In our approach, molecules undergoing processing are assigned a molecular feature vector using the ECFP fingerprint, resulting in a vector of length F = 255. This vector, along with the depth value of the reaction path, collectively generates a 256-bit molecular features. Subsequently, following the quantum information processing procedure, we derive a 8-qubit input quantum state for the parameterized quantum circuits, considering log(F ) = log256 = 8. The number of parameters in an MLP algorithm, which employs an inputhidden-output layer structure, is chiefly determined by input features and the dimensions of the hidden and output layers. However, employing the same molecular description vector with the density matrix of a 8-bit quantum state obtained after quantum information processing significantly reduces the number of parameters in the quantum neural network. While the quantum neural network effectively reduces its parameter count to address the same task, it demonstrates the capability to execute the training task within the federated learning framework. We were able to find a comparison between Federated Reinforcement Learning(FRL) and Federated Quantum Reinforcement Learning(FQRL) in the Fig. . Due to the decreased parameters in the quantum neural network, the communication between the server and the client per round is now only 0.1 percent of its previous value. Simultaneously, we conducted synthetic route planning for molecules exhibiting the highest affinity energy values predicted by DeepDTA, with the results depicted in Fig. . To further understand the retrosynthesis in Fig. ., DFT calculations by ORCA 5.0.1 at B3LYP - /def2-SVP level of theory conjunction with the SMD continuum solvation model in the solvent of Chlorobenzene and Grimme's D3 - dispersion corrections were performed. The calculations show the reaction is exothermic by 7.5 kcal mol-1, confirming the reaction is favorable thermodynamically.