The duality game: a quantum algorithm for body dynamics modeling

In recent years, quantum algorithms have emerged as a groundbreaking approach toward solving complex computational problems, particularly in physical modeling and artificial intelligence. This study introduces a novel quantum algorithm termed the duality game, tailored for addressing challenges in body dynamics modeling. The practicality and efficacy of the proposed algorithm are elucidated through three distinct data scenarios: (1) approximation of classical von Bertalanffy growth in the presence of random noise (simulated), (2) personalized tumor burden modeling leveraging a small dataset, and (3) modeling of COVID-19 population growth employing big data analytics. The algorithm’s performance in these scenarios underscores its potential for practical applications at a large scale. Moreover, the findings foster optimism regarding the algorithm’s promise in the burgeoning field of physical-based quantum artificial intelligence (quantum AI). Through the duality game, a pathway is delineated for addressing real-world problems in body dynamics, opening avenues for further research and development in quantum AI, aimed at harnessing quantum computational advantages for solving intricate physical modeling problems.