Taming Quantum Systems: A Tutorial for Using Shortcuts-To-Adiabaticity, Quantum Optimal Control, and Reinforcement Learning

Precise manipulation of quantum effects at the atomic and nanoscale has become an essential task in ongoing scientific and technological endeavors. Quantum control methods are thus routinely exploited for research in areas such as quantum materials, quantum chemistry, and atomic and molecular physics, as well as in the development of quantum technologies like computing, simulation, and sensing. Here, we present a pedagogical introduction to the basics of quantum control methods in tutorial form, with the aim of providing newcomers to the field with the core concepts and practical tools to use these methods in their research. We focus on three areas: shortcuts to adiabaticity, quantum optimal control, and machine-learning-based control. We lay out the basic theoretical elements of each area in a pedagogical way and describe their application to a series of example cases. For these, we include detailed analytical derivations as well as extensive numerical results. As an outlook, we discuss quantum control methods in the broader context of quantum technologies development and complex quantum systems research, outlining potential connections and synergies between them.

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Publication Details

Journal
PRX Quantum
Published
2025-09-08
DOI
https://doi.org/10.1103/j8c7-v2hd
Citations
6
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
14.89
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article

Taming Quantum Systems: A Tutorial for Using Shortcuts-To-Adiabaticity, Quantum Optimal Control, and Reinforcement Learning

N. T. Zinner, Marin Bukov, Pablo M. Poggi, Steve Campbell et al.
6 citations
PRX Quantum
Quantum Computing Algorithms and Architecture
14.89
article

Taming Quantum Systems: A Tutorial for Using Shortcuts-To-Adiabaticity, Quantum Optimal Control, and Reinforcement Learning

N. T. Zinner, Marin Bukov, Pablo M. Poggi, Steve Campbell, Callum W. Duncan
article en
6 citations

Abstract

Precise manipulation of quantum effects at the atomic and nanoscale has become an essential task in ongoing scientific and technological endeavors. Quantum control methods are thus routinely exploited for research in areas such as quantum materials, quantum chemistry, and atomic and molecular physics, as well as in the development of quantum technologies like computing, simulation, and sensing. Here, we present a pedagogical introduction to the basics of quantum control methods in tutorial form, with the aim of providing newcomers to the field with the core concepts and practical tools to use these methods in their research. We focus on three areas: shortcuts to adiabaticity, quantum optimal control, and machine-learning-based control. We lay out the basic theoretical elements of each area in a pedagogical way and describe their application to a series of example cases. For these, we include detailed analytical derivations as well as extensive numerical results. As an outlook, we discuss quantum control methods in the broader context of quantum technologies development and complex quantum systems research, outlining potential connections and synergies between them.

PRX QuantumVol. 6(4)
University College Dublin (IE), University of New Mexico (US), University of Strathclyde (GB), Aarhus University (DK), Kvant (Slovakia) (SK), Max Planck Institute for the Physics of Complex Systems (DE), Kjobenhavns Telefon Aktieselskab (Denmark) (DK), Freie Universität Berlin (DE), University of Sheffield (GB)
Openalex Percentile: Top 1%
Quantum Computing Algorithms and Architecture
14.89
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Taming Quantum Systems: A Tutorial for Using Shortcuts-To-Adiabaticity, Quantum Optimal Control, and Reinforcement Learning — N. T. Zinner, Marin Bukov, et al. · PRX Quantum (2025) | TGRS Research Map | TGRS