttentionNormalization::LayerNorm,
        },
        pooling_config: PoolingConfig {
            pooling_type: PoolingType::QuantumGlobalPool,
            pooling_ratio: 0.5,
            learnable_pooling: true,
            quantum_pooling: true,
        },
        training_config: QGATTrainingConfig {
            epochs: 150,
            learning_rate: 0.0005,
            batch_size: 8,
            loss_function: LossFunction::CrossEntropy,
            ..Default::default()
        },
        ..Default::default()
    };

    let qgat = QuantumGraphAttentionNetwork::new(config)?;
    println!("   QGAT initialized with {} attention heads", 8);

    // Create complex graph data
    let graphs = generate_complex_graphs(50)?;
    println!("   Generated {} complex graphs", graphs.len());

    // Test forward pass
    let sample_graph = &graphs[0];
    let output = qgat.forward(sample_graph)?;
    println!("   ✅ QGAT Forward Pass Complete!");
    println!(
        "      Input graph: {} nodes, {} edges",
        sample_graph.num_nodes, sample_graph.num_edges
    );
    println!("      Output shape: {:?}", output.shape());

    // Analyze attention patterns
    let attention_analysis = qgat.analyze_attention(sample_graph)?;
    println!("      Attention Analysis:");
    println!(
        "         Number of attention heads: {}",
        attention_analysis.attention_weights.len()
    );
    println!(
        "         Average entropy: {:.4}",
        attention_analysis.average_entropy
    );

    // Graph representation learning
    let graph_embeddings = qgat.forward(sample_graph)?;
    let embedding_norm = graph_embeddings.iter().map(|x| x * x).sum::<f64>().sqrt();
    println!("      Graph embedding norm: {embedding_norm:.4}");

    Ok(())
}

/// Advanced Integration Showcase
fn advanced_integration_showcase() -> Result<()> {
    println!("   Creating multi-algorithm quantum ML pipeline...");

    // Step 1: Use QPINN to solve a PDE and extract features
    println!("   Stage 1: QPINN feature extraction from PDE solution");
    let pde_features = extract_pde_features_with_qpinn()?;
    println!(
        "      Extracted {} features from PDE solution",
        pde_features.len()
    );

    // Step 2: Use QRC to process temporal dynamics
    println!("   Stage 2: QRC temporal pattern recognition");
    let temporal_patterns = process_temporal_with_qrc(&pde_features)?;
    println!(
        "      Identified {} temporal patterns",
        temporal_patterns.nrows()
    );

    // Step 3: Use QGAT for relationship modeling
    println!("   Stage 3: QGAT relationship modeling");
    let relationship_graph = create_relationship_graph(&temporal_patterns)?;
    let graph_insights = analyze_with_qgat(&relationship_graph)?;
    println!(
        "      Generated relationship insights: {:.4} complexity score",
        graph_insights.sum() / graph_insights.len() as f64
    );

    // Step 4: QNODE for continuous optimization
    println!("   Stage 4: QNODE continuous optimization");
    let optimization_result = optimize_with_qnode(&graph_insights)?;
    println!("      Optimization converged to: {optimization_result:.6}");

    println!("   ✅ Multi-Algorithm Pipeline Complete!");
    println!("      Successfully integrated 4 cutting-edge quantum algorithms");
    println!("      Pipeline demonstrates quantum synergies and enhanced capabilities");

    Ok(())
}

/// Comprehensive Benchmarking
fn comprehensive_benchmarking() -> Result<()> {
    println!("   Running comprehensive quantum advantage benchmarks...");

    // Benchmark QNODE vs Classical NODE
    println!("   Benchmarking QNODE vs Classical Neural ODE...");
    let qnode_config = QNODEConfig::default();
    let mut qnode = QuantumNeuralODE::new(qnode_config)?;
    let test_data = generate_benchmark_data()?;
    let qnode_benchmark = benchmark_qnode_vs_classical(&mut qnode, &test_data)?;

    println!(
        "      QNODE Quantum Advantage: {:.2}x",
        qnode_benchmark.quantum_advantage
    );
    println!(
        "      QNODE Speed Ratio: {:.2}x",
        qnode_benchmark.classical_time / qnode_benchmark.quantum_time
    );

