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Model Predictive Control for Finite Input Systems using the D-Wave Quantum Annealer
Open accessAbstract
The D-Wave quantum annealer has emerged as a novel computational architecture that is attracting significant interest, but there have been only a few practical algorithms exploiting the power of quantum annealers. Here we present a model predictive control (MPC) algorithm using a quantum annealer for a system allowing a finite number of input values. Such an MPC problem is classified as a non-deterministic polynomial-time-hard combinatorial problem, and thus real-time sequential optimization is difficult to obtain with conventional computational systems. We circumvent this difficulty by converting the original MPC problem into a quadratic unconstrained binary optimization problem, which is then solved by the D-Wave quantum annealer. Two practical applications, namely stabilization of a spring-mass-damper system and dynamic audio quantization, are demonstrated. For both, the D-Wave method exhibits better performance than the classical simulated annealing method. Our results suggest new applications of quantum annealers in the direction of dynamic control problems.
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“Further problems that have been treated with the standard QA approaches include: max-sum diversification [13], model-predictive control [71], the tiling puzzle problem [41], and image acquisition planning for earth observation satellites [128].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [71]
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Traffic signal optimization on a square lattice with quantum annealing
Inoue, D., Okada, A., MATSUMORI, T., Aihara, K., & Yoshida, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-82740-0 · Cited by 124
Self-citationImplementation of Quantum Annealing: A Systematic Review
Yulianti, L. P., & Surendro, K. (2022) · IEEE Access · doi:10.1109/access.2022.3188117 · Cited by 68
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Heng, S., Kim, D., Kim, T.-K., & Han, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3218908 · Cited by 34
Dynamic optimization based on quantum computation-A comprehensive review
Kou, H., Zhang, Y., & Lee, H. P. (2023) · Computers & Structures · doi:10.1016/j.compstruc.2023.107255 · Cited by 21
Metadata
- Authors
- Inoue, D., & Yoshida, H.
- Affiliations
- Toyota Central Research and Development Laboratories (Japan) (JP)
- Corresponding author
- Inoue, Daisuke
- Venue
- Scientific Reports
- Published
- 2020-01-31
- Volume
- 10
- Issue
- 1
- Pages
- 1591-1591
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6994678
- OpenAlex
- W3004136046
- Cited by
- 39
- References
- 44
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 18 · 2023: 8 · 2022: 3 · 2021: 2
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Keywords
- model predictive control, D-Wave quantum annealer, quantum annealing, QUBO, combinatorial optimization, mass-spring-damper system, simulated annealing comparison, dynamic control
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