Bridging the Gap between Deep Learning and Frustrated Quantum Spin System for Extreme-scale Simulations on New Generation of Sunway Supercomputer

TL;DR

This paper introduces a CNN-based method for simulating large-scale frustrated quantum spin systems, achieving high accuracy and scalability on a supercomputer, thus advancing quantum many-body simulations.

Contribution

The paper presents a novel CNN approach combined with transfer learning for large-scale quantum spin simulations, enabling studies on unprecedented lattice sizes with high efficiency.

Findings

Successfully simulated a 24x24 lattice quantum system

Achieved high accuracy in representing quantum states

Demonstrated scalability on 30 million cores of Sunway supercomputer

Abstract

Efficient numerical methods are promising tools for delivering unique insights into the fascinating properties of physics, such as the highly frustrated quantum many-body systems. However, the computational complexity of obtaining the wave functions for accurately describing the quantum states increases exponentially with respect to particle number. Here we present a novel convolutional neural network (CNN) for simulating the two-dimensional highly frustrated spin-$1/2$ $J_1-J_2$ Heisenberg model, meanwhile the simulation is performed at an extreme scale system with low cost and high scalability. By ingenious employment of transfer learning and CNN's translational invariance, we successfully investigate the quantum system with the lattice size up to $24\times24$, within 30 million cores of the new generation of sunway supercomputer. The final achievement demonstrates the effectiveness of CNN-based representation of quantum-state and brings the state-of-the-art record up to a brand-new level from both aspects of remarkable accuracy and unprecedented scales.