Unsupervised phase discovery with deep anomaly detection
- URL: http://arxiv.org/abs/2003.09905v2
- Date: Thu, 18 Mar 2021 14:31:39 GMT
- Title: Unsupervised phase discovery with deep anomaly detection
- Authors: Korbinian Kottmann, Patrick Huembeli, Maciej Lewenstein, Antonio Acin
- Abstract summary: We demonstrate how to explore phase diagrams with automated and unsupervised machine learning.
We employ deep neural networks to determine the entire phase diagram in a completely unsupervised and automated fashion.
Our method allows us to reveal a phase-separated region between supersolid and superfluid parts with unexpected properties.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We demonstrate how to explore phase diagrams with automated and unsupervised
machine learning to find regions of interest for possible new phases. In
contrast to supervised learning, where data is classified using predetermined
labels, we here perform anomaly detection, where the task is to differentiate a
normal data set, composed of one or several classes, from anomalous data. Asa
paradigmatic example, we explore the phase diagram of the extended Bose Hubbard
model in one dimension at exact integer filling and employ deep neural networks
to determine the entire phase diagram in a completely unsupervised and
automated fashion. As input data for learning, we first use the entanglement
spectra and central tensors derived from tensor-networks algorithms for
ground-state computation and later we extend our method and use experimentally
accessible data such as low-order correlation functions as inputs. Our method
allows us to reveal a phase-separated region between supersolid and superfluid
parts with unexpected properties, which appears in the system in addition to
the standard superfluid, Mott insulator, Haldane-insulating, and density wave
phases.
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