Quantum Software Engineering and Potential of Quantum Computing in Software Engineering Research: A Review

TL;DR

The paper surveys the intersection of quantum computing and software engineering, introducing quantum software engineering (QSE) as a nascent discipline and outlining four research questions on QML, quantum optimization, tooling, and challenges. It synthesizes foundational quantum concepts, gates, SDKs, and algorithms, and then maps quantum approaches to software engineering tasks, notably in defect prediction, code analysis, and testing. Key contributions include a taxonomy of QML integration, a review of quantum optimization workflows (QAOA, VQE, quantum annealing) with encodings like $Q(x)$ or $QUBO$, and a typology of QSE tooling and lifecycle challenges. The study highlights that while quantum methods show promise for SE, current hardware limits, data encoding issues, and tool maturity constrain immediate impact; it calls for dedicated QSE frameworks, higher-level abstractions, and workforce training to realize practical quantum-enabled software development.

Abstract

Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.

Figures (21)

• Figure 1: Superposition, entanglement, measurement, and interference processes of a quantum computer.
• Figure 2: The workflow diagram for the typical quantum computing process
• Figure 3: A Simple Experiment Demonstrates Superior Performance of Quantum Computing Over Classical Methods
• Figure 4: The Bloch sphere: Any state of a qubit on the Bloch sphere can be represented as $|\Psi\rangle = \cos \frac{\theta}{2} |0\rangle + \exp^{i\phi} \sin \frac{\theta}{2} |1\rangle$, where $0 \leq \theta \leq \pi$, $0 \leq \phi \leq 2\pi$
• Figure 5: A 4-qubit GHZ state Circuit