Struct QuantumGraphLayer 

pub struct QuantumGraphLayer {
    pub quantum_dim: usize,
    pub input_dim: usize,
    pub output_dim: usize,
    pub rotation_params: Tensor,
    pub entanglement_params: Tensor,
    pub measurement_matrix: Tensor,
    pub training: bool,
}

Quantum-inspired Graph Neural Network Layer

Implements quantum superposition and entanglement concepts for enhanced graph representation learning.

Fields

quantum_dim: usize

Quantum state dimension

input_dim: usize

Input feature dimension

output_dim: usize

Output feature dimension

rotation_params: Tensor

Quantum rotation parameters

entanglement_params: Tensor

Entanglement strength parameters

measurement_matrix: Tensor

Measurement projection matrix

training: bool

Training mode flag

Implementations

impl QuantumGraphLayer

pub fn new( input_dim: usize, output_dim: usize, quantum_dim: usize, ) -> Result<Self, Box<dyn Error>>

Create a new quantum graph layer

pub fn quantum_encoding( &self, features: &Tensor, ) -> Result<QuantumState, Box<dyn Error>>

Encode classical features into quantum state

pub fn quantum_entanglement( &self, state: &QuantumState, adjacency: &Tensor, ) -> Result<QuantumState, Box<dyn Error>>

Apply quantum entanglement operations

pub fn quantum_measurement( &self, state: &QuantumState, ) -> Result<Tensor, Box<dyn Error>>

Perform quantum measurement to extract classical features

pub fn quantum_interference( &self, state: &QuantumState, edge_index: &Tensor, ) -> Result<QuantumState, Box<dyn Error>>

Apply quantum interference patterns based on graph structure

Trait Implementations

impl Clone for QuantumGraphLayer

fn clone(&self) -> QuantumGraphLayer

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for QuantumGraphLayer

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

Formats the value using the given formatter.

impl GraphLayer for QuantumGraphLayer

fn forward(&self, graph: &GraphData) -> GraphData

Forward pass through the layer

fn parameters(&self) -> Vec<Tensor>

Get layer parameters