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Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
Pim de Haan · Roberto Bondesan

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in Machine Learning and the Physical Sciences »

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 ϕ⁴ 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 x 32, we improve a key metric, the effective sample size, from 1% to 66% w.r.t. the realNVP baseline.

Author Information

Pim de Haan (Qualcomm AI Research, University of Amsterdam)
Roberto Bondesan (Qualcomm AI Research)

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