Accuracy of Restricted Boltzmann Machines for the one-dimensional $J_1-J_2$ Heisenberg model
Luciano Loris Viteritti, Francesco Ferrari, Federico Becca

TL;DR
This paper evaluates the effectiveness of Restricted Boltzmann Machines in representing the ground states of the one-dimensional $J_1-J_2$ Heisenberg model, demonstrating their flexibility and limitations compared to other variational methods.
Contribution
It introduces complex-valued RBMs for the $J_1-J_2$ model and systematically compares their accuracy to exact solutions and other variational states.
Findings
Fully complex RBMs outperform real-valued variants.
RBMs accurately capture incommensurate correlations and low-energy spectra.
Transferability to larger systems is limited by computational cost.
Abstract
Neural networks have been recently proposed as variational wave functions for quantum many-body systems [G. Carleo and M. Troyer, Science 355, 602 (2017)]. In this work, we focus on a specific architecture, known as Restricted Boltzmann Machine (RBM), and analyse its accuracy for the spin-1/2 antiferromagnetic Heisenberg model in one spatial dimension. The ground state of this model has a non-trivial sign structure, especially for , forcing us to work with complex-valued RBMs. Two variational Ans\"atze are discussed: one defined through a fully complex RBM, and one in which two different real-valued networks are used to approximate modulus and phase of the wave function. In both cases, translational invariance is imposed by considering linear combinations of RBMs, giving access also to the lowest-energy excitations at fixed momentum . We perform a systematic…
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