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Adiabatic Quantum Computing for Max-Sum Diversification
Abstract
The combinatorial problem of max-sum diversification asks for a maximally diverse subset of a given set of data. Here, we show that it can be expressed as an Ising energy minimization problem. Given this result, max-sum diversification can be solved on adiabatic quantum computers and we present proof of concept simulations which support this claim. This, in turn, suggests that quantum computing might play a role in data mining. We therefore discuss quantum computing in a tutorial like manner and elaborate on its current strengths and weaknesses for data analysis.
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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 [13]
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Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study
Gemeinhardt, F., Garmendia, A., Wimmer, M., Weder, B., & Leymann, F. (2024) · ACM Computing Surveys · doi:10.1145/3620668 · Cited by 22
Problem Solving with Hopfield Networks and Adiabatic Quantum Computing
Bauckhage, C., Sánchez, R. J., & Sifa, R. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206916 · Cited by 19
Self-citationOptimization of a refinery scheduling process with column generation and a quantum annealer
Ossorio-Castillo, J., & Pena-Brage, F. (2021) · Optimization and Engineering · doi:10.1007/s11081-021-09662-8 · Cited by 14
Streaming Algorithms for Diversity Maximization with Fairness Constraints
Wang, Y., Fabbri, F., & Mathioudakis, M. (2022) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde53745.2022.00008 · Cited by 13
Fair Max–Min Diversity Maximization in Streaming and Sliding-Window Models
Wang, Y., Fabbri, F., Mathioudakis, M., et al. (2023) · Entropy · doi:10.3390/e25071066 · Cited by 10
Quantum Computing Approaches for Vector Quantization—Current Perspectives and Developments
Engelsberger, A., & Villmann, T. (2023) · Entropy · doi:10.3390/e25030540 · Cited by 8
A Simple QUBO Formulation of Sudoku
Mücke, S. (2024) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3638530.3664106 · Cited by 6
Diversity Maximization in the Presence of Outliers
Amagata, D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i10.26454 · Cited by 6
Max-Min Diversification with Asymmetric Distances
Kumpulainen, I., Adriaens, F., & Tatti, N. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671757 · Cited by 2
Quantum Artificial Intelligence: A tutorial
Martín‐Guerrero, J. D., Lamata, L., & Villmann, T. (2023) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2023.es2023-2 · Cited by 1
DESICOM as Metaheuristic Search
Sifa, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-53552-0_38
Self-citationBauckhage, C., & Sifa, R. (2025) · Cognitive technologies · doi:10.1007/978-3-031-99402-9_7
Self-citationBauckhage, C., & Sifa, R. (2025) · Cognitive technologies · doi:10.1007/978-3-031-99402-9_5
Self-citation
Metadata
- Authors
- Bauckhage, C., Sifa, R., & Wrobel, S.
- Venue
- Proceedings of the 2020 SIAM International Conference on Data Mining
- Published
- 2020-01
- Pages
- 343-351
- OpenAlex
- W3013788674
- Cited by
- 13
- References
- 0
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 5 · 2022: 1 · 2021: 1 · 2020: 2
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Society for Industrial and Applied Mathematics eBooks (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Keywords
- adiabatic quantum computation, Ising model, energy minimization, combinatorial optimization, quantum annealing, data mining
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