Waveflow: boundary-conditioned normalizing flows applied to fermionic wavefunctions
Luca Thiede, Chong Sun, Al\'an Aspuru-Guzik

TL;DR
Waveflow introduces a novel boundary-conditioned normalizing flow framework for modeling fermionic wavefunctions, overcoming limitations of Slater determinants and effectively learning complex many-body electronic states.
Contribution
It proposes a new wavefunction ansatz using boundary-conditioned normalizing flows with O-spline priors to handle topological mismatches, enhancing expressiveness over traditional methods.
Findings
Successfully models ground-state wavefunctions in a 1D many-electron system.
Addresses topological mismatch with O-spline priors and I-spline bijections.
Demonstrates effective energy minimization via variational quantum Monte Carlo.
Abstract
An efficient and expressive wavefunction ansatz is key to scalable solutions for complex many-body electronic structures. While Slater determinants are predominantly used for constructing antisymmetric electronic wavefunction ans\"{a}tze, this construction can result in limited expressiveness when the targeted wavefunction is highly complex. In this work, we introduce Waveflow, an innovative framework for learning many-body fermionic wavefunctions using boundary-conditioned normalizing flows. Instead of relying on Slater determinants, Waveflow imposes antisymmetry by defining the fundamental domain of the wavefunction and applying necessary boundary conditions. A key challenge in using normalizing flows for this purpose is addressing the topological mismatch between the prior and target distributions. We propose using O-spline priors and I-spline bijections to handle this mismatch,…
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Taxonomy
TopicsComputational Physics and Python Applications
