Quantum computing represents a significant advancement in the tech industry, offering researchers a new avenue to explore through the development of novel programming languages and frameworks tailored for the quantum domain. However, as quantum programming evolves, so does the challenge of software errors and bugs introduced during code writing. These bugs can compromise program performance and alter the intended output. In response to this challenge, researchers have been developing various tools for detecting quantum bugs. This study focuses on analyzing three major quantum bug-detecting tools: QChecker, QSmell, and QPAC. To evaluate their effectiveness, the study utilizes the Bugs4Q benchmark dataset, which comprises 36 quantum software snippets with diverse bugs. The dataset undergoes preprocessing before being simultaneously applied to all three tools to assess their bug-detection capabilities. Subsequently, the tools are compared and analyzed based on their results to ascertain their efficiency, advantages, and disadvantages. The comparison primarily centers on performance and tool utilization efficiency. The findings indicate that, in both scenarios, QChecker demonstrates superior effectiveness compared to the other tools, emerging as the most efficient solution for detecting potential threats posed by bugs in quantum programs.