Learning quantum systems via out-of-time-order correlators

• URL: http://arxiv.org/abs/2208.02254v1
• Date: Wed, 3 Aug 2022 18:00:00 GMT
• Title: Learning quantum systems via out-of-time-order correlators
• Authors: Thomas Schuster and Murphy Niu and Jordan Cotler and Thomas O'Brien and Jarrod R. McClean and Masoud Mohseni
• Abstract summary: We show that out-of-time-order correlators can substantially improve the learnability of strongly-interacting systems. We numerically characterize these advantages across a variety of learning problems, and find that they are robust to both read-out error and decoherence.
• Score: 0.27961972519572437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning the properties of dynamical quantum systems underlies applications ranging from nuclear magnetic resonance spectroscopy to quantum device characterization. A central challenge in this pursuit is the learning of strongly-interacting systems, where conventional observables decay quickly in time and space, limiting the information that can be learned from their measurement. In this work, we introduce a new class of observables into the context of quantum learning -- the out-of-time-order correlator -- which we show can substantially improve the learnability of strongly-interacting systems by virtue of displaying informative physics at large times and distances. We identify two general scenarios in which out-of-time-order correlators provide a significant advantage for learning tasks in locally-interacting systems: (i) when experimental access to the system is spatially-restricted, for example via a single "probe" degree of freedom, and (ii) when one desires to characterize weak interactions whose strength is much less than the typical interaction strength. We numerically characterize these advantages across a variety of learning problems, and find that they are robust to both read-out error and decoherence. Finally, we introduce a binary classification task that can be accomplished in constant time with out-of-time-order measurements. In a companion paper, we prove that this task is exponentially hard with any adaptive learning protocol that only involves time-ordered operations.

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