With the continuous advancement of quantum computing (QC), the demand for high-quality quantum programs (QPs) is growing. To avoid program failure, in software engineering, the technology of automatic program repair (APR) employs appropriate patches to remove potential bugs without the intervention of a human. However, the method tailored for repairing defective QPs is still absent. This article proposes, to the best of our knowledge, a new APR method named UnitAR that can repair QPs via unitary operation automatically. Based on the characteristics of superposition and entanglement in QC, the article constructs an algebraic model and adopts a generate-and-validate approach for the repair procedure. Furthermore, the article presents two schemes that can respectively promote the efficiency of generating patches and guarantee the effectiveness of applying patches. For the purpose of evaluating the proposed method, the article selects 29 mutated versions as well as five real-world buggy programs as the objects and introduces two traditional APR approaches GenProg and TBar as baselines. According to the experiments, UnitAR can fix 23 buggy programs, and this method demonstrates the highest efficiency and effectiveness among three APR approaches. Besides, the experimental results further manifest the crucial roles of two constituents involved in the framework of UnitAR .
With the continuous advancement of quantum computing (QC), the demand for high-quality quantum programs (QPs) is growing. To avoid program failure, in software engineering, the technology of automatic program repair (APR) employs appropriate patches to remove potential bugs without the intervention of a human. However, the method tailored for repairing defective QPs is still absent. This article proposes, to the best of our knowledge, a new APR method named UnitAR that can repair QPs via unitary operation automatically. Based on the characteristics of superposition and entanglement in QC, the article constructs an algebraic model and adopts a generate-and-validate approach for the repair procedure. Furthermore, the article presents two schemes that can respectively promote the efficiency of generating patches and guarantee the effectiveness of applying patches. For the purpose of evaluating the proposed method, the article selects 29 mutated versions as well as five real-world buggy programs as the objects and introduces two traditional APR approaches GenProg and TBar as baselines. According to the experiments, UnitAR can fix 23 buggy programs, and this method demonstrates the highest efficiency and effectiveness among three APR approaches. Besides, the experimental results further manifest the crucial roles of two constituents involved in the framework of UnitAR .
Automated Program Repair (APR) is a vital area in software engineering that generates automatic patches for vulnerable programs. While numerous techniques have been proposed for repairing classical programs, quantum programming lacks a comparable automated repair technique. In this initial exploration, we investigate the use of ChatGPT for quantum program repair and evaluate its performance on Bugs4Q, a benchmark suite of quantum program bugs. Our findings demonstrate the feasibility of employing ChatGPT for quantum program repair. Specifically, we assess ChatGPT's ability to address bugs within the Bugs4Q benchmark, revealing its success in repairing 29 out of 38 bugs. This research represents a promising step towards automating the repair process for quantum programs.
In recent years, Automated Program Repair (APR) techniques specifically designed for quantum programs have been proposed. However, existing approaches often suffer from low repair success rates or poor understandability of the generated patches. In this study, we construct a framework in which a large language model (LLM) generates code repairs along with a natural language explanation of the applied repairs. To investigate how the contextual information included in prompts influences APR performance for quantum programs, we design four prompt configurations with different combinations of static information, dynamic information, and mutation analysis results. Mutation analysis evaluates how small changes to specific parts of a program affect its execution results and provides more detailed dynamic information than simple execution outputs such as stack traces. Our experimental results show that mutation analysis can provide valuable contextual information for LLM-based APR of quantum programs, improving repair success rates (achieving 94.4% in our experiment) and in some cases also improving the quality of generated explanations. Our findings point toward new directions for developing APR techniques for quantum programs that enhance both reliability and explainability.
