Generative model benchmarks for superconducting qubits
Open accessAbstract
In this work we demonstrate experimentally how generative model training can be used as a benchmark for small (fewer than five qubits) quantum devices. Performance is quantified using three data analytic metrics: the Kullback-Leibler divergence and two adaptations of the ${F}_{1}$ score. Using the $2\ifmmode\times\else\texttimes\fi{}2$ bars and stripes data set, we train several different circuit constructions for generative modeling with superconducting qubits. By taking hardware connectivity constraints into consideration, we show that sparsely connected shallow circuits outperform denser counterparts on noisy hardware.
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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 Hamilton 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 Hamilton et al., 2019
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- Authors
- Hamilton, K. E., Dumitrescu, E. F., & Pooser, R. C.
- Affiliations
- Oak Ridge National Laboratory (US)
- Venue
- Physical Review A
- Publisher
- American Physical Society (APS)
- Published
- 2019-06-18
- Volume
- 99
- Issue
- 6
- ISSN
- 0556-2791 (ISSN-L)
- 2469-9926
- 2469-9934
- OpenAlex
- W2950205552
- Cited by
- 57
- References
- 26
- Citations by year
- 2026: 8 · 2025: 1 · 2024: 7 · 2023: 5 · 2022: 7 · 2021: 16 · 2020: 8 · 2019: 5
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Physical Review A (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Oak Ridge National Laboratory (ERKJ332)
- U.S. Department of Energy
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- generative models, superconducting qubits, Kullback-Leibler divergence, F1 score, shallow circuits, noisy quantum hardware, quantum circuit design
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