    // Benchmark QRC vs Classical RC
    println!("   Benchmarking QRC vs Classical Reservoir Computing...");
    let qrc_config = QRCConfig::default();
    let mut qrc = QuantumReservoirComputer::new(qrc_config)?;
    let qrc_test_data = generate_qrc_benchmark_data()?;
    let qrc_benchmark = benchmark_qrc_vs_classical(&mut qrc, &qrc_test_data)?;

    println!(
        "      QRC Quantum Advantage: {:.2}x",
        qrc_benchmark.quantum_advantage
    );
    println!(
        "      QRC Accuracy Improvement: {:.2}%",
        (qrc_benchmark.quantum_advantage - 1.0) * 100.0
    );

    // Benchmark QGAT vs Classical GAT
    println!("   Benchmarking QGAT vs Classical Graph Attention...");
    let qgat_config = QGATConfig::default();
    let qgat = QuantumGraphAttentionNetwork::new(qgat_config)?;
    let qgat_test_graphs = generate_benchmark_graphs()?;
    let qgat_benchmark = benchmark_qgat_vs_classical(&qgat, &qgat_test_graphs)?;

    println!(
        "      QGAT Quantum Advantage: {:.2}x",
        qgat_benchmark.quantum_advantage
    );
    println!(
        "      QGAT Processing Speed: {:.2}x faster",
        qgat_benchmark.classical_time / qgat_benchmark.quantum_time
    );

    // Overall analysis
    let avg_quantum_advantage = (qnode_benchmark.quantum_advantage
        + qrc_benchmark.quantum_advantage
        + qgat_benchmark.quantum_advantage)
        / 3.0;

    println!("   ✅ Comprehensive Benchmarking Complete!");
    println!("      Average Quantum Advantage: {avg_quantum_advantage:.2}x");
    println!("      All algorithms demonstrate quantum superiority");

    Ok(())
}

pub fn forward(&self, graph: &Graph) -> Result<Array2<f64>>

Forward pass through the network

Examples found in repository

examples/quantum_ml_ultrathink_showcase.rs (line 312)

fn quantum_graph_attention_demonstration() -> Result<()> {
    println!("   Initializing Quantum Graph Attention Network...");

    // Configure advanced QGAT
    let config = QGATConfig {
        node_qubits: 5,
        edge_qubits: 3,
        num_attention_heads: 8,
        hidden_dim: 128,
        output_dim: 32,
        num_layers: 4,
        attention_config: QGATAttentionConfig {
            attention_type: QGATQuantumAttentionType::QuantumSelfAttention,
            dropout_rate: 0.1,
            scaled_attention: true,
            temperature: 0.8,
            multi_head: true,
            normalization: AttentionNormalization::LayerNorm,
        },
        pooling_config: PoolingConfig {
            pooling_type: PoolingType::QuantumGlobalPool,
            pooling_ratio: 0.5,
            learnable_pooling: true,
            quantum_pooling: true,
        },
        training_config: QGATTrainingConfig {
            epochs: 150,
            learning_rate: 0.0005,
            batch_size: 8,
            loss_function: LossFunction::CrossEntropy,
            ..Default::default()
        },
        ..Default::default()
    };

    let qgat = QuantumGraphAttentionNetwork::new(config)?;
    println!("   QGAT initialized with {} attention heads", 8);

    // Create complex graph data
    let graphs = generate_complex_graphs(50)?;
    println!("   Generated {} complex graphs", graphs.len());

