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Genetic and Evolutionary Computation Conference Companion (GECCO Companion)

Introduction to Quantum Optimization

Alberto Moraglio, Francisco Chicano

Published Jul 13, 2026• 0 citations
Publisher

Abstract

include quantum computing, the application of search techniques to Software Engineering problems and the use of theoretical results to efficiently solve combinatorial optimization problems.
DOI
10.1145/3795101.3807751

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Introduction to Quantum Optimization

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include quantum computing, the application of search techniques to Software Engineering problems and the use of theoretical results to efficiently solve combinatorial optimization problems.

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Abstract Software engineering plays an important role in computer science. Novel quantum algorithms can efficiently solve software-engineering problems. Not only software engineering but also many industries including logistics, finance, genomics, resource allocation, logistics, bioinformatics, mobile agents and more have optimization problems. Such problems may have long time solutions [15]. Research has been conducted to improve the performance of current solutions and to search for optimized solutions. Search-based software engineering (SBSE) uses computational techniques to determine optimized solutions in a large search space. There are SBSE problems such as Test Suite Minimization (TSM) and Maximum Independent Set (MIS) that require efficient solutions due to its important role. A quantum-inspired genetic algorithm had solved the TSM problem with higher performance than classical solutions [10]. The quantum-inspired genetic algorithm and quantum algorithm showed better performance results than classical solutions. This improvement motivated us to modify such algorithms in order to solve the MIS optimization problems [24, 25]. In addition, MIS has crucial applications in many domains. It can be applied in software engineering to separate related and unrelated requirements, which is of great support for project management. Resources, time, cost, and relevance can be updated accordingly. MIS can also be applied in network design, scheduling, resource allocation, logistics, bioinformatics, mobile agents, and more [17]. Quantum-inspired genetic algorithm combines quantum mechanics concepts and genetic algorithms which enhances search capability and provides efficient search mechanism [19]. In this study, a modified quantum-inspired genetic algorithm (QIGA) is proposed and implemented to find an optimized solution for the MIS problem. A classical genetic algorithm (GA) is implemented and has been tested. A Comparison is conducted to show the results of QIGA and GA to measure the performance improvement. Results and its analysis are displayed to show QIGA and GA convergence. The proposed algorithm has no prior assumptions.

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Quantum Computing For Combinatorial Optimization: Algorithms, Complexity Analysis, And Real-World Applications.

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Combinatorial optimization problems play a vital role in computer science and engineering, with applications in logistics, network design, finance, and resource allocation. However, many of these problems are NP-hard, making them computationally expensive to solve using classical optimization techniques, especially for large-scale instances. In recent years, quantum computing has emerged as a promising paradigm capable of addressing these limitations by leveraging quantum mechanical principles such as superposition and entanglement. This study investigates the application of quantum computing for solving combinatorial optimization problems, with a focus on algorithm design, complexity analysis, and real-world applicability. The research primarily employs the Quantum Approximate Optimization Algorithm (QAOA) and Grover's search algorithm to solve representative problems such as Max-Cut and the Knapsack problem. The performance of these algorithms is evaluated using key metrics including accuracy, execution time, and approximation ratio, and is compared with classical optimization techniques. The results demonstrate that QAOA is capable of generating near-optimal solutions, achieving accuracy levels of up to 93% for small problem instances. However, the study also highlights the challenges associated with current quantum systems, including noise, limited qubit availability, and increased execution time. Comparative analysis reveals that while classical methods remain more efficient for small-scale problems, quantum algorithms show significant potential for scalability and handling complex optimization tasks. The findings suggest that hybrid quantum-classical approaches offer a practical solution in the current Noisy Intermediate-Scale Quantum (NISQ) era. The study concludes that although quantum computing is still evolving, it holds strong potential for transforming combinatorial optimization in the future.

•Jan 1, 2026•0 citations
Abstract Software engineering plays an important role in computer science. Novel quantum algorithms can efficiently solve software-engineering problems. Not only software engineering but also many industries including logistics, finance, genomics, resource allocation, logistics, bioinformatics, mobile agents and more have optimization problems. Such problems may have long time solutions [15]. Research has been conducted to improve the performance of current solutions and to search for optimized solutions. Search-based software engineering (SBSE) uses computational techniques to determine optimized solutions in a large search space. There are SBSE problems such as Test Suite Minimization (TSM) and Maximum Independent Set (MIS) that require efficient solutions due to its important role. A quantum-inspired genetic algorithm had solved the TSM problem with higher performance than classical solutions [10]. The quantum-inspired genetic algorithm and quantum algorithm showed better performance results than classical solutions. This improvement motivated us to modify such algorithms in order to solve the MIS optimization problems [24, 25]. In addition, MIS has crucial applications in many domains. It can be applied in software engineering to separate related and unrelated requirements, which is of great support for project management. Resources, time, cost, and relevance can be updated accordingly. MIS can also be applied in network design, scheduling, resource allocation, logistics, bioinformatics, mobile agents, and more [17]. Quantum-inspired genetic algorithm combines quantum mechanics concepts and genetic algorithms which enhances search capability and provides efficient search mechanism [19]. In this study, a modified quantum-inspired genetic algorithm (QIGA) is proposed and implemented to find an optimized solution for the MIS problem. A classical genetic algorithm (GA) is implemented and has been tested. A Comparison is conducted to show the results of QIGA and GA to measure the performance improvement. Results and its analysis are displayed to show QIGA and GA convergence. The proposed algorithm has no prior assumptions.
Quantum computing has demonstrated the potential to solve computationally intensive problems more efficiently than classical methods. Many software engineering tasks, such as test case selection, static analysis, code clone detection, and defect prediction, involve complex optimization, search, or classification, making them candidates for quantum enhancement. In this paper, we introduce Quantum-Based Software Engineering (QBSE) as a new research direction for applying quantum computing to classical software engineering problems. We outline its scope, clarify its distinction from quantum software engineering (QSE), and identify key problem types that may benefit from quantum optimization, search, and learning techniques. We also summarize existing research efforts that remain fragmented. Finally, we outline a preliminary research agenda that may help guide the future development of QBSE, providing a structured and meaningful direction within software engineering.
Combinatorial optimization problems play a vital role in computer science and engineering, with applications in logistics, network design, finance, and resource allocation. However, many of these problems are NP-hard, making them computationally expensive to solve using classical optimization techniques, especially for large-scale instances. In recent years, quantum computing has emerged as a promising paradigm capable of addressing these limitations by leveraging quantum mechanical principles such as superposition and entanglement. This study investigates the application of quantum computing for solving combinatorial optimization problems, with a focus on algorithm design, complexity analysis, and real-world applicability. The research primarily employs the Quantum Approximate Optimization Algorithm (QAOA) and Grover's search algorithm to solve representative problems such as Max-Cut and the Knapsack problem. The performance of these algorithms is evaluated using key metrics including accuracy, execution time, and approximation ratio, and is compared with classical optimization techniques. The results demonstrate that QAOA is capable of generating near-optimal solutions, achieving accuracy levels of up to 93% for small problem instances. However, the study also highlights the challenges associated with current quantum systems, including noise, limited qubit availability, and increased execution time. Comparative analysis reveals that while classical methods remain more efficient for small-scale problems, quantum algorithms show significant potential for scalability and handling complex optimization tasks. The findings suggest that hybrid quantum-classical approaches offer a practical solution in the current Noisy Intermediate-Scale Quantum (NISQ) era. The study concludes that although quantum computing is still evolving, it holds strong potential for transforming combinatorial optimization in the future.