A Caltech Library Service

Towards Neural Variational Monte Carlo That Scales Linearly with System Size

Sharir, Or and Chan, Garnet Kin-Lic and Anandkumar, Anima (2022) Towards Neural Variational Monte Carlo That Scales Linearly with System Size. .

[img] PDF - Submitted Version
See Usage Policy.


Use this Persistent URL to link to this item:


Quantum many-body problems are some of the most challenging problems in science and are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, coupled with the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. However, the run-time of this approach scales quadratically with the number of simulated particles, constraining the practically usable NN to - in machine learning terms - minuscule sizes (<10M parameters). Considering the many breakthroughs brought by extreme NN in the +1B parameters scale to other domains, lifting this constraint could significantly expand the set of quantum systems we can accurately simulate on classical computers, both in size and complexity. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm - the source of the quadratic scaling. In our preliminary experiments, we demonstrate VQ-NQS ability to reproduce the ground state of the 2D Heisenberg model across various system sizes, while reporting a significant reduction of about ×10 in the number of FLOPs in the local-energy calculation.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Sharir, Or0000-0003-4957-8957
Chan, Garnet Kin-Lic0000-0001-8009-6038
Anandkumar, Anima0000-0002-6974-6797
Record Number:CaltechAUTHORS:20230316-153803430
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120090
Deposited By: George Porter
Deposited On:16 Mar 2023 19:19
Last Modified:16 Mar 2023 19:19

Repository Staff Only: item control page