The scale and complexity of modern software systems (e.g., AI-based systems, robotic systems) are constantly increasing. Moreover, these systems are characterized by unique features, such as environmental uncertainty and the absence of precise oracles. All these aspects make the design, development, and assessment of modern software systems extremely challenging.

In recent years, AI-based solutions have been proposed to address some of the issues affecting software engineering (SE) tasks, such as requirements engineering, testing, and repair. Notable examples are: the active area of search-based software engineering (SBSE) [2], which proposed different search-based approaches for different SE tasks; and ML-based solutions for test case optimization and prediction [5]. However, even AI-based solutions struggle to scale to modern software systems; for example, the search spaces that the SBSE approach must handle keep increasing, and so does the size of the training sets for ML-based solutions.

Quantum Computing (QC) is expected to solve many complex problems [1]. To this end, various technologies are being explored to build quantum computers, such as superconducting, trappedion, and neutral-atom architectures [3]. In the QC field, quantum algorithms have the potential to solve some complex SE tasks more efficiently. Recently, some works have begun to explore such algorithms, including the Harrow–Hassidim–Lloyd (HHL) algorithm for secure log search [3], quantum approximate optimization algorithms for test case optimization [7], quantum annealing for regression testing [4], and quantum neural networks for test case prediction [6,8].

This meeting's goal is to unite researchers and practitioners to identify the main challenges in applying quantum algorithms to classical SE tasks. By connecting the scalability issues of current SE methods to the opportunities provided by QC, the meeting aims to create a research roadmap to guide future efforts in this field.

This meeting is focused on discussing various aspects (challenges, ideas, research roadmaps) of quantum algorithms applied to classical software engineering, including, but not limited to:

• Which classes of classical software engineering problems (e.g., test generation, code analysis, refactoring) are amenable to existing quantum algorithms? How can we systematically identify and map suitable SE tasks to quantum-executable formulations?

• What abstractions and architectural patterns are required to integrate quantum algorithms into existing software engineering workflows and development environments?

• Given the inherent noise in near-term quantum computers, should the focus be on developing quantum algorithms that handle noise inherently in solving classical SE tasks, or on solutions that assume ideal, noise-free quantum computations? How can we develop best practices and solutions for developing quantum software to solve classical SE tasks? Are classical solutions, e.g., agile methods, still applicable?

• What metrics should we use to evaluate quantum algorithms applied to solve classical SE tasks vs classical methods (vs AI?)?

• How can SE artefacts (code structures, models, abstract syntax trees, architectural graphs) be automatically extracted and encoded into quantum-compatible representations (e.g., QUBO, Ising models, quantum circuits)?

• How can we build repositories and taxonomies that match SE task characteristics to suitable quantum algorithms (e.g., Grover's search, QAOA, quantum annealing)?

• What execution orchestration patterns and hybrid quantum-classical architectures are needed to coordinate computations across classical and quantum resources? How can quantum results be post-processed, validated, and reintegrated into classical development workflows in ways that are actionable for software engineers?

We aim to invite researchers and practitioners working on: (1) applying quantum algorithms toclassical software engineering tasks, including testing, debugging, modeling, execution, and optimization; (2) applying classical AI techniques for SE/quantum SE tasks; (3) advancing classical software engineering methods; and (4) developing new quantum algorithms, including quantum machine learning and optimization.

The meeting will follow a structured format. It will begin with an introductory session where participants present themselves and their research interests. Participants will then be organized into smaller groups based on their focus areas, each engaging in in-depth discussions and presenting their findings to the full group. Dedicated sessions will be held to draft an outline for a report summarizing key insights. Beyond this, the meeting will provide a platform for fostering new research collaborations, joint publications, and international projects in the intersection of quantum computing and classical software engineering.

• No.224. Quantum Software Engineering: This proposed seminar builds on No. 224 by exploring how quantum algorithms can improve classical software engineering tasks, including testing, debugging, modeling, and optimization. It will also discuss how classical AI techniques, enhanced with quantum approaches, can tackle challenges of scale and complexity in classical software systems. In contrast, No. 224 focused on developing quantum software for execution on quantum computers.

• No.248. Diversity and Inclusion in the Era of Artificial Intelligence and Quantum Computing: No. 248 focuses exclusively on diversity and inclusion in emerging topics at the intersection of quantum computing and artificial intelligence. The proposed seminar, in contrast, covers the technical aspects of applying quantum algorithms to a specific domain of software engineering. Insights from No. 248 could still be relevant for the proposed seminar.
• No.247. Advances in distributed quantum computing, quantum learning and cryptography: In contrast to No. 247, the proposed seminar shifts the emphasis toward applying quantum algorithms to classical software engineering tasks, whereas No. 247 covers broader technical aspects, including distributed computing, quantum learning, and cryptography.
• NO.198. New Directions in Provable Quantum Advantages: The proposed seminar builds on the technical progress highlighted in No. 198 by exploring how these advances can enhance SE tasks such as testing, debugging, modeling, and optimization, fostering practical applications and new research directions of quantum algorithms applied to classical SE.

[1] Shaukat Ali, Tao Yue, and Rui Abreu. 2022. When software engineering meets quantum computing. Commun. ACM 65, 4 (April 2022), 84–88. https://doi.org/10.1145/3512340

[2] Thelma Elita Colanzi, Wesley KG Assunção, Silvia R Vergilio, Paulo Roberto Farah, and Giovani Guizzo. 2020. The symposium on search-based software engineering: Past, present and future. Information and Software Technology 127 (2020), 106372. https://doi.org/10.1016/j.infsof.2020.106372

[3] Andriy Miranskyy, Mushahid Khan, Jean Paul Latyr Faye, and Udson C. Mendes. 2022. Quantum computing for software engineering: prospects. In Proceedings of the 1st International Workshop on Quantum Programming for Software Engineering (QP4SE 2022). Association for Computing Machinery, New York, NY, USA, 22–25. https://doi.org/10.1145/3549036.3562060

[4] Antonio Trovato, Manuel De Stefano, Fabiano Pecorelli, Dario Di Nucci, Andrea De Lucia. Reformulating regression test suite optimization using quantum annealing - an empirical study. Int J Softw Tools Technol Transfer 26, 767–780 (2024). https://doi.org/10.1007/s10009-024-00775-w

[5] Yanming Yang, Xin Xia, David Lo, and John Grundy. 2022. A Survey on Deep Learning for Software Engineering. ACM Comput. Surv. 54, 10s, Article 206 (January 2022), 73 pages. https://doi.org/10.1145/3505243

[6] Xinyi Wang, Shaukat Ali, and Paolo Arcaini. "Quantum Artificial Intelligence for Software Engineering: the Road Ahead." arXiv preprint arXiv:2505.04797 (2025).

[7] Xinyi Wang, Shaukat Ali, Tao Yue, and Paolo Arcaini, "Quantum Approximate Optimization Algorithm for Test Case Optimization,"in IEEE Transactions on Software Engineering, vol. 50, no. 12, pp. 3249-3264, Dec. 2024, https://doi.org/10.1109/TSE.2024.3479421

[8] Jianjun Zhao, "Quantum-Based Software Engineering." arXiv preprint arXiv:2505.23674 (2025).