A quantum mechanics-based framework for infectious disease modeling.

Traditional infectious disease models often use fixed compartments to represent different states of individuals. However, these models can be limited in accurately reflecting real-world conditions of individuals. In this study, we integrate quantum mechanics into infectious disease modeling, developing a quantum mechanics-based model that effectively addresses the limitations of traditional compartmental models and introduces a novel approach to understanding disease dynamics. Firstly, we examined the individual infection process and the model's evolutionary dynamics, deriving both the disease-free equilibrium point and the model's basic reproduction number. Secondly, the proposed model is simulated on a quantum circuit. The simulation results are utilized to analyze the model's parameter sensitivity and verify its rationality. The results indicate that the model's predictions align with the general patterns of viral transmission and are capable of replicating the structural attributes of compartmental models. Finally, we apply the model to simulate the spread of COVID-19. The observed similarity between the simulated results and actual infection trends demonstrates the model's effectiveness in accurately capturing viral transmission dynamics. Comparative experiments show that the proposed model significantly improves accuracy over traditional models. By leveraging quantum mechanics, our method offers a fresh perspective in infectious disease modeling, broadening the application of quantum mechanics methodologies in understanding information propagation within the macroscopic world.