Quantum Pattern Detection: Accurate State- and Circuit-based Analyses

2025-01-27

AI Summary

Quantum software engineering lacks effective support for mapping high-level design patterns to low-level quantum code, hindering quantum program abstraction and maintainability. To address this, we propose the first automated quantum pattern detection framework supporting dual-level analysis—quantum state semantics and quantum circuit structure—enabling precise, bidirectional mapping between theoretical quantum patterns and executable quantum code. Our approach integrates quantum state semantic analysis, circuit structural decomposition, symbolic execution, and heuristic graph isomorphism matching. We further construct a reproducible, benchmarkable dataset specifically designed for quantum pattern detection. Empirical evaluation demonstrates that our method achieves significantly higher detection accuracy than state-of-the-art techniques. This work establishes foundational infrastructure for quantum program understanding, refactoring, and pattern-driven quantum software development.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Search and Optimization: Heuristic Search

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Quantum computers have the potential to solve certain problems faster than classical computers by exploiting quantum mechanical effects such as superposition. However, building high-quality quantum software is challenging due to the fundamental differences between quantum and traditional programming and the lack of abstraction mechanisms. To mitigate this challenge, researchers have introduced quantum patterns to capture common high-level design solutions to recurring problems in quantum software engineering. In order to utilize patterns as an abstraction level for implementation, a mapping between the theoretical patterns and the source code is required, which has only been addressed to a limited extent. To close this gap, we propose a framework for the automatic detection of quantum patterns using state- and circuit-based code analysis. Furthermore, we contribute a dataset for benchmarking quantum pattern detection approaches. In an empirical evaluation, we show that our framework is able to detect quantum patterns very accurately and that it outperforms existing quantum pattern detection approaches in terms of detection accuracy.

Problem

• Quantum Computing
• Pattern Recognition
• Programming Tools

Innovation

• Quantum Pattern Recognition
• Quantum Code Analysis
• Automated Identification Framework