Heterogeneous quantum computing for satellite constellation optimization: solving the weighted k -clique problem
Open accessAbstract
NP-hard optimization problems scale very rapidly with problem size, becoming unsolvable with brute force methods, even with supercomputing resources. Typically, such problems have been approximated with heuristics. However, these methods still take a long time and are not guaranteed to find an optimal solution. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. Current quantum annealing (QA) devices are designed to solve difficult optimization problems, but they are limited by hardware size and qubit connectivity restrictions. We present a novel heterogeneous computing stack that combines QA and classical machine learning, allowing the use of QA on problems larger than the hardware limits of the quantum device. These results represent experiments on a real-world problem represented by the weighted k -clique problem. Through this experiment, we provide insight into the state of quantum machine learning.
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A comparison between various decomposition techniques has demonstrated the superiority of qbsolv in terms of solution quality.
“Acomparison between various decomposition techniques, i.e., principal component decomposition [185], free-and-anneal [163], qbSolv, and iterative centrality halo [172] has demonstrated the superiority of qbsolv in terms of solution quality [12].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [163]
The Results (section 3.1, p. 11) compare genetic, pruning, and random preprocessing, not qbsolv with the decomposition techniques named in the claim. Searches for qbsolv, solution quality, and the named techniques found no passage establishing qbsolv’s superiority. (LLM, conf=0.76)
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- Authors
- Bass, G., Tomlin, C., Kumar, V., Rihaczek, P., & Dulny, J.
- Affiliations
- Booz Allen Hamilton (United States) (US)
- Corresponding author
- Bass, Gideon
- Tomlin, Casey
- Kumar, Vaibhaw
- Rihaczek, Pete
- Venue
- Quantum Science and Technology
- Publisher
- IOP Publishing
- Published
- 2018-03-07
- Volume
- 3
- Issue
- 2
- Pages
- 024010
- ISSN
- 2058-9565 (ISSN-L)
- OpenAlex
- W2755987512
- Cited by
- 16
- References
- 21
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 3 · 2020: 2 · 2018: 1
- FWCI
- 0.84
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Quantum Science and Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Keywords
- quantum annealing, satellite constellation optimization, hybrid quantum-classical optimization, quantum machine learning, NP-hard optimization, combinatorial optimization
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