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arXiv · 2002.02618

Finding Quantum Critical Points with Neural-Network Quantum States

Remmy Zen·Long My·Ryan Tan·Frederic Hebert·Mario Gattobigio·Christian Miniatura·Dario Poletti·Stephane Bressan
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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.

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Remmy Zen, Long My, Ryan Tan, Frederic Hebert, Mario Gattobigio, Christian Miniatura, Dario Poletti, Stephane Bressan. 2020-02-07. Finding Quantum Critical Points with Neural-Network Quantum States. https://arxiv.org/abs/2002.02618

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