Evaluation of Phase Networks in Transformer-Based Neural Network Quantum States (Communications in Computational Chemistry, 2025)
Neural network quantum states represent a powerful approach for solving electronic structures in strongly correlatedmolecular and material systems. For a neural network ansatz to be accurate, it must effectively learn the phase of a complex wavefunction. In this work, we demonstrate several different network structures as the phase network for a Transformer-based neuralnetwork quantum state implementation. We compare the accuracy of ground state energies, the number of parameters, andcomputational time across several small molecules. Furthermore, we propose a phase network setup that combines cross attentionand multilayer perceptron structures, with the number of parameters remaining constant across different systems. Such anarchitecture may help reduce computational costs and enable transfer learning to larger quantum chemical systems.