Quantum computing has shown great potential in a wide range of applications across various fields, including search-based software engineering. Quantum annealing (QA) is a promising quantum algorithm specifically designed for solving combinatorial optimization problems, offering a quantum approach to searching for optimal solutions. In this paper, we designed a quantum-classical hybrid tool, BQTmizer, to solve large-scale real-world test case minimization problems. BQTmizer aims to select the smallest number of test cases among an existing test suite, while satisfying all test objectives. We demonstrated the performance of BQTmizer on two open-source industrial datasets. Results show that BQTmizer can effectively and efficiently reduce the number of selected test cases on large-scale datasets. This paper summarises: “X. Wang, S. Ali and P. Arcaini, BQTmizer: A Tool for Test Case Minimization With Quantum Annealing, in IEEE Software, vol. 42, no. 5, pp. 51-57, Sept.-Oct. 2025, doi: https://doi.org/10.1109/MS.2025.3546511.”