Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo

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.

arXiv commentBump to the submitted&accepted version. Many relatively minor changes from previous upload, due to the peer reviewing process

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• Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation
• Tensor Networks for Lattice Gauge Theories beyond one dimension: a Roadmap
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• The quantum Gaussian process state: A kernel-inspired state with quantum support data
• Synergy between deep neural networks and the variational Monte Carlo method for small ${}^{4}H{e}_N$ clusters
• Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions
• Neural-Network Quantum States for Periodic Systems in Continuous Space