Abstract: A quantum hybrid coronavirus herd immunity optimizer (QCHIO) algorithm is proposed to address the issues of local optima trapping, slow convergence speed, and poor convergence accuracy in the Coronavirus herd immunity optimizer (CHIO) algorithm. Firstly, the concept of quantum computing is introduced to achieve the goals of global search and fast convergence through quantum correlations, effectively avoiding the problem of the algorithm getting trapped in local optima. Secondly, the algorithm enhances its global exploration capability by utilizing both large and small step sizes of the Weibull distribution operator to increase algorithm diversity and better explore the search space. Additionally, the hill-climbing operator is introduced to search the neighborhood of the current best solution, attempting to find better solutions and thereby increasing the algorithm’s search breadth and improving the quality of solutions. Multi-neighborhood search further enhances the convergence accuracy of the algorithm by searching multiple neighborhoods of the global optimum. To validate its performance, QCHIO is applied to 10 standard test cases and compared with other improved algorithms, demonstrating its superiority through significant testing. Finally, the feasibility and superiority of QCHIO are further demonstrated by applying it to a case of engine production scheduling.