Comