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

Julian Shen, Joshua Ammermann, Christoph König, Ina Schaefer

TL;DR

The paper tackles the challenge of making quantum software more understandable and maintainable by automatically detecting eight practical quantum computing patterns in source code. It introduces a framework that combines state-based (dynamic) and circuit-based (static) analyses, supported by a benchmarking dataset of 20 quantum algorithms, and demonstrates superior detection accuracy compared to prior work. Key findings show state-based detectors achieve perfect precision/recall/F1 for some patterns, while circuit-based detectors offer scalable performance, with the overall system outperforming Pérez-Castillo et al.’s approach in both coverage (eight vs five patterns) and accuracy. The work lays groundwork for using patterns as high-level building blocks in quantum software, and points to future expansion to more patterns and automatic identification of code passages that lack pattern coverage.

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.

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

TL;DR

The paper tackles the challenge of making quantum software more understandable and maintainable by automatically detecting eight practical quantum computing patterns in source code. It introduces a framework that combines state-based (dynamic) and circuit-based (static) analyses, supported by a benchmarking dataset of 20 quantum algorithms, and demonstrates superior detection accuracy compared to prior work. Key findings show state-based detectors achieve perfect precision/recall/F1 for some patterns, while circuit-based detectors offer scalable performance, with the overall system outperforming Pérez-Castillo et al.’s approach in both coverage (eight vs five patterns) and accuracy. The work lays groundwork for using patterns as high-level building blocks in quantum software, and points to future expansion to more patterns and automatic identification of code passages that lack pattern coverage.

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.

Paper Structure

This paper contains 18 sections, 3 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Motivation and Objectives
  3. Background
  4. Quantum Computing
  5. Patterns for quantum computing
  6. State- and Circuit-based Analysis for Quantum Pattern Detection
  7. Creating Entanglement (State-based analysis)
  8. Uncompute (Circuit-based analysis)
  9. Evaluation
  10. Subject Systems and Ground Truth
  11. Methodology
  12. Results
  13. Discussion
  14. Threats to Validity
  15. Internal Validity
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Quantum circuit with two time slices for the creation of an entangled state using a Hadamard gate followed by a CNOT gate Nielsen.2011.
  • Figure 3: Average execution times of different detection algorithms depending on the circuit width and depth.
  • Figure 4: Comparison of the total number of patterns detected between our framework and the implementation of Pérez-Castillo et al. Perez.2024. The results of the patterns that both approaches can detect are shown to the left of the dashed line, while those that can only be recognized by one implementation are shown to the right. The abbreviations INI and OR correspond to the patterns Initialization and Oracle Leymann.2019.