🤖 AI Summary
This study addresses the limited systematic understanding of real-world quantum software development practices. It presents the first empirical investigation into programming patterns in quantum software by analyzing 80 open-source projects comprising 985 Jupyter Notebooks built with Qiskit, PennyLane, and Classiq. The authors construct a knowledge base of these patterns and develop an automated approach combining semantic search, static analysis, and natural language processing to detect their usage. The work identifies nine previously undocumented patterns and reveals a three-tiered paradigm of quantum programming—from basic circuit construction to domain-specific applications—demonstrating that higher-level abstractions are already widely adopted in fields such as finance and optimization. These findings indicate that quantum software engineering is transitioning toward maturity through the increasing use of structured, high-level development practices.
📝 Abstract
Quantum computing has become an active research field in recent years, as its applications in fields such as cryptography, optimization, and materials science are promising. Along with these developments, challenges and opportunities exist in the field of Quantum Software Engineering, as the development of frameworks and higher-level abstractions has attracted practitioners from diverse backgrounds. Unlike initial quantum frameworks based on the circuit model, recent frameworks and libraries leverage higher-level abstractions for creating quantum programs. This paper presents an empirical study of 985 Jupyter Notebooks from 80 open-source projects to investigate how quantum patterns are applied in practice. Our work involved two main stages. First, we built a knowledge base from three quantum computing frameworks (Qiskit, PennyLane, and Classiq). This process led us to identify and document 9 new patterns that refine and extend the existing quantum computing pattern catalog. Second, we developed a reusable semantic search tool to automatically detect these patterns across our large-scale dataset, providing a practitioner-focused analysis. Our results show that developers use patterns in three levels: from foundational circuit utilities, to common algorithmic primitives (e.g., Amplitude Amplification), up to domain-specific applications for finance and optimization. This indicates a maturing field where developers are increasingly using high-level building blocks to solve real-world problems.