Finding Quantum Critical Points with Neural-Network Quantum States
Abstract
Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature. However, quantum many-body systems are notoriously hard to study because the dimension of their Hilbert space increases exponentially with their size. Recently, machine learning tools known as neural-network quantum states have been shown to effectively and efficiently simulate quantum many-body systems. We present an approach to finding the quantum critical points of the quantum Ising model using neural-network quantum states, analytically constructed innate restricted Boltzmann machines, transfer learning and unsupervised learning. We validate the approach and evaluate its efficiency and effectiveness in comparison with other traditional approaches.
Cite
@article{arxiv.2002.02618,
title = {Finding Quantum Critical Points with Neural-Network Quantum States},
author = {Remmy Zen and Long My and Ryan Tan and Frederic Hebert and Mario Gattobigio and Christian Miniatura and Dario Poletti and Stephane Bressan},
journal= {arXiv preprint arXiv:2002.02618},
year = {2020}
}
Comments
19 pages, 12 figures, extended version of an accepted paper at the 24th European Conference on Artificial Intelligence (ECAI 2020)