Quantum-Inspired Algorithms for Software Optimization: A Conceptual Framework and Future Directions
Keywords:
Computational complexity, Heuristic optimization, Quantum annealing, Quantum-inspired algorithms, Software optimization, Variational algorithmsAbstract
Quantum-inspired algorithms can be considered a successful intersection of quantum computational theory and classical software engineering, which provides new avenues for finding suitable solutions to the complex optimization problems that have long been difficult to break through the traditional computing paradigm. This is a conceptual paper that discusses the theoretical background, new methodologies, and software optimization that is being studied using quantum-inspired algorithms. Based on quantum mechanical proposals including superposition, entanglement, and quantum tunneling, these algorithms translate quantum computational logic to classical hardware and thus bring quantum benefit theory and software engineering issues of the real world closer to each other. The paper provides an overview of some of the critical quantum-inspired methods such as Quantum Annealing-inspired methods, Variational Quantum Eigensolver (VQE) adaptations, Quantum Frameworks based on Approximate Optimization Algorithms (QAOA), and quantum evolutionary algorithms, exploring how they can be used in a wide range of optimization problems such as code compilation, resource allocation, scheduling, and machine learning model optimization. Some serious conceptual issues are pointed out, such as the faithfulness of quantum simulation of classical systems, scaling, and the computational cost of simulating quantum behavior. The paper concludes with a roadmap of the interdisciplinary study that might help to unlock the transformative capability of quantum-inspired optimization in upcoming software systems.
References
Arute et al., “Quantum supremacy using a programmable superconducting processor,” Nature, vol. 574, pp. 505–510, 2019.
M. Zhang, Y. Li, T. Yue, and K.-Y. Cai, “Quantum optimization for software engineering: A survey,” 2025.
E. Tang, “A quantum-inspired classical algorithm for recommendation systems,” In Proceedings of the 51st Annual ACM Symposium on Theory of Computing (STOC), 2019.
L. K. Grover, “A fast quantum mechanical algorithm for database search,” In Proceedings of the 28th Annual ACM Symposium on Theory of Computing (STOC), 1996, pp. 212–219.
P. W. Shor, “Algorithms for quantum computation: Discrete logarithms and factoring,” in Proceedings of the 35th Annual Symposium Foundations of Computer Science (FOCS), 1994, pp. 124–134.
J. Biamonte et al., “Quantum machine learning,” Nature, vol. 549, pp. 195–202, 2017.
R. Orús, “A practical introduction to tensor networks,” Annals of Physics, vol. 349, pp. 117–158, 2014.
T. Kadowaki and H. Nishimori, “Quantum annealing in the transverse Ising model,” Physical Review, vol. 58, no. 5, pp. 5355–5363, 1998.
A. Peruzzo et al., “A variational eigenvalue solver on a quantum processor,” Nature Communications, vol. 5, art. no. 4213, 2014.
E. Farhi, J. Goldstone, and S. Gutmann, “A quantum approximate optimization algorithm,” arXiv:1411.4028, 2014.
L. Zhou, S. Wang, S. Choi, H. Pichler, and M. D. Lukin, “Quantum Approximate Optimization Algorithm: Performance and mechanism,” PRX Quantum, vol. 1, no. 2, Art. no. 021067, 2020.
K.-H. Han and J.-H. Kim, “Quantum-inspired evolutionary algorithm for combinatorial optimization,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 6, pp. 580–593, 2002.
A. Agapie and H. Dediu, “GA for deceptive problems: Inverting schemata by a statistical approach, Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC), 1996.
Y. Wang and W. Wang, “Quantum-inspired differential evolution for knapsack problem,” Mathematics, vol. 9, no. 11, art. no. 1233, 2021.
E. Stoudenmire and D. J. Schwab, “Supervised learning with tensor networks,” Advances in Neural Information Processing Systems (NeurIPS), 2016.
Q. G. Zeng et al., “Performance of quantum annealing-inspired algorithms for combinatorial optimization,” Communication Physics, vol. 7, art. no. 249, 2024.
A. Mittal et al., “Performance and scalability of a quantum-inspired evolutionary optimizer on GPU,” 2025.
A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information. Cambridge, U.K. Cambridge University Press, 2010.
Pooja and S. K. Sood, “Scientometric analysis of quantum-inspired metaheuristic algorithms,” Artificial Intelligence Review, vol. 57, art. no. 22, 2024.
