The Multi-Objective Artificial Physics Optimization algorithm faces significant challenges due to its population-dependent update mechanism. This dependency often restricts the exploration of the search space, potentially leading to premature convergence to local optimization and a reduction in solution diversity, thus limiting its effectiveness in complex multi-objective problems. To address these issues, this paper introduces a novel quantum computing-based MOAPO algorithm. By leveraging quantum parallelism and superposition, the proposed method aims for more effective exploration. Specifically, it employs quantum state initialization to establish a diverse initial population, then utilizes an angle-based quantum space rotation mechanism to refine solution updates and guide the search trajectory. This hybrid strategy integrates the global exploration of quantum computing with the local exploitation advantages of APO forces, effectively enhancing the algorithm’s ability to escape local optimization and maintain solution diversity. Extensive comparisons against seven algorithms on twenty-one benchmark functions demonstrate that the proposed quantum-based MOAPO significantly improves both Pareto front diversity and convergence performance, indicating its potential for solving complex multi-objective optimization problems.