Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
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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× 32, we improve a key metric, the effective sample size, from 1
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