Reinforcement Learning in Different Phases of Quantum Control

By Marin Bukov, Alexandre G. R. Day, Dries Sels, Phillip Weinberg, A. Polkovnikov, Pankaj Mehta. Physical Review X, 2017

399 citations recorded by the source corpus.

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 non-integrable 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.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/ef45dc196af747363b610e7e16f9f555ad7915af

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In Atomic and Molecular Physics, and Optics

Citations in the corpus

A high-bias, low-variance introduction to Machine Learning for physicists (2018, neutral): “…algorithms, Reinforcement Learning has only recently taken off in physics (Albarran-Arriagada et al., 2018; August and Hernández-Lobato, 2018; Bukov, 2018; Bukov et al., 2018; Cárdenas-López et al., 2017; Chen et al., 2014; Chen and Xue, 2019; Dunjko et al., 2017; Fösel et al., 2018; Lamata,…”
Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe (2022, background): “In a system of coupled spins with bang-bang controls, RL has been shown to lead to a quasi-optimal fidelity [99].”
Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices (2018, neutral): “, [67]).”