Benchmarking Quantum Annealing Controls with Portfolio Optimization
Open accessAbstract
Quantum annealing offers an approach to finding the optimal solutions for a variety of computational problems, where the quantum annealing controls influence the observed performance and error mechanisms by tuning the underlying quantum dynamics. However, the influence of the available controls is often poorly understood, and methods for evaluating the effects of these controls are necessary to tune quantum computational performance. Here we use portfolio optimization as a case study by which to benchmark quantum annealing controls and their relative effects on computational accuracy. We compare empirical results from the D-Wave $2000{\mathrm{Q}}^{\mathrm{TM}}$ quantum annealer to the computational ground truth for a variety of portfolio optimization instances. We evaluate both forward and reverse annealing methods and we identify control variations that yield optimal performance in terms of probability of success and probability of chain breaks.
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The impact of various quantum controls, such as annealing time and number of spin reversals, on the success probability and chain breaks has been studied by Grant et al., yielding a saturation effect for both controls.
“The impact of various quantum controls, such as annealing time and number of spin reversals [194], on the success probability and chain breaks has been studied by Grant et al. [55], yielding a saturation effect for both controls.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [55]
The success rate of obtaining the optimal solution saturates with increasing annealing time and number of spin reversals.
“The success rate of obtaining the optimal solution saturates with increasing annealing time and number of spin reversals [55].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [55]
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“The impact of various quantum controls, such as annealing time and number of spin reversals [194], on the success probability and chain breaks has been studied by Grant et al. [55], yielding a saturation effect for both controls.”
Background
- Authors
- Grant, E., Humble, T. S., & Stump, B.
- Affiliations
- Oak Ridge National Laboratory (US)
- Venue
- Physical Review Applied
- Publisher
- American Physical Society (APS)
- Published
- 2021-01-08
- Volume
- 15
- Issue
- 1
- ISSN
- 2331-7019 (ISSN-L)
- OpenAlex
- W3040309518
- Cited by
- 90
- References
- 44
- Citations by year
- 2026: 5 · 2025: 23 · 2024: 14 · 2023: 15 · 2022: 21 · 2021: 10 · 2020: 2
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physical Review Applied (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Department of Energy (DE-AC05-00OR22725)
- LLC (DE-AC05-00OR22725)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- quantum annealing, control parameters, portfolio optimization, D-Wave 2000Q, success probability, benchmarking, combinatorial optimization
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