Machine learning the dynamics of quantum kicked rotor

Journal Article · Wed Dec 15 00:00:00 EST 2021 · Annals of Physics

DOI:https://doi.org/10.1016/J.AOP.2021.168500· OSTI ID:23183181

Mano, Tomohiro; Ohtsuki, Tomi ^[1]

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1. Physics Division, Sophia University, Kioicho 7-1, Chiyoda-ku, Tokyo, 102-8554 (Japan)

Highlights: • Quantum kicked rotor. • Quantum phase transition. • Machine learning. • LSTM network. • Convolutional neural network. Using the multilayer convolutional neural network (CNN), we can detect the quantum phases in random electron systems, and phase diagrams of two and higher dimensional Anderson transitions and quantum percolations as well as disordered topological systems have been obtained. Here, instead of using CNN to analyze the wave functions, we analyze the dynamics of wave packets via long short-term memory network (LSTM). We adopt the quasi-periodic quantum kicked rotors, which simulate the three and four dimensional Anderson transitions. By supervised training, we let LSTM extract the features of the time series of wave packet displacements in localized and delocalized phases. We then simulate the wave packets in unknown phases and let LSTM classify the time series to localized and delocalized phases. We compare the phase diagrams obtained by LSTM and those obtained by CNN.

• OSTI ID: 23183181
• Journal Information: Annals of Physics, Vol. 435; Other Information: Copyright (c) 2021 Elsevier Inc. All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0003-4916
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
97 MATHEMATICAL METHODS AND COMPUTING
ELECTRONS
FOUR-DIMENSIONAL CALCULATIONS
MACHINE LEARNING
NEURAL NETWORKS
PHASE DIAGRAMS
PHASE TRANSFORMATIONS
ROTORS
TOPOLOGY
WAVE FUNCTIONS
WAVE PACKETS