Finding the ground states of spin glasses relevant for disordered magnets and many other physical systems is computationally challenging. The authors propose here a deep reinforcement learning framework for calculating the ground states, which can be trained on small-scale spin glass instances and then applied to arbitrarily large ones.
- Changjun Fan
- Mutian Shen
- Yang-Yu Liu