A cross-disciplinary introduction to quantum annealing-based algorithms
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A central goal in quantum computing is the development of quantum hardware and quantum algorithms in order to analyse challenging scientific and engineering problems. Research in quantum computation involves contributions from both physics and computer science; hence this article presents a concise introduction to basic concepts from both fields that are used in annealing-based quantum computation, an alternative to the more familiar quantum gate model. We introduce some concepts from computer science required to define difficult computational problems and to realise the potential relevance of quantum algorithms to find novel solutions to those problems. We introduce the structure of quantum annealing-based algorithms as well as two examples of this kind of algorithms for solving instances of the max-SAT and Minimum Multicut problems. An overview of the quantum annealing systems manufactured by D-Wave Systems is also presented.
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Liang, X., Wu, Y., & Zhai, H. (2020) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-019-1493-7 · Cited by 3 · see citing works
A General Framework Based on Walsh Decomposition for Combinatorial Optimization Problems
Unanue, I., Merino, M., & Lozano, J. A. (2021) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec45853.2021.9504699 · Cited by 3 · see citing works
Entropic Alternatives to InitializationPreprint
Musso, D. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4057054 · Cited by 3 · see citing works
Quantum Boltzmann Machines Using Parallel Annealing for Medical Image Classification
Schuman, D., Seebode, M. V., Rohe, T., Mansky, M. B., Schroedl-Baumann, M., Stein, J., Linnhoff–Popien, C., & Krellner, F. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00233 · Cited by 2 · see citing works
Spatial Correlations in the Qubit Properties of D-Wave 2000Q Measured and Simulated Qubit Networks
Park, J., Stepney, S., & D’Amico, I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34034-5_10 · Cited by 2 · see citing works
Quick design of feasible tensor networks for constrained combinatorial optimization
Nakada, H., Tanahashi, K., & Tanaka, S. (2025) · Quantum · doi:10.22331/q-2025-07-21-1799 · Cited by 2 · see citing works
Evaluating the convergence of tabu enhanced hybrid quantum optimization
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Re-use of samples in stochastic annealing
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QUBO Formulation for Sparse Sensor Placement for Classification
van Dommelen, M. R., & Phillipson, F. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-60433-1_2 · Cited by 1 · see citing works
Identifying Quantum Computing Applications in the Study and Operation of Manufacturing Systems
Schiffmann, O., Gopsill, J., Ridley, M., & Valero, M. (2025) · Procedia CIRP · doi:10.1016/j.procir.2025.02.161 · Cited by 1 · see citing works
Quantum Information Processing
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Quantum-Powered Hyperautomation
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A Quantum Adiabatic Algorithm for Multiobjective Combinatorial Optimization
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Boolean satisfiability verification by quantum annealing
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An encoding of argumentation problems using quadratic unconstrained binary optimization
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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit
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Many Known Quantum Algorithms Are Optimal: Symmetry-Based Proofs
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Experimental verification of the inertial theorem control protocolsPreprint
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Computational annealing as mechanism in eye movement desensitization and reprocessing (EMDR)Preprint
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Self-citationDynamic Programming in Economics on a Quantum AnnealerPreprint
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The Natural Bias of Artificial Instances
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Studies of quantum combinatorial optimization algorithms: <?A3B2 pi6?>a review
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Assessing Quantum Annealing to solve the Minimum vertex multicut
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GPU-Based Digital Annealing for Accelerating Large-Scale Combinatorial Optimization
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Quantum Vehicular Ecosystem: Analysis of Emerging Applications and Security Implications
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Quantum Annealing Applications for Power Grid Modernization
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- Authors
- Venegas-Andraca, S. E., Cruz-Santos, W., McGeoch, C., & Lanzagorta, M.
- Affiliations
- Tecnológico de Monterrey (MX)
- Universidad Autónoma del Estado de México (MX)
- D-Wave Systems (Canada) (CA)
- United States Naval Research Laboratory (US)
- Corresponding author
- Venegas-Andraca, Salvador E.
- Venue
- Contemporary Physics
- Publisher
- Informa UK Limited
- Published
- 2018-04-03
- Volume
- 59
- Issue
- 2
- Pages
- 174-197
- ISSN
- 0010-7514 (ISSN-L)
- 1366-5812
- OpenAlex
- W2792255093
- Cited by
- 83
- References
- 45
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 9 · 2023: 14 · 2022: 10 · 2021: 13 · 2020: 8 · 2019: 5 · 2018: 2
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Contemporary Physics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Consejo Nacional de Ciencia y Tecnología (1007, 41594)
- CONACyT (1007, 41594)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum annealing, MaxSAT, combinatorial optimization, quantum computing hardware, computational complexity
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