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.