Struct QuantumTransformerConfig 

pub struct QuantumTransformerConfig {
    pub model_dim: usize,
    pub num_heads: usize,
    pub ff_dim: usize,
    pub num_layers: usize,
    pub max_seq_len: usize,
    pub num_qubits: usize,
    pub dropout_rate: f64,
    pub attention_type: QuantumAttentionType,
    pub position_encoding: PositionEncodingType,
}

Quantum transformer model configuration

Fields

model_dim: usize

Model dimension (d_model)

num_heads: usize

Number of attention heads

ff_dim: usize

Feedforward dimension

num_layers: usize

Number of transformer layers

max_seq_len: usize

Maximum sequence length

num_qubits: usize

Number of qubits for quantum computation

dropout_rate: f64

Dropout rate

attention_type: QuantumAttentionType

Attention mechanism type

position_encoding: PositionEncodingType

Position encoding type

Implementations

impl QuantumTransformerConfig

pub fn default() -> Self

Create default transformer configuration

Examples found in repository

examples/quantum_transformer.rs (line 64)

fn config_demo() -> Result<()> {
    println!("   Creating various transformer configurations...");

    // Small efficient model
    let small_config = QuantumTransformerConfig::small();
    println!(
        "   Small model: {} params, {} heads, {} layers",
        small_config.model_dim, small_config.num_heads, small_config.num_layers
    );

    // Standard model
    let default_config = QuantumTransformerConfig::default();
    println!(
        "   Default model: {} params, {} heads, {} layers",
        default_config.model_dim, default_config.num_heads, default_config.num_layers
    );

    // Large model
    let large_config = QuantumTransformerConfig::large();
    println!(
        "   Large model: {} params, {} heads, {} layers",
        large_config.model_dim, large_config.num_heads, large_config.num_layers
    );

    // Custom configuration
    let custom_config = QuantumTransformerConfig {
        model_dim: 384,
        num_heads: 6,
        ff_dim: 1536,
        num_layers: 8,
        max_seq_len: 1024,
        num_qubits: 12,
        dropout_rate: 0.15,
        attention_type: QuantumAttentionType::QuantumEnhancedMultiHead,
        position_encoding: PositionEncodingType::Rotary,
    };

    println!(
        "   Custom model: {} dim, {} qubits, {:?} attention",
        custom_config.model_dim, custom_config.num_qubits, custom_config.attention_type
    );

    // Create transformer with custom config
    let transformer = QuantumTransformer::new(custom_config)?;
    println!(
        "   Created transformer with {} total parameters",
        transformer.num_parameters()
    );

    Ok(())
}

pub fn large() -> Self

Create configuration for large model

Examples found in repository

examples/quantum_transformer.rs (line 71)

fn config_demo() -> Result<()> {
    println!("   Creating various transformer configurations...");

    // Small efficient model
    let small_config = QuantumTransformerConfig::small();
    println!(
        "   Small model: {} params, {} heads, {} layers",
        small_config.model_dim, small_config.num_heads, small_config.num_layers
    );

    // Standard model
    let default_config = QuantumTransformerConfig::default();
    println!(
        "   Default model: {} params, {} heads, {} layers",
        default_config.model_dim, default_config.num_heads, default_config.num_layers
    );

    // Large model
    let large_config = QuantumTransformerConfig::large();
    println!(
        "   Large model: {} params, {} heads, {} layers",
        large_config.model_dim, large_config.num_heads, large_config.num_layers
    );

    // Custom configuration
    let custom_config = QuantumTransformerConfig {
        model_dim: 384,
        num_heads: 6,
        ff_dim: 1536,
        num_layers: 8,
        max_seq_len: 1024,
        num_qubits: 12,
        dropout_rate: 0.15,
        attention_type: QuantumAttentionType::QuantumEnhancedMultiHead,
        position_encoding: PositionEncodingType::Rotary,
    };

    println!(
        "   Custom model: {} dim, {} qubits, {:?} attention",
        custom_config.model_dim, custom_config.num_qubits, custom_config.attention_type
    );

    // Create transformer with custom config
    let transformer = QuantumTransformer::new(custom_config)?;
    println!(
        "   Created transformer with {} total parameters",
        transformer.num_parameters()
    );

    Ok(())
}

pub fn small() -> Self

Create configuration for small/efficient model

Examples found in repository

examples/quantum_transformer.rs (line 57)

fn config_demo() -> Result<()> {
    println!("   Creating various transformer configurations...");

    // Small efficient model
    let small_config = QuantumTransformerConfig::small();
    println!(
        "   Small model: {} params, {} heads, {} layers",
        small_config.model_dim, small_config.num_heads, small_config.num_layers
    );

    // Standard model
    let default_config = QuantumTransformerConfig::default();
    println!(
        "   Default model: {} params, {} heads, {} layers",
        default_config.model_dim, default_config.num_heads, default_config.num_layers
    );

    // Large model
    let large_config = QuantumTransformerConfig::large();
    println!(
        "   Large model: {} params, {} heads, {} layers",
        large_config.model_dim, large_config.num_heads, large_config.num_layers
    );

    // Custom configuration
    let custom_config = QuantumTransformerConfig {
        model_dim: 384,
        num_heads: 6,
        ff_dim: 1536,
        num_layers: 8,
        max_seq_len: 1024,
        num_qubits: 12,
        dropout_rate: 0.15,
        attention_type: QuantumAttentionType::QuantumEnhancedMultiHead,
        position_encoding: PositionEncodingType::Rotary,
    };

    println!(
        "   Custom model: {} dim, {} qubits, {:?} attention",
        custom_config.model_dim, custom_config.num_qubits, custom_config.attention_type
    );

    // Create transformer with custom config
    let transformer = QuantumTransformer::new(custom_config)?;
    println!(
        "   Created transformer with {} total parameters",
        transformer.num_parameters()
    );

    Ok(())
}

Trait Implementations

impl Clone for QuantumTransformerConfig

fn clone(&self) -> QuantumTransformerConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for QuantumTransformerConfig

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

Formats the value using the given formatter.