Finding Quantum Critical Points with Neural-Network Quantum States
- URL: http://arxiv.org/abs/2002.02618v1
- Date: Fri, 7 Feb 2020 04:39:09 GMT
- Title: Finding Quantum Critical Points with Neural-Network Quantum States
- Authors: Remmy Zen, Long My, Ryan Tan, Frederic Hebert, Mario Gattobigio,
Christian Miniatura, Dario Poletti, Stephane Bressan
- Abstract summary: We present an approach to finding the quantum critical points of the quantum Ising model using neural-network quantum states.
We analytically constructed innate restricted Boltzmann machines, transfer learning and unsupervised learning.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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