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Cited by 267

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Minimizing irreversible losses in quantum systems by local counterdiabatic driving

Open access
Sels, Dries, Polkovnikov, Anatoli·2017·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1619826114
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Abstract

Significance: Losses are ubiquitous in manipulating complex systems. They arise from our lack of control on the microscopic degrees of freedom of the system. A universal way to minimize losses is to consider adiabatic processes. These processes are, however, very slow, which significantly limits their power. In this work, we show how to speed up these protocols for general complex (quantum) systems. Although dissipation cannot be avoided, we show how it can be reduced significantly with only local access to the system. Applications range from quantum information technologies to preparing experiments and even controlling complicated classical systems, such as those found in nature.

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Metadata

Authors
Sels, D., & Polkovnikov, A.
Venue
Proceedings of the National Academy of Sciences
Publisher
Proceedings of the National Academy of Sciences
Published
2017-05
Volume
114
Issue
20
Pages
E3909-E3916
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.1619826114
OpenAlex
W2488616581
Cited by
267
References
24
FWCI
12.96
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Minimizing irreversible losses in quantum systems by local counterdiabatic driving. https://app.cochat.ai/papers/10.1073/pnas.1619826114
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Sels, D., & Polkovnikov, A. (2017). Minimizing irreversible losses in quantum systems by local counterdiabatic driving. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1619826114

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.