$2^{1296}$ Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method

Abstract

For decades, people are developing efficient numerical methods for solving the challenging quantum many-body problem, whose Hilbert space grows exponentially with the size of the problem. However, this journey is far from over, as previous methods all have serious limitations. The recently developed deep learning methods provide a very promising new route to solve the long-standing quantum many-body problems. We report that a deep learning based simulation protocol can achieve the solution with state-of-the-art precision in the Hilbert space as large as $2^{1296}$ for spin system and $3^{144}$ for fermion system , using a HPC-AI hybrid framework on the new Sunway supercomputer. With highly scalability up to 40 million heterogeneous cores, our applications have measured 94% weak scaling efficiency and 72% strong scaling efficiency. The accomplishment of this work opens the door to simulate spin models and Fermion models on unprecedented lattice size with extreme high precision.

Massive ground state optimizations of CNN-based wave-functions for $J1$-$J2$ model and $t$-$J$ model carried out on a heterogeneous cores supercomputer

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• Design principles of deep translationally-symmetric neural quantum states for frustrated magnets
• Solving Fermi-Hubbard-type Models by Tensor Representations of Backflow Corrections
• Accelerating two-dimensional tensor network contractions using QR decompositions
• A hybrid method integrating Green's function Monte Carlo and projected entangled pair states
• Optimizing Temperature Distributions for Training Neural Quantum States using Parallel Tempering