pub struct QCNN {
pub num_qubits: usize,
pub conv_layers: Vec<(QuantumConvFilter, QuantumPooling)>,
pub fc_params: Vec<f64>,
}Expand description
Quantum Convolutional Neural Network
Fields§
§num_qubits: usizeNumber of qubits
conv_layers: Vec<(QuantumConvFilter, QuantumPooling)>Convolutional layers
fc_params: Vec<f64>Final fully connected layer parameters
Implementations§
Source§impl QCNN
impl QCNN
Sourcepub fn new(
num_qubits: usize,
conv_filters: Vec<(usize, usize)>,
pool_sizes: Vec<usize>,
fc_params: usize,
) -> Result<Self, MLError>
pub fn new( num_qubits: usize, conv_filters: Vec<(usize, usize)>, pool_sizes: Vec<usize>, fc_params: usize, ) -> Result<Self, MLError>
Create a new QCNN
Sourcepub fn forward(
&self,
input_state: &Vec<Complex>,
) -> Result<Vec<Complex>, MLError>
pub fn forward( &self, input_state: &Vec<Complex>, ) -> Result<Vec<Complex>, MLError>
Forward pass through the QCNN
Sourcepub fn get_parameters(&self) -> Vec<f64>
pub fn get_parameters(&self) -> Vec<f64>
Get all trainable parameters
Auto Trait Implementations§
impl Freeze for QCNN
impl RefUnwindSafe for QCNN
impl Send for QCNN
impl Sync for QCNN
impl Unpin for QCNN
impl UnwindSafe for QCNN
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T: ?Sized,
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fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
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The inclusion map: converts
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