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

TL;DR

This review explores the emerging field of quantum software engineering, highlighting its potential to revolutionize software development with quantum computing, while noting current limitations and future opportunities.

Contribution

It is the first comprehensive review of quantum software engineering, summarizing recent developments, techniques, and challenges in integrating quantum computing into software engineering.

Findings

Quantum machine learning and optimization show promise for software tasks.

Current QSE tools lack robustness and maturity.

Quantum computing's full potential in software engineering is still unrealized.

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.