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Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management
Open accessAbstract
We present the mapping of a class of simplified air traffic management problems (strategic conflict resolution) to quadratic unconstrained Boolean optimization problems. The mapping is performed through an original representation of the conflict-resolution problem in terms of a conflict graph, where the nodes of the graph represent flights and the edges represent a potential conflict between flights. The representation allows a natural decomposition of a real-world instance related to wind-optimal trajectories over the Atlantic Ocean into smaller subproblems that can be discretized and are amenable to be programmed in quantum annealers. In this paper, we tested the new programming techniques, and we benchmark the hardness of the instances using both classical solvers and the D-Wave 2X and D-Wave 2000Q quantum chip. The preliminary results show that for reasonable modeling choices, the most challenging subproblems which are programmable in the current devices are solved to optimality with 99% of probability within a second of annealing time.
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“A simplified version of the air-traffic management conflict-resolution problem has been tackled with QA after extraction of feasible subproblems [129].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [129]
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Cites
Diameter of the World-Wide Web
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (1999) · Nature · doi:10.1038/43601 · Cited by 4,240
A review of conflict detection and resolution modeling methods
Kuchar, J. K., & Lh, Y. (2000) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/6979.898217 · Cited by 1,070
Minor-embedding in adiabatic quantum computation: I. The parameter setting problem
Choi, V. (2008) · Quantum Information Processing · doi:10.1007/s11128-008-0082-9 · Cited by 410
A practical heuristic for finding graph minorsPreprint
Cai, J. L., Macready, W. G., & Roy, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2741 · Cited by 365
(1972) · Mathematics in Science and Engineering/Mathematics in science and engineering · doi:10.1016/s0076-5392(08)x6010-2 · Cited by 341
Halin, R. (1976) · Journal of Geometry · doi:10.1007/bf01917434 · Cited by 265
On non-serial dynamic programming
Bertelè, U., & Brioschi, F. (1973) · Journal of Combinatorial Theory Series A · doi:10.1016/0097-3165(73)90016-2 · Cited by 231
A case study in programming a quantum annealer for hard operational planning problems
Rieffel, E. G., Venturelli, D., O’Gorman, B., Do, M. B., Prystay, E. M., & Smelyanskiy, V. (2014) · Quantum Information Processing · doi:10.1007/s11128-014-0892-x · Cited by 221
Self-citationComplexity of Scheduling Shops with No Wait in Process
Sahni, S. K., & Cho, Y. (1979) · Mathematics of Operations Research · doi:10.1287/moor.4.4.448 · Cited by 132
Quantum Annealing Implementation of Job-Shop SchedulingPreprint
Venturelli, D., Marchand, D. J. J., & Rojo, G. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.08479 · Cited by 129
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Nakano, M., Seki, Y., Kikuchi, S., & Tanaka, S. (2026) · IEEE Access · doi:10.1109/access.2026.3655591 · Cited by 2
Performance of Domain-Wall Encoding for Quantum Annealing
Chen, J., Stollenwerk, T., & Chancellor, N. (2021) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2021.3094280 · Cited by 2
Self-citationAdvancements in Quantum Algorithms for Solving Complex Optimization ProblemsPreprint
Adediran, E., Fatoki, I., & Ogunyinka, T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5015789 · Cited by 2
Suzuki, T., & Tomono, T. (2026) · Advanced Quantum Technologies · doi:10.1002/qute.202500660 · Cited by 1
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Niu, C., Irannezhad, E., Myers, C. R., & Dixit, V. (2026) · Transportation Letters · doi:10.1080/19427867.2026.2640414 · Cited by 1
Metadata
- Authors
- Stollenwerk, T., O'Gorman, B., Venturelli, D., Mandra, S., Rodionova, O., Ng, H., Sridhar, B., Rieffel, E. G., & Biswas, R.
- Affiliations
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Ames Research Center (US)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2020-01
- Volume
- 21
- Issue
- 1
- Pages
- 285-297
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2769474857
- Cited by
- 129
- References
- 21
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 25 · 2023: 23 · 2022: 12 · 2021: 20 · 2020: 15
- FWCI
- 10.48
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- elib (German Aerospace Center) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NASA Aviation Systems Division
- NASA Advanced Exploration Systems Program
- Ames Research Center
- NASA Academic Mission Services (NNA16BD14C)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Vehicle Routing Optimization Methods
- Keywords
- quantum annealing, air traffic management, conflict graph, D-Wave quantum annealer, problem decomposition, benchmarking, combinatorial optimization
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