As shown in Fig. , a neural network is employed to predict the reaction type, and this information is deposited into the experience pool (Buffer). The neural network parameters are continuously updated using data from the reaction database. The algorithmic flow is described in the Methods section. Besides, we have replaced this MLP neural network (Fig. ) with a quantum neural network (Fig. ). In our quantum neural network, the density matrix of this quantum state is determined through the Hermitian matrix calculation, which is related to structural information of the molecule. The visual depiction of the quantum circuits in our quantum neural network can be observed in Fig. , illustrating each layer comprising rotation operators of Pauli gates and controlled operators. For constructing the loss function, we rely on the final state following quantum state evolution and its corresponding measured values. Throughout the reinforcement learning training process, our quantum neural network continuously adjusts the learnable phase information. At the present stage, quantum neural networks are executed within a quantum simulator environment.