A structural approach to the design of quantum algorithms based on the composition of modules

P. V. Zrelov, O.V. Ivantsova

Abstract


This article explores a structured approach to designing quantum algorithms based on module composition. It argues for the need to develop high-level abstractions to reduce the cognitive barrier for specialists experienced with classical computing and to facilitate the active implementation of quantum methods in various subject areas. This article presents an analysis of modern quantum computing languages and platforms that support modularity through operation libraries and hybrid integration with classical programs. A methodology for the step-by-step design of quantum circuits is presented, consisting of a consistent transition from high-level templates to practical implementation on simulators and quantum computers. A technology for applying a modular approach is described, which allows the use of ready-made, optimized library functions, simplifying and accelerating algorithm development. The application of this methodology is illustrated by formalizing compositional rules for built-in templates of the PennyLane framework and creating a hybrid quantum-classical pipeline for data analysis. Practical testing of the proposed approach on a classification problem using machine learning methods demonstrated that its effectiveness is at least as good as the best classical solutions. The article discusses the potential for developing a structured approach as a basis for developing quantum software design standards. The practical value of the article's materials for specialists in the fields of quantum computing, machine learning, and data analysis is emphasized.

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References


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