Struct QuantumVariationalDiffusion 

pub struct QuantumVariationalDiffusion { /* private fields */ }

Variational Diffusion Model with quantum components

Implementations

impl QuantumVariationalDiffusion

pub fn new( data_dim: usize, latent_dim: usize, num_qubits: usize, ) -> Result<Self>

Create new variational diffusion model

Examples found in repository

examples/quantum_diffusion.rs (lines 309-313)

fn advanced_diffusion_demo() -> Result<()> {
    println!("\n6. Advanced Diffusion Techniques:");

    // Conditional generation
    println!("\n   a) Conditional Generation:");
    let model = QuantumDiffusionModel::new(4, 4, 50, NoiseSchedule::Cosine { s: 0.008 })?;
    let condition = Array1::from_vec(vec![0.5, -0.5]);
    let conditional_samples = model.conditional_generate(&condition, 5)?;

    println!(
        "   Generated {} conditional samples",
        conditional_samples.nrows()
    );
    println!("   Condition: [{:.3}, {:.3}]", condition[0], condition[1]);

    // Variational diffusion
    println!("\n   b) Variational Diffusion Model:");
    let vdm = QuantumVariationalDiffusion::new(
        4, // data_dim
        2, // latent_dim
        4, // num_qubits
    )?;

    let x = Array1::from_vec(vec![0.1, 0.2, 0.3, 0.4]);
    let (mean, log_var) = vdm.encode(&x)?;

    println!("   Encoded data to latent space:");
    println!("   - Input: {:?}", x.as_slice().unwrap());
    println!("   - Latent mean: [{:.3}, {:.3}]", mean[0], mean[1]);
    println!(
        "   - Latent log_var: [{:.3}, {:.3}]",
        log_var[0], log_var[1]
    );

    Ok(())
}

pub fn encode(&self, x: &Array1<f64>) -> Result<(Array1<f64>, Array1<f64>)>

Encode data to latent space

Examples found in repository

examples/quantum_diffusion.rs (line 316)

fn advanced_diffusion_demo() -> Result<()> {
    println!("\n6. Advanced Diffusion Techniques:");

    // Conditional generation
    println!("\n   a) Conditional Generation:");
    let model = QuantumDiffusionModel::new(4, 4, 50, NoiseSchedule::Cosine { s: 0.008 })?;
    let condition = Array1::from_vec(vec![0.5, -0.5]);
    let conditional_samples = model.conditional_generate(&condition, 5)?;

    println!(
        "   Generated {} conditional samples",
        conditional_samples.nrows()
    );
    println!("   Condition: [{:.3}, {:.3}]", condition[0], condition[1]);

    // Variational diffusion
    println!("\n   b) Variational Diffusion Model:");
    let vdm = QuantumVariationalDiffusion::new(
        4, // data_dim
        2, // latent_dim
        4, // num_qubits
    )?;

    let x = Array1::from_vec(vec![0.1, 0.2, 0.3, 0.4]);
    let (mean, log_var) = vdm.encode(&x)?;

    println!("   Encoded data to latent space:");
    println!("   - Input: {:?}", x.as_slice().unwrap());
    println!("   - Latent mean: [{:.3}, {:.3}]", mean[0], mean[1]);
    println!(
        "   - Latent log_var: [{:.3}, {:.3}]",
        log_var[0], log_var[1]
    );

    Ok(())
}

pub fn decode(&self, z: &Array1<f64>) -> Result<Array1<f64>>

Decode from latent space