A comprehensive review of common QUBO solvers in the cybersecurity domain. Advantages and disadvantages

Khubiev K.Yu., Kholodov Y.A., Petrenko A.
 pdf (357K)

Quadratic Unconstrained Binary Optimization (QUBO) provides a common modeling framework for combinatorial optimization problems arising in cybersecurity. This review analyzes how cryptanalytic constraints, anomaly-detection objectives, network-defense tasks and cyber-risk models can be encoded as QUBO or equivalent Ising formulations. We compare three classes of solvers used in the literature: physical quantum annealers, hybrid quantum-classical workflows and quantum-inspired classical heuristics such as simulated annealing and simulated bifurcation. Particular attention is paid to the gap between formal QUBO expressiveness and practical solvability on current hardware, including embedding overhead, limited connectivity, noise and scaling constraints. The reviewed studies are grouped by application domain and assessed in terms of model size, solution quality, runtime indicators and reproducibility of computational settings. We show that QUBO is already useful as a unifying language for cybersecurity optimization, while near-term practical advantages are most plausible in hybrid and quantum-inspired settings rather than in fully quantum implementations. The review concludes by outlining open problems in QUBO construction, decomposition, sparsification and benchmarking for cybersecurity applications.

Keywords: cybersecurity, quantum annealing, quantum computation, QUBO
Citation in English: Khubiev K.Yu., Kholodov Y.A., Petrenko A. A comprehensive review of common QUBO solvers in the cybersecurity domain. Advantages and disadvantages // Computer Research and Modeling, 2026, vol. 18, no. 5, pp. 1129-1159
Citation in English: Khubiev K.Yu., Kholodov Y.A., Petrenko A. A comprehensive review of common QUBO solvers in the cybersecurity domain. Advantages and disadvantages // Computer Research and Modeling, 2026, vol. 18, no. 5, pp. 1129-1159
DOI: 10.20537/2076-7633-2026-18-5-1129-1159
URL: http://crm-en.ics.org.ru/journal/article/3774/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2026 Khubiev K.Yu., Kholodov Y.A., Petrenko A.

Indexed in Scopus

Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU

The journal is included in the Russian Science Citation Index

The journal is included in the RSCI

International Interdisciplinary Conference "Mathematics. Computing. Education"

Website Copyright © 2009–2026 Institute of Computer Science