Communication efficiency enhanced federated learning derived from quantum reinforcement learning for retrosynthesis

The combination of parametric quantum circuits and density matrix coding can significantly reduce the number of parameters in artificial neural networks. The reduction in the number of model parameters helps to improve the commu- nication efficiency when training deep learning models under federated learning architectures. In this study, we showcase the enhanced communication efficiency achieved in federated learning by utilizing quantum neural networks in the context of the molecular inverse synthesis task within reinforcement learning. Specifically, we consider the federated learning task on the reinforcement learning-based retrosynthesis. we adopted the USPTO-50k chemical reaction dataset. the MLP and quantum neural network are used as the agent of the reinforcement learning algorithm, respectively. Enhancements in communica- tion efficiency stem from the capacity of encoded quantum states within quantum neural networks to effectively represent data. All instances are additionally situated within the framework of ligand molecules associated with Tau proteins.