The Quantum Software Engineering Path.

Xinyi Wang, Shaukat Ali, Paolo Arcaini

In order to handle the increasing complexity of software systems, Artificial Intelligence (AI) has been applied to various areas of software engineering, including requirements engineering, coding, testing, and debugging. This has led to the emergence of AI for Software Engineering as a distinct research area within the field of software engineering. With the development of quantum computing, the field of Quantum AI (QAI) is arising, enhancing the performance of classical AI and holding significant potential for solving classical software engineering problems. Some initial applications of QAI in software engineering have already emerged, such as test case optimization. However, the path ahead remains open, offering ample opportunities to solve complex software engineering problems cost-effectively with QAI. To this end, this article presents a roadmap towards the application of QAI in software engineering. Specifically, we consider two of the main categories of QAI, i.e., quantum optimization algorithms and quantum machine learning. For each software engineering phase, we discuss how these QAI approaches can address some of the tasks associated with that phase. Moreover, we provide an overview of some of the possible challenges that need to be addressed to make the application of QAI for software engineering successful.

Manuel Serrano, Ricardo Pérez‐Castillo, Mario G. Piattini

Mario G. Piattini, Manuel Serrano, Ricardo Pérez‐Castillo +2 more

Nowadays, we are at the dawn of a new age, the quantum era. Quantum computing is no longer a dream; it is a reality that needs to be adopted. But this new technology is taking its first steps, so we still do not have models, standards, or methods to help us in the creation of new systems and the migration of current ones. Given the current state of quantum computing, we need to go back to the path software engineering took in the last century to achieve the new golden age for quantum software engineering.

Luis Jiménez-Navajas, Ricardo Pérez‐Castillo, Mario G. Piattini

Harry Buhrman, Floor van de Pavert

Venkat Kaushal Thippisetty, Sridhar Chimalakonda

Classical software engineering has been shaped by learned code representations, where approaches such as code2vec, CodeBERT, and recent large-scale code models support tasks including clone detection, structural anomaly detection, and code search. Quantum software engineering, however continues to build primarily on syntactic and rule-based techniques. Existing representations and tools such as QChecker, NiCad for quantum programs, and LintQ apply pattern matching and static analysis but they do not capture program-level semantics across circuits. Essentially, these representations are not designed for Aritificial Intelligence and Large Language Model pipelines for quantum software engineering. We present QuantumCode2Vec, a learned representation of quantum programs that captures structural patterns at the level of algorithmic behavior. Driven by the impact of code2vec, the approach decomposes quantum circuits into quantum-aware paths that reflect qubit evolution, entanglement structure, and circuit properties, and aggregates them through attention into fixed length representations and embeddings. Across canonical quantum algorithms, the learned representations group circuits by algorithm, remain stable under superficial changes, and generalize across circuit sizes. In a retrieval setting, similar circuits consistently correspond to the same algorithm, supporting semantic clone detection and code search. In this paper, our goal is to position QuantumCode2Vec as a fundamental learned representation and emedding for quantum software engineering.