Struct QVAE 

pub struct QVAE {
    pub num_data_qubits: usize,
    pub num_latent_qubits: usize,
    pub num_ancilla_qubits: usize,
    pub encoder_params: Vec<f64>,
    pub decoder_params: Vec<f64>,
}

Quantum Variational Autoencoder

Fields

num_data_qubits: usize

Number of data qubits

num_latent_qubits: usize

Number of latent qubits (compressed representation)

num_ancilla_qubits: usize

Number of ancilla qubits for encoding

encoder_params: Vec<f64>

Encoder parameters

decoder_params: Vec<f64>

Decoder parameters

Implementations

impl QVAE

pub fn new( num_data_qubits: usize, num_latent_qubits: usize, num_ancilla_qubits: usize, ) -> Result<Self, MLError>

Create a new quantum variational autoencoder

pub fn total_qubits(&self) -> usize

Get total number of qubits required

pub fn encode<const N: usize>( &self, circuit: &mut Circuit<N>, data_start: usize, latent_start: usize, ) -> Result<(), MLError>

Apply encoding circuit

pub fn decode<const N: usize>( &self, circuit: &mut Circuit<N>, latent_start: usize, output_start: usize, ) -> Result<(), MLError>

Apply decoding circuit

pub fn build_circuit<const N: usize>(&self) -> Result<Circuit<N>, MLError>

Build full autoencoder circuit

pub fn reconstruction_fidelity( &self, input_state: &[Complex], output_state: &[Complex], ) -> Result<f64, MLError>

Compute reconstruction fidelity

pub fn get_parameters(&self) -> Vec<f64>

Get all trainable parameters

pub fn set_parameters(&mut self, params: &[f64]) -> Result<(), MLError>

Set parameters from a flat vector

pub fn compute_loss( &self, input_states: &[Vec<Complex>], lambda: f64, ) -> Result<f64, MLError>

Compute loss function (negative fidelity + regularization)