AI for Quantum Mechanics: High Performance Quantum Many-Body Simulations via Deep Learning

Solving quantum many-body problems is one of the most fascinating research fields in condensed matter physics. An efficient numerical method is crucial to understand the mechanism of novel physics, such as the high Tc superconductivity, as one has to find the optimal solution in the exponentially large Hilbert space. The development of Artificial Intelligence (AI) provides a unique opportunity to solve the quantum many-body problems, but there is still a large gap from the goal. In this work, we present a novel computational framework, and adapt it to the Sunway supercomputer. With highly efficient scalability up to 40 million heterogeneous cores, we can drastically increase the number of variational parameters, which greatly improves the accuracy of the solutions. The investigations of the spin-1/2 J1-J2 model and the t-J model achieve unprecedented accuracy and time-to-solution far beyond the previous state of the art.