  Spin glasses are disordered magnets with random interactions that are,
generally, in conflict with each other. Finding the ground states of spin
glasses is not only essential for the understanding of the nature of disordered
magnetic and other physical systems, but also useful to solve a broad array of
hard combinatorial optimization problems across multiple disciplines. Despite
decades-long efforts, an algorithm with both high accuracy and high efficiency
is still lacking. Here we introduce DIRAC - a deep reinforcement learning
framework, which can be trained purely on small-scale spin glass instances and
then applied to arbitrarily large ones. DIRAC displays better scalability than
other methods and can be leveraged to enhance any thermal annealing method.
Extensive calculations on 2D, 3D and 4D Edwards-Anderson spin glass instances
demonstrate the superior performance of DIRAC over existing methods. As many
hard combinatorial optimization problems have Ising spin glass formulations,
our results suggest a promising tool in solving these hard problems. Moreover,
the presented algorithm will help us better understand the nature of the
low-temperature spin-glass phase, which is a fundamental challenge in
statistical physics.
