Training of quantum circuits on a hybrid quantum computer
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We train generative modeling circuits on a quantum hybrid computer showing an optimization strategy and a resource trade-off.
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QCBMs are well suited to be run on current NISQ hardware and can serve as benchmarks.
“QCBMs are well suited to be run on current NISQ hardware and can serve as benchmarks (Hamilton et al. ,”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Zhu et al. , 2019
Generative models such as the QCBM can serve as benchmarks for NISQ devices.
“Generative models such as the QCBM (see Sec. VI.B.2) can also serve as benchmarks for NISQ devices (Hamilton et al. , 2019; Leyton-Ortega et al. , 2019; Zhu et al. , 2019).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Zhu et al., 2019
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References 45 · Cited by 215
Research works printed but not linked: 20
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- Authors
- Zhu, D., Linke, N. M., Benedetti, M., Landsman, K. A., Nguyen, N. H., Alderete, C. H., Perdomo-Ortiz, A., Korda, N., Garfoot, A., Brecque, C., Egan, L., Perdomo, O., & Monroe, C.
- Affiliations
- Joint Center for Quantum Information and Computer Science (US)
- Cambridge Quantum Computing (United Kingdom) (GB)
- Department of Computer Science, University College London, WC1E 6BT London, UK.
- Mind Foundry Limited, OX2 7DD Oxford, UK.
- Central Connecticut State University (US)
- Corresponding author
- Zhu, D.
- ORCID
- Venue
- Science Advances
- Published
- 2019-10-11
- Volume
- 5
- Issue
- 10
- Pages
- eaaw9918-eaaw9918
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC6799983
- OpenAlex
- W2980456669
- Cited by
- 215
- References
- 45
- Citations by year
- 2026: 14 · 2025: 14 · 2024: 30 · 2023: 36 · 2022: 29 · 2021: 55 · 2020: 28 · 2019: 9
- FWCI
- 13.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Science Advances (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- APS Division of Atomic, Molecular and Optical Physics Meeting Abstracts
- Funders
- National Science Foundation (PHY0822671)
- Air Force Office of Scientific Research (5710003628)
- Army Research Office (W911NF1610349)
- Intelligence Advanced Research Projects Activity (W911NF16-1-0082)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Keywords
- quantum circuit training, generative models, trapped-ion quantum computer, particle swarm optimization, Bayesian optimization, variational quantum circuits, near-term quantum devices
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