The Maximum Power Point Tracking method is a mainstream method for improving the operational efficiency of photovoltaic power generation, but it is difficult to adapt to the rapidly changing environment and lacks good steady-state and dynamic performance. To achieve fast and accurate tracking of the Maximum Power Point Tracking, the optimization of the contraction expansion coefficient of the Quantum Particle Swarm Optimization algorithm is studied, and then the Levy flight strategy is introduced to optimize the algorithm’s global convergence ability, thereby constructing the Hybrid Quantum Particle Swarm Optimization algorithm. Finally, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm is obtained. The research results showed that the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm can always converge to the theoretical minimum value with a probability of more than 94% in the Roserock function and Rastigin function tests. The tracking error of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm was less than 1% under lighting conditions. The convergence time of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm in arbitrary shadow occlusion environments can reach a stable state within 0.1 s. In summary, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm proposed in the study has excellent performance and very wide applicability. To a certain extent, it improves the total power generation capacity of the photovoltaic power generation system and the power generation efficiency of the photovoltaic array.