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Bulletin of the American Physical Society

Si based quantum computer architecture and associated engineering challenges

M. Vinet, Pierre-André Mortemousque, Louis Hutin, Benoît Bertrand, X. Jehl, Yann‐Michel Niquet, Gaël Pillonnet, G. Billiot, M. Sanquer, Baptiste Jadot, Emmanuel Chanrion, Matias Urdampilleta, Tristan Meunier

Published Mar 3, 2020• 0 citations
OpenAlex

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Quantum software engineering (QSE) has emerged as a discipline to address the challenges associated with developing software systems in the context of advancing quantum computing technology. This paper explores architectural design patterns specific to QSE. Building on previous work highlighting the need for high-level patterns, we analyze emerging architectural approaches in quantum computing. We identify key challenges unique to hybrid quantum-classical systems and propose a preliminary taxonomy of QSE architectural patterns. These patterns address recurring design problems in areas such as quantum resource management and classical-quantum integration. We describe several potential patterns and discuss their theoretical applications in hybrid software systems. This work aims to provide a foundation for further research into standardizing QSE architectural practices, with the goal of enhancing various quality characteristics of hybrid software (e.g., modularity or maintainability) in the NISQ era and beyond.
Quantum Software Engineering (QSE) is an emerging discipline devoted to specifying, designing, implementing, and deploying software for quantum computers. As quantum mechanics and hybrid architecture are evolving at a rapid pace, security challenges are also emerging. This paper investigates the key QSE security concerns, classifying them into four major families: crosstalk, side-channel, fault injection, and pulse-level attacks. For each of these threats, we explore their place of origin, consequences, and available specimens. In response, we map mitigation approaches such as dynamical decoupling, randomized compiling, transpilation masking, secure lifecycle models, frameworks, and associated tools.
Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversarial attacks, and reduce the number of parameters without compromising accuracy. However, moving beyond proof-of-concept or simulations to develop practical applications of these systems while ensuring high software quality faces significant challenges due to the limitations of quantum hardware and the underdeveloped knowledge base in software engineering for such systems. In this work, we have conducted a systematic mapping study to identify the challenges and solutions associated with the software architecture of quantum-enhanced artificial intelligence systems. The results of the systematic mapping study reveal several architectural patterns that describe how quantum components can be integrated into inference engines, as well as middleware patterns that facilitate communication between classical and quantum components. Each pattern realises a trade-off between various software quality attributes, such as efficiency, scalability, trainability, simplicity, portability, and deployability. The outcomes of this work have been compiled into a catalogue of architectural patterns. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board .