Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo. Issue 4 (25th January 2021) - Kessler, Jan; Calcavecchia, Francesco; Kühne, Thomas D.
Abstract: Inspired by the universal approximation theorem and widespread adoption of artificial neural network techniques in a diversity of fields, feed‐forward neural networks are proposed as a general purpose trial wave function for quantum Monte Carlo simulations of continuous 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 the trial wave functions, an exactly solvable model system of two trapped interacting particles, as well as the hydrogen dimer, is studied. Abstract : A number of general purpose trial wave functions based on feed‐forward neural networks are proposed for quantum Monte Carlo simulations of bosons and fermions. The behavior and accuracy of the trial wave functions are investigated for an exactly solvable model system of two trapped interacting particles and the hydrogen dimer.