🏵️Harnessing AI for Physics plus AI Expansion

Disordered systems like spin glasses, with their randomness and conflicting interactions, pose a long-standing challenge in physics and computation, holding keys to fundamental principles and diverse field connections. Finding their ground states is crucial for understanding complex matter and tackling difficult optimization problems. Traditional algorithms struggle with their complexity. However, a new AI-driven framework using deep reinforcement learning shows remarkable ability in navigating spin glass energy landscapes. This scalable approach enhances thermal annealing and offers a promising path to understanding low-temperature phases, forging a link between physics and AI, potentially solving hard combinatorial problems. This synergy marks a significant step in addressing fundamental physics questions and computational challenges.