  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.
