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May 2020: Expressibility and trainability of parameterized analog quantum systems for machine learning applications

Expressibility and trainability of parameterized analog quantum systems for machine learning applications
J. Tangpanitanon, S. Thanasilp, M. A. Lemonde, N. Dangniam, D. G. Angelakis
arxiv.org/2005.11222 

In this work we analyze the trainability of analog quantum processors and applications in machine learning. We investigate how the interplay between external driving and disorder can dictate the trainability and expressibility of quantum many body systems and apply it to solve a generative modelling problem. We show that if the system thermalizes, the training fails at the expense of the a large expressibility, while the opposite happens when the system enters the many-body localized (MBL) phase.Our approach can be implemented with a variety of available quantum platforms including cold ions, atoms and superconducting circuits

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