Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 015033 |
| Fachzeitschrift | Quantum Science and Technology |
| Jahrgang | 10 |
| Ausgabenummer | 1 |
| Frühes Online-Datum | 19 Nov. 2024 |
| Publikationsstatus | Veröffentlicht - 1 Jan. 2025 |
Abstract
State engineering of quantum objects is a central requirement for precision sensing and quantum computing implementations. When the quantum dynamics can be described by analytical solutions or simple approximation models, optimal state preparation protocols have been theoretically proposed and experimentally realized. For more complex systems such as interacting quantum gases, simplifying assumptions do not apply anymore and the optimization techniques become computationally impractical. Here, we propose Bayesian optimization based on multi-output Gaussian processes to learn the physical properties of a Bose-Einstein condensate within few simulations only. We evaluate its performance on an optimization study case of diabatically transporting the quantum gas while keeping it in its ground state. Within a few hundred executions, we reach a competitive performance to other protocols. While restricting this benchmark to the well known Thomas-Fermi approximation for straightforward comparisons, we expect a similar performance when employing more complex theoretical models, which would be computationally more challenging, rendering standard optimal control theory protocols impractical. This paves the way for efficient state engineering of complex quantum systems including mixtures of interacting gases or cold molecules.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Atom- und Molekularphysik sowie Optik
- Werkstoffwissenschaften (insg.)
- Werkstoffwissenschaften (sonstige)
- Physik und Astronomie (insg.)
- Physik und Astronomie (sonstige)
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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in: Quantum Science and Technology, Jahrgang 10, Nr. 1, 015033, 01.01.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Bayesian optimization for state engineering of quantum gases
AU - Müller, Gabriel
AU - Martínez-Lahuerta, Víctor J.
AU - Sekulic, Ivan
AU - Burger, Sven
AU - Schneider, Philipp Immanuel
AU - Gaaloul, Naceur
N1 - Publisher Copyright: © 2024 The Author(s). Published by IOP Publishing Ltd.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - State engineering of quantum objects is a central requirement for precision sensing and quantum computing implementations. When the quantum dynamics can be described by analytical solutions or simple approximation models, optimal state preparation protocols have been theoretically proposed and experimentally realized. For more complex systems such as interacting quantum gases, simplifying assumptions do not apply anymore and the optimization techniques become computationally impractical. Here, we propose Bayesian optimization based on multi-output Gaussian processes to learn the physical properties of a Bose-Einstein condensate within few simulations only. We evaluate its performance on an optimization study case of diabatically transporting the quantum gas while keeping it in its ground state. Within a few hundred executions, we reach a competitive performance to other protocols. While restricting this benchmark to the well known Thomas-Fermi approximation for straightforward comparisons, we expect a similar performance when employing more complex theoretical models, which would be computationally more challenging, rendering standard optimal control theory protocols impractical. This paves the way for efficient state engineering of complex quantum systems including mixtures of interacting gases or cold molecules.
AB - State engineering of quantum objects is a central requirement for precision sensing and quantum computing implementations. When the quantum dynamics can be described by analytical solutions or simple approximation models, optimal state preparation protocols have been theoretically proposed and experimentally realized. For more complex systems such as interacting quantum gases, simplifying assumptions do not apply anymore and the optimization techniques become computationally impractical. Here, we propose Bayesian optimization based on multi-output Gaussian processes to learn the physical properties of a Bose-Einstein condensate within few simulations only. We evaluate its performance on an optimization study case of diabatically transporting the quantum gas while keeping it in its ground state. Within a few hundred executions, we reach a competitive performance to other protocols. While restricting this benchmark to the well known Thomas-Fermi approximation for straightforward comparisons, we expect a similar performance when employing more complex theoretical models, which would be computationally more challenging, rendering standard optimal control theory protocols impractical. This paves the way for efficient state engineering of complex quantum systems including mixtures of interacting gases or cold molecules.
KW - Bayesian optimization
KW - quantum control
KW - quantum engineering
KW - quantum gases
KW - quantum metrology
KW - quantum sensing
UR - http://www.scopus.com/inward/record.url?scp=85219381374&partnerID=8YFLogxK
U2 - 10.1088/2058-9565/ad9050
DO - 10.1088/2058-9565/ad9050
M3 - Article
AN - SCOPUS:85219381374
VL - 10
JO - Quantum Science and Technology
JF - Quantum Science and Technology
SN - 2058-9565
IS - 1
M1 - 015033
ER -