This paper introduces a Hybrid Quantum Genetic Algorithm (HQGA-strong), which utilizes strongly entangled quantum states in the recombination process to enhance the coverage area of unmanned aerial vehicles (UAVs) in disaster-affected areas. The HQGA-strong is a fusion of quantum computing benefits and traditional methods, creating a quantum population that improves the recombination process. Moreover, a fuzzy inference system is implemented to determine the most suitable angle for the quantum rotation gate, instead of a conventional lookup table or mathematical equation. To showcase the advantages of quantum genetic algorithms in terms of accuracy and convergence time compared to classical genetic algorithms, various statistical tests were conducted. Depending on the data distribution, both parametric and non-parametric tests were utilized. The results demonstrated that the HQGA-strong performed comparably in accuracy to its quantum counterpart. The hybrid nature of the algorithm is highlighted by its operation: initialization and measurement processes are conducted on a quantum computer, while the remainder of the process runs on a classical computer. Two experimental scenarios were designed, termed “best case” and “worst case,” each assessing different aspects of algorithmic performance.