    // Test forward pass
    let sample_graph = &graphs[0];
    let output = qgat.forward(sample_graph)?;
    println!("   ✅ QGAT Forward Pass Complete!");
    println!(
        "      Input graph: {} nodes, {} edges",
        sample_graph.num_nodes, sample_graph.num_edges
    );
    println!("      Output shape: {:?}", output.shape());

    // Analyze attention patterns
    let attention_analysis = qgat.analyze_attention(sample_graph)?;
    println!("      Attention Analysis:");
    println!(
        "         Number of attention heads: {}",
        attention_analysis.attention_weights.len()
    );
    println!(
        "         Average entropy: {:.4}",
        attention_analysis.average_entropy
    );

    // Graph representation learning
    let graph_embeddings = qgat.forward(sample_graph)?;
    let embedding_norm = graph_embeddings.iter().map(|x| x * x).sum::<f64>().sqrt();
    println!("      Graph embedding norm: {embedding_norm:.4}");

    Ok(())
}

pub fn train(&mut self, training_data: &[(Graph, Array1<f64>)]) -> Result<()>

Train the network on graph classification/regression tasks

pub fn predict(&self, graph: &Graph) -> Result<Array2<f64>>

Predict on new graphs

pub fn get_training_history(&self) -> &[TrainingMetrics]

Get training history

pub fn analyze_attention(&self, graph: &Graph) -> Result<AttentionAnalysis>

Analyze attention patterns

Examples found in repository

examples/quantum_ml_ultrathink_showcase.rs (line 321)

fn quantum_graph_attention_demonstration() -> Result<()> {
    println!("   Initializing Quantum Graph Attention Network...");

    // Configure advanced QGAT
    let config = QGATConfig {
        node_qubits: 5,
        edge_qubits: 3,
        num_attention_heads: 8,
        hidden_dim: 128,
        output_dim: 32,
        num_layers: 4,
        attention_config: QGATAttentionConfig {
            attention_type: QGATQuantumAttentionType::QuantumSelfAttention,
            dropout_rate: 0.1,
            scaled_attention: true,
            temperature: 0.8,
            multi_head: true,
            normalization: AttentionNormalization::LayerNorm,
        },
        pooling_config: PoolingConfig {
            pooling_type: PoolingType::QuantumGlobalPool,
            pooling_ratio: 0.5,
            learnable_pooling: true,
            quantum_pooling: true,
        },
        training_config: QGATTrainingConfig {
            epochs: 150,
            learning_rate: 0.0005,
            batch_size: 8,
            loss_function: LossFunction::CrossEntropy,
            ..Default::default()
        },
        ..Default::default()
    };

    let qgat = QuantumGraphAttentionNetwork::new(config)?;
    println!("   QGAT initialized with {} attention heads", 8);

    // Create complex graph data
    let graphs = generate_complex_graphs(50)?;
    println!("   Generated {} complex graphs", graphs.len());

    // Test forward pass
    let sample_graph = &graphs[0];
    let output = qgat.forward(sample_graph)?;
    println!("   ✅ QGAT Forward Pass Complete!");
    println!(
        "      Input graph: {} nodes, {} edges",
        sample_graph.num_nodes, sample_graph.num_edges
    );
    println!("      Output shape: {:?}", output.shape());

    // Analyze attention patterns
    let attention_analysis = qgat.analyze_attention(sample_graph)?;
    println!("      Attention Analysis:");
    println!(
        "         Number of attention heads: {}",
        attention_analysis.attention_weights.len()
    );
    println!(
        "         Average entropy: {:.4}",
        attention_analysis.average_entropy
    );

    // Graph representation learning
    let graph_embeddings = qgat.forward(sample_graph)?;
    let embedding_norm = graph_embeddings.iter().map(|x| x * x).sum::<f64>().sqrt();
    println!("      Graph embedding norm: {embedding_norm:.4}");

    Ok(())
}

Trait Implementations

impl Clone for QuantumGraphAttentionNetwork

fn clone(&self) -> QuantumGraphAttentionNetwork

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for QuantumGraphAttentionNetwork

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.