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arXiv · 1904.10251

Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo

Jan Kessler·Francesco Calcavecchia·Thomas D. Kühne
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Abstract

Inspired by the universal approximation theorem and widespread adoption of artificial neural network techniques in a diversity of fields, we propose feed-forward neural networks as a general purpose trial wave function for quantum Monte Carlo simulations of continous many-body systems. Whereas for simple model systems the whole many-body wave function can be represented by a neural network, the antisymmetry condition of non-trivial fermionic systems is incorporated by means of a Slater determinant. To demonstrate the accuracy of our trial wave functions, we have studied an exactly solvable model system of two trapped interacting particles, as well as the hydrogen dimer.

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Jan Kessler, Francesco Calcavecchia, Thomas D. Kühne. 2019-04-23. Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo. https://doi.org/10.1002/adts.202000269

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