Benchmark Test of Black-box Optimization Using D-Wave Quantum Annealer
Open accessAbstract
In solving optimization problems, objective functions generally need to be minimized or maximized. However, objective functions cannot always be formulated explicitly in a mathematical form for complicated problem settings. Although several regression techniques infer the approximate forms of objective functions, they are at times expensive to evaluate. Optimal points of "black-box" objective functions are computed in such scenarios, while effectively using a small number of clues. Recently, an efficient method by use of inference by sparse prior for a black-box objective function with binary variables has been proposed. In this method, a surrogate model was proposed in the form of a quadratic unconstrained binary optimization (QUBO) problem, and was iteratively solved to obtain the optimal solution of the black-box objective function. In the present study, we employ the D-Wave 2000Q quantum annealer, which can solve QUBO by driving the binary variables by quantum fluctuations. The D-Wave 2000Q quantum annealer does not necessarily output the ground state at the end of the protocol due to freezing effect during the process. We investigate effects from the output of the D-Wave quantum annealer in performing black-box optimization. We demonstrate a benchmark test by employing the sparse Sherrington-Kirkpatrick (SK) model as the black-box objective function, by introducing a parameter controlling the sparseness of the interaction coefficients. Comparing the results of the D-Wave quantum annealer to those of the simulated annealing (SA) and semidefinite programming (SDP), our results by the D-Wave quantum annealer and SA exhibit superiority in black-box optimization with SDP. On the other hand, we did not find any advantage of the D-Wave quantum annealer over the simulated annealing. As far as in our case, any effects by quantum fluctuation are not found.
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Koshikawa et al. utilized and benchmarked QA within black-box optimization.
“Finally, Koshikawa et al. [79] utilized and benchmarked QA within black-box optimization.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [79]
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Self-citation
- Authors
- Koshikawa, A. S., Ohzeki, M., Kadowaki, T., & Tanaka, K.
- Affiliations
- Tohoku University (JP)
- Tokyo Institute of Technology (JP)
- Denso (Japan) (JP)
- ORCID
- Ohzeki, Masayuki
- Venue
- Journal of the Physical Society of Japan
- Publisher
- Physical Society of Japan
- Published
- 2021-06-15
- Volume
- 90
- Issue
- 6
- Pages
- 064001
- ISSN
- 0031-9015 (ISSN-L)
- 1347-4073
- OpenAlex
- W3139206159
- Cited by
- 43
- References
- 44
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 4 · 2023: 13 · 2022: 8 · 2021: 3
- FWCI
- 2.45
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Journal of the Physical Society of Japan (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Japan Society for the Promotion of Science (18H03303, 18J20396, 19H01095, 20H02168)
- Japan Science and Technology Agency (JPMJCR1402)
- Ministry of Education, Culture, Sports, Science and Technology (MXS0120352009)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Keywords
- black-box optimization, D-Wave quantum annealer, quantum annealing, QUBO, Sherrington-Kirkpatrick model, simulated annealing, semidefinite programming, benchmark tests, binary optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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