Journal
PHYSICAL REVIEW LETTERS
Volume 125, Issue 17, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.125.170603
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Funding
- European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant [665884, 713729]
- Spanish Ministry MINECO (National Plan 15) [FIS2016-79508-P, SEV-2015-0522]
- European Social Fund
- Fundacio Cellex and Mir-Puig
- Generalitat de Catalunya (AGAUR) [SGR 1341, SGR1381]
- Generalitat de Catalunya (QuantumCAT)
- Generalitat de Catalunya (CERCA/Program)
- ERC AdG NOQIA
- ERC AdG CERQUTE
- AXA Chair in Quantum Information Science
- National Science Centre, Poland-Symfonia Grant [2016/20/W/ST4/00314]
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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 dataset, composed of one or several classes, from anomalous data. As a 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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