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Mining Quantum Software Patterns in Open-Source Projects

Neilson Carlos Leite Ramalho, Erico A. da Silva, Higor Amario de Souza, Marcos Lordello Chaim

TL;DR

Quantum software engineering tackles the gap between low-level circuit construction and practical, real-world quantum applications. The authors perform an empirical study of 985 Jupyter Notebooks from 80 open-source projects, building a knowledge base from Classiq, PennyLane, and Qiskit, and extend the pattern catalog by nine new patterns to 61. They deploy a reusable semantic search tool based on sentence-transformer embeddings to detect patterns across the dataset, finding 573 matches across 251 files and 22 of 24 framework patterns represented, including all nine new patterns. The results reveal a three-level abstraction in practice—foundational utilities, algorithmic building blocks, and domain-specific applications—signaling a maturing ecosystem and offering a practical reference for researchers and practitioners.

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.

Mining Quantum Software Patterns in Open-Source Projects

TL;DR

Quantum software engineering tackles the gap between low-level circuit construction and practical, real-world quantum applications. The authors perform an empirical study of 985 Jupyter Notebooks from 80 open-source projects, building a knowledge base from Classiq, PennyLane, and Qiskit, and extend the pattern catalog by nine new patterns to 61. They deploy a reusable semantic search tool based on sentence-transformer embeddings to detect patterns across the dataset, finding 573 matches across 251 files and 22 of 24 framework patterns represented, including all nine new patterns. The results reveal a three-level abstraction in practice—foundational utilities, algorithmic building blocks, and domain-specific applications—signaling a maturing ecosystem and offering a practical reference for researchers and practitioners.

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.
Paper Structure (19 sections, 1 figure, 4 tables)

This paper contains 19 sections, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Quantum Computing Software Patterns
  4. Quantum Computing Patterns Atlas
  5. Mining QC patterns
  6. Identify framework-specific concepts
  7. Classiq
  8. PennyLane
  9. Qiskit
  10. Pattern matching and consolidation
  11. New Quantum Computing patterns
  12. GitHub search for QC projects and repositories
  13. QC pattern matching in GitHub repositories
  14. Results and discussion
  15. A Hierarchy of abstractions in practice
  16. ...and 4 more sections

Figures (1)

  • Figure 1: Overview of the mining process. It consists of four main stages: (1) Identifying QC concepts in selected frameworks, (2) Matching those concepts to a known atlas of patterns, (3) Searching GitHub for a broad set of QC projects, and (4) Matching the identified patterns against the code from the GitHub repositories.