Physics Inspired Neural Networks Enable Efficient Graph Coloring
The model uses a physics-inspired neural framework combining Graph Neural Networks with statistical mechanics to solve large-scale graph coloring problems. It learns a mapping that minimizes a Hamiltonian, using noise annealing and specific loss functions to navigate complex energy landscapes. This approach demonstrates that neural networks can learn scalable algorithmic strategies for hard combinatorial problems.