Work

Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

Year: 2021

Type: preprint

Abstract: We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics. In contrast to the deep architectures used so far for this task, our proposal is based on a shallow design and incorporates the symmetries of the problem. We test our model on the $ϕ^4$ theory, showing that it systematically outperforms a realNVP baseline in sampling efficiency, with the difference between the two increasing for larger lattices. On the largest lattice we consider, of size $32\times 32$, we improve a key metric, the effective sample size, from 1% to 66% w.r.t. the realNVP baseline.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2110.02673

Authors: de Haan, Pim, Rainone, Corrado, Cheng, Miranda C. N., Bondesan, Roberto

Institutions: University of Amsterdam, Research Center for Applied Science, Academia Sinica, Institute of Mathematics, Academia Sinica, Institute of Physics

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3203254982

Cites: 22

Cited by: 11

Related to: 20