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Extracting complexity of quantum dynamics using machine learning ▸ Zala Lenarcic

Автор: Kavli Institute for Theoretical Physics

Загружено: 2021-09-29

Просмотров: 141

Описание: Recorded as part of the 2021 Non-Equilibrium Universality in Many-Body Physics KITP Conference

The advent of quantum simulators have ushered a new era of non-equilibrium many-body physics and has sparked a dialogue between seemingly disparate fields. This conference will explore universal aspects of many-body systems far from equilibrium at the interface of statistical physics, AMO, condensed matter and high-energy physics, by bringing together scientists from these diverse communities. Our focus is on novel phases of matter far from equilibrium and their associated universality classes that emerge beyond equilibrium paradigms. Diverse topics will be covered including short-time universality, entanglement dynamics, and mapping between classical and quantum non-equilibrium systems. Our goal is to identify aspects of high-energy physics that can inform non-equilibrium condensed and AMO systems and vice versa, with an eye towards realizing experiments which can sharpen our understanding of far-from-equilibrium universality

Coordinators: Leticia F. Cugliandolo, Kaden Hazzard, Mohammad Maghrebi, and Jörg Schmiedmayer

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Learn more at: https://www.kitp.ucsb.edu

Follow @KITP_UCSB for updates on Twitter:   / kitp_ucsb  

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The position of the KITP is that ownership and copyright of all online material -- slides, text, audio, video, and podcasts -- belongs to the author. KITP is providing dissemination for these materials but does not claim ownership. Any person citing these materials for scholarly purposes should provide an appropriate scholarly reference.

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Extracting complexity of quantum dynamics using machine learning ▸ Zala Lenarcic

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