Unsupervised Machine Learning of Quantum Phase Transitions Using Diffusion Maps
| Autor: | Alexander Lidiak, Zhe-Xuan Gong |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Quantum phase transition
FOS: Computer and information sciences Quantum Physics Statistical Mechanics (cond-mat.stat-mech) Computer science Nonlinear dimensionality reduction Diffusion map General Physics and Astronomy Quantum simulator FOS: Physical sciences Observable Machine Learning (stat.ML) Quantum phases 01 natural sciences Statistics - Machine Learning 0103 physical sciences Unsupervised learning Topological order Statistical physics 010306 general physics Quantum Physics (quant-ph) Condensed Matter - Statistical Mechanics |
| Zdroj: | Physical review letters. 125(22) |
| ISSN: | 1079-7114 |
| Popis: | Experimental quantum simulators have become large and complex enough that discovering new physics from the huge amount of measurement data can be quite challenging, especially when little theoretical understanding of the simulated model is available. Unsupervised machine learning methods are particularly promising in overcoming this challenge. For the specific task of learning quantum phase transitions, unsupervised machine learning methods have primarily been developed for phase transitions characterized by simple order parameters, typically linear in the measured observables. However, such methods often fail for more complicated phase transitions, such as those involving incommensurate phases, valence-bond solids, topological order, and many-body localization. We show that the diffusion map method, which performs nonlinear dimensionality reduction and spectral clustering of the measurement data, has significant potential for learning such complex phase transitions unsupervised. This method works for measurements of local observables in a single basis and is thus readily applicable to many experimental quantum simulators as a versatile tool for learning various quantum phases and phase transitions. Comment: 11 pages, 10 figures. Version published in Physical Review Letters |
| Databáze: | OpenAIRE |
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