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 nine 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.

Although classical computing has excelled in a wide range of applications, there remain problems that push the limits of its capabilities, especially in fields like cryptography, optimization, and materials science. Quantum computing introduces a new computational paradigm, based on principles of superposition and entanglement to explore solutions beyond the capabilities of classical computation. With the increasing interest in the field, there are challenges and opportunities for academics and practitioners in terms of software engineering practices, particularly in testing quantum programs. This paper presents an empirical study of testing patterns in quantum algorithms. We analyzed all the tests handling quantum aspects of the implementations in the Qiskit Algorithms library and identified seven distinct patterns that make use of (1) fixed seeds for algorithms based on random elements; (2) deterministic oracles; (3) precise and approximate assertions; (4) Data-Driven Testing (DDT); (5) functional testing; (6) testing for intermediate parts of the algorithms being tested; and (7) equivalence checking for quantum circuits. Our results show a prevalence of classical testing techniques to test the quantum-related elements of the library, while recent advances from the research community have yet to achieve wide adoption among practitioners.