The increasing maturity of quantum computers has ushered in an era of novel computational tools. The profound impact of quantum computing on drug design has garnered widespread attention. Among these quantum algorithms, we find UCCSD, which relies on the quantum phase estimation algorithms, quantum optimization solvers, and quantum machine learning techniques. The fusion of quantum data advantages with deep learning tasks is poised to assume a pivotal role both presently and in the future. Meanwhile, the application of deep learning in the field of biomedicine hinges upon the availability of high-quality datasets and the imperative of safeguarding data privacy. Enter federated learning, a crucial approach in today's deep learning landscape for addressing data privacy concerns. Notably, this framework is also adaptable for quantum machine learning applications. In the realm of federated learning, the process of training deep learning models locally necessitates communication and updates with servers or other clients, adhering to algorithms like FedAvg. As the data's feature encoding complexity grows, there is a consequential surge in the neural network's parameter count. This, in turn, amplifies the communication overhead associated with training deep learning models within federated learning frameworks. For federated learning to remain practical, it becomes imperative to discover means of substantially reducing model parameters while employing the same data preprocessing techniques, all without compromising model performance significantly. - In this context, it's worth highlighting that quantum neural networks have the potential, under specific conditions, to yield such outcomes. An illustrative case in point is the realm of reinforcement learning applied to molecular inverse synthesis, as mentioned earlier. Within the chemical industry, the plan-ning of chemical reaction pathways assumes paramount significance, particularly in the production of organic molecules. One notable approach, exemplified by Aspuru-Guzik et al., leverages reinforcement learning algorithms in tandem with chemical reaction templates. In their work, the authors employed Multi-Layer Perceptrons (MLP) and molecular fingerprinting to craft an intelligent framework for decisionmaking regarding chemical reaction types. By subsequently converting the internal structure of this intelligent framework into parametric quantum circuits, a remarkable reduction in the number of parameters becomes evident. - To explore the potential utility of quantum neural networks in the context of drug design, we delve into our work centered on small molecule targeted drug design for Tau proteins. Our approach involves utilizing the open-source binding energy prediction model, deepDTA, to identify molecules with the highest affinity for Tau from the USPTO-50k chemical reaction database. Subsequently, we employ a reinforcement learning/quantum reinforcement learning inverse synthesis strategy to dissect the synthetic pathways leading to the desired molecules. Throughout this process, we illustrate how quantum neural networks contribute to heightened communication efficiency when training reinforcement learning inverse synthesis algorithms within the framework of federated learning.