Reinforcement Learning in Different Phases of Quantum Control

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ID: 60128
2018
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Abstract
The ability to prepare a physical system in a desired quantum state is central to many areas of physics such as nuclear magnetic resonance, cold atoms, and quantum computing. Yet, preparing states quickly and with high fidelity remains a formidable challenge. In this work, we implement cutting-edge reinforcement learning (RL) techniques and show that their performance is comparable to optimal control methods in the task of finding short, high-fidelity driving protocol from an initial to a target state in nonintegrable many-body quantum systems of interacting qubits. RL methods learn about the underlying physical system solely through a single scalar reward (the fidelity of the resulting state) calculated from numerical simulations of the physical system. We further show that quantum-state manipulation viewed as an optimization problem exhibits a spin-glass-like phase transition in the space of protocols as a function of the protocol duration. Our RL-aided approach helps identify variational protocols with nearly optimal fidelity, even in the glassy phase, where optimal state manipulation is exponentially hard. This study highlights the potential usefulness of RL for applications in out-of-equilibrium quantum physics.
Reference Key
bukov2018reinforcementphysical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bukov, Marin;Day, Alexandre G. R.;Sels, Dries;Weinberg, Phillip;Polkovnikov, Anatoli;Mehta, Pankaj;
Journal physical review x
Year 2018
DOI
DOI not found
URL
http://doi.org/10.1103/PhysRevX.8.031086
Keywords
microscopy Materials of engineering and construction. Mechanics of materials Technology (General) Chemical technology Engineering (General). Civil engineering (General) Technology Electrical engineering. Electronics. Nuclear engineering Descriptive and experimental mechanics physics electronic computers. computer science heat

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