Quantum LBMNet for clasification

Quantum-LBMNet is a novel quantum-inspired extension of LBMNet that processes images using complex-valued (real and imaginary) feature representations and lattice Boltzmann dynamics. By combining physics-inspired computation with quantum-inspired encoding, the architecture efficiently captures directional and spatial information before generating compact feature representations for accurate image classification. Its lightweight design offers an excellent balance between computational efficiency, scalability, and predictive performance.