Quantum programs provide exponential speedups compared to classical programs in certain areas, but they also inevitably encounter logical faults. Automatically repairing quantum programs is much more challenging than repairing classical programs due to the non-replicability of data, the vast search space of program inputs, and the new programming paradigm. Existing works based on semantic-based or learning-based program repair techniques are fundamentally limited in repairing efficiency and effectiveness. In this work, we propose HornBro, an efficient framework for automated quantum program repair. The key insight of HornBro lies in the homotopy-like method, which iteratively switches between the classical and quantum parts. This approach allows the repair tasks to be efficiently offloaded to the most suitable platforms, enabling a progressive convergence toward the correct program. We start by designing an implication assertion pragma to enable rigorous specifications of quantum program behavior, which helps to generate a quantum test suite automatically. This suite leverages the orthonormal bases of quantum programs to accommodate different encoding schemes. Given a fixed number of test cases, it allows the maximum input coverage of potential counter-example candidates. Then, we develop a Clifford approximation method with an SMT-based search to transform the patch localization program into a symbolic reasoning problem. Finally, we offload the computationally intensive repair of gate parameters to quantum hardware by leveraging the differentiability of quantum gates. Experiments suggest that HornBro increases the repair success rate by more than 62.5 % compared to the existing repair techniques, supporting more types of quantum bugs. It also achieves 35.7 × speedup in the repair and 99.9 % gate reduction of the patch.
Context: Bug-fix pattern detection has been investigated in the past in the context of classical software. However, while quantum software is developing rapidly, the literature still lacks automated methods and tools to identify, analyze, and detect bug-fix patterns. To the best of our knowledge, our work previously published in SEKE'23 was the first to leverage classical techniques to detect bug-fix patterns in quantum code. Objective: To extend our previous effort, we present a research agenda (Q-Repair), including a series of testing and debugging methodologies, to improve the quality of quantum software. The ultimate goal is to utilize machine learning techniques to automatically predict fix patterns for existing quantum bugs. Method: As part of the first stage of the agenda, we extend our initial study and propose a more comprehensive automated framework, called Q-PAC, for detecting bug-fix patterns in IBM Qiskit quantum code. In the framework, we develop seven bug-fix pattern detectors using abstract syntax trees, syntactic filters, and semantic checks. Results: To demonstrate our method, we run Q-PAC on a variety of quantum bug-fix patterns using both real-world and handcrafted examples of bugs and fixes. The experimental results show that Q-PAC can effectively identify bug-fix patterns in IBM Qiskit. Conclusion: We hope our initial study on quantum bug-fix detection can bring awareness of quantum software engineering to both researchers and practitioners. Thus, we also publish Q-PAC as an open-source software on GitHub. We would like to encourage other researchers to work on research directions (such as Q-Repair) to improve the quality of the quantum programming.
Large language models have recently advanced automated program repair, yet most existing approaches provide only post-hoc naturallanguage explanations that are neither executable nor verifiable.This limitation is especially critical for quantum programs, where correctness hinges on subtle semantic properties such as circuit equivalence and fidelity preservation.We propose Explainable Quantum Program Repair, a framework that couples repair generation with machine-checkable executable explanations.Given a buggy quantum circuit, a language model proposes candidate repairs together with structured transformation rationales, which are compiled into proof traces and validated using formal verification backends, including circuit equivalence checking, ZX-calculus reasoning, stabilizer analysis, and quantum simulation.Only repairs whose explanations are fully verified are accepted.Experiments on QASMBench with mutationgenerated quantum program bugs demonstrate that our approach achieves competitive repair success while substantially improving semantic precision and explanation faithfulness over baselines that rely on unconstrained or purely natural-language explanations.
Bug-fix pattern detection has been investigated in the past in the context of classical software.However, while quantum software is developing rapidly, the literature is still lacking automated methods and tools to identify, analyze, and detect bug-fix patterns.To the best of our knowledge, our work is the first to leverage classical techniques to detect bug-fix patterns in quantum code.In this paper, we propose an automated framework, called Q-Diff, for detecting bug-fix patterns in IBM Qiskit quantum code.In the framework, we develop a proof-of-concept tool based on Abstract Syntax Trees.To validate our method, we test Q-Diff with a variety of quantum bug-fix patterns using examples.We hope our work will attract the attention of the quantum software engineering community to improve the quality of quantum software.