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Quantum Computing as a Service – a Software Engineering Perspective

Muhammad Waseem, Aakash Ahmad, Bakheet Aljedaani, Mahdi Fahmideh, Peng Liang, Feras M. Awaysheh

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.2139/ssrn.5610153

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Abstract Quantum software is becoming a key enabler for applying quantum computing to industrial use cases. This poses challenges to quantum software engineering in providing efficient and effective means to develop such software. Eventually, this must be reliably achieved in time, on budget, and in quality, using sound and well-principled engineering approaches. Given that quantum computers are based on fundamentally different principles than classical machines, this raises the question if, how, and to what extent established techniques for systematically engineering software need to be adapted. In this chapter, we analyze three paradigmatic application scenarios for quantum software engineering from an industrial perspective. The respective use cases center around (1) optimization and quantum cloud services, (2) quantum simulation, and (3) embedded quantum computing. Our aim is to provide a concise overview of the current and future applications of quantum computing in diverse industrial settings. We derive presumed challenges for quantum software engineering and thus provide research directions for this emerging field.

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Quantum systems have started to emerge as a disruptive technology and enabling platforms - exploiting the principles of quantum mechanics - to achieve quantum supremacy in computing. Academic research, industrial projects (e.g., Amazon Braket), and consortiums like 'Quantum Flagship' are striving to develop practically capable and commercially viable quantum computing (QC) systems and technologies. Quantum Computing as a Service (QCaaS) is viewed as a solution attuned to the philosophy of service-orientation that can offer QC resources and platforms, as utility computing, to individuals and organisations who do not own quantum computers. To understand the quantum service development life cycle and pinpoint emerging trends, we used evidence-based software engineering approach to conduct a systematic mapping study (SMS) of research that enables or enhances QCaaS. The SMS process retrieved a total of 55 studies, and based on their qualitative assessment we selected 9 of them to investigate (i) the functional aspects, design models, patterns, programming languages, deployment platforms, and (ii) trends of emerging research on QCaaS. The results indicate three modelling notations and a catalogue of five design patterns to architect QCaaS, whereas Python (native code or frameworks) and Amazon Braket are the predominant solutions to implement and deploy QCaaS solutions. From the quantum software engineering (QSE) perspective, this SMS provides empirically grounded findings that could help derive processes, patterns, and reference architectures to engineer software services for QC.
Abstract Quantum software is becoming a key enabler for applying quantum computing to industrial use cases. This poses challenges to quantum software engineering in providing efficient and effective means to develop such software. Eventually, this must be reliably achieved in time, on budget, and in quality, using sound and well-principled engineering approaches. Given that quantum computers are based on fundamentally different principles than classical machines, this raises the question if, how, and to what extent established techniques for systematically engineering software need to be adapted. In this chapter, we analyze three paradigmatic application scenarios for quantum software engineering from an industrial perspective. The respective use cases center around (1) optimization and quantum cloud services, (2) quantum simulation, and (3) embedded quantum computing. Our aim is to provide a concise overview of the current and future applications of quantum computing in diverse industrial settings. We derive presumed challenges for quantum software engineering and thus provide research directions for this emerging field.