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On Refactoring Quantum Programs
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Refactoring is a crucial technique for improving the efficiency and maintainability of software by restructuring its internal design while preserving its external behavior. While classical programs have benefited from various refactoring methods, the field of quantum programming lacks dedicated refactoring techniques. The distinct properties of quantum computing, such as quantum superposition, entanglement, and the no-cloning principle, necessitate specialized refactoring techniques. This paper bridges this gap by presenting a comprehensive set of refactorings specifically designed for quantum programs. Each refactoring is carefully designed and explained to ensure the effective restructuring of quantum programs. Additionally, we highlight the importance of tool support in automating the refactoring process for quantum programs. Although our study focuses on the quantum programming language Q\#, our approach is applicable to other quantum programming languages, offering a general solution for enhancing the maintainability and efficiency of quantum software.
Forward citations
Cited by 2 Pith papers
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Automatic Qiskit Code Refactoring Using Large Language Models
A structured taxonomy of Qiskit migration scenarios improves GPT-4's line-level refactoring precision from 0.32 to 0.55 and recall from 0.35 to 0.62 on 25 synthetic snippets.
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Taxonomy of migration scenarios for Qiskit refactoring using LLMs
LLMs can generate a structured taxonomy of Qiskit migration and refactoring scenarios that largely overlaps with an expert-built taxonomy and adds some scenarios.
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