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International Conference on Cloud Computing and Services Science

Operations Patterns for Hybrid Quantum Applications

Martin Beisel, Johanna Barzen, Frank Leymann, Benjamin Weder

Published Jan 1, 2025• 2 citations
Publisher
DOI
10.5220/0013246900003950

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Quantum computing provides computational advantages in various domains.To benefit from these advantages complex hybrid quantum applications must be built, which comprise both quantum and classical programs.Engineering these applications requires immense expertise in physics, mathematics, and software engineering.To facilitate the development of quantum applications, a corresponding quantum computing pattern language providing proven solutions to recurring problems has been presented.However, identifying suitable patterns for tackling a specific application scenario and subsequently combining them in an application is a time-consuming manual task.To overcome this issue, we present an approach that enables (i) the automated detection of patterns solving a given problem, (ii) the selection of suitable implementations fulfilling non-functional requirements of the user, and (iii) the automated aggregation of these solutions into an executable quantum application.
Quantum computing is quickly becoming a potentially disruptive technology that can tackle the problems inaccessible to classical computers, but the use of quantum computing in real software systems has yet to be fully explored. Present day quantum studies are largely focused on the development of algorithms and hardware, whereas engineering workflows to be used to enable smooth hybrid quantum-classical operations attract limited attention. The paper suggests a concept of quantum-ready software engineering- a systematic approach to applying established principles of software engineering to hybrid systems. In order to meet maintainability, scalability, and reliability objectives in hybrid applications, propose the adoption of a multilayered design, such as abstraction, integration, orchestration, and verification. An example concrete programming template, written in Python in Qiskit, demonstrates how quantum functions being used as modular services can be simulated with classical software. Evaluation measures (maintainability index, latency, and modularity) demonstrate the feasibility of the framework. Results indicate that the hybrid pipelines can be designed in a systematic way using quantum-classical-adapted design patterns. The study introduces a repeatable methodology, program templates, and design pattern engineering principles, which open the door to additional development of quantum DevOps and design pattern formalization. This project, by bridging the gap between classical software engineering and quantum integration, gives a platform upon which to build extraordinarily reliable, scalable systems in the Noisy Intermediate-Scale Quantum and beyond.
The exponential growth of high-dimensional data across scientific, financial, and technological domains has intensified the demand for scalable pattern recognition systems capable of operating under noise, uncertainty, and computational constraints. Classical machine learning techniques, while effective in many contexts, often encounter limitations related to dimensionality, feature sparsity, and optimization complexity. This paper explores hybrid quantum–classical machine learning architectures for scalable pattern recognition in high-dimensional and noisy data environments, emphasizing the synergistic integration of quantum computational principles with established classical algorithms. By leveraging quantum feature mapping, variational quantum circuits, and classical optimization frameworks, hybrid architectures aim to enhance representational capacity and computational efficiency. The study synthesizes recent advances in quantum machine learning and evaluates their potential to address scalability and robustness challenges in real-world data analytics. The findings suggest that hybrid quantum–classical systems may serve as a complementary paradigm that bridges theoretical quantum advantage with practical machine learning applications.