Machine learning the dynamics of quantum kicked rotor

Mano, Tomohiro; Ohtsuki, Tomi

doi:10.1016/j.aop.2021.168500

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…Recently, the long short-term memory (LSTM) network has been exploited to extract the character of time series and thus to predict the phase diagram of quantum diffusion [80]. Based on the character of the time evolution of N , we conducted supervised training on the LSTM network and used it to evaluate the feature of N (t), namely, whether N (t) = e µt or not, for different system parameters.…”

Section: B Spontaneous Pt -Symmetry Breakingmentioning

confidence: 99%

Scaling laws of the out-of-time-order correlators at the transition to the spontaneous $\cal{PT}$-symmetry breaking in a Floquet system

Zhao¹,

Wang²,

Han³

et al. 2023

Preprint

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We investigate both numerically and analytically the dynamics of out-of-time-order correlators (OTOCs) in a non-Hermitian kicked rotor model, addressing the scaling laws of the time dependence of OTOCs at the transition to the spontaneous PT symmetry breaking. In the unbroken phase of PT symmetry, the OTOCs increase monotonically and eventually saturate with time, demonstrating the freezing of information scrambling. Just beyond the phase transition points, the OTOCs increase in the power-laws of time, with the exponent larger than two. Interestingly, the quadratic growth of OTOCs with time emerges when the system is far beyond the phase transition points. Above numerical findings have been validated by our theoretical analysis, which provides a general framework with important implications for Floquet engineering and the information scrambling in chaotic systems.

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Section: B Spontaneous Pt -Symmetry Breakingmentioning

confidence: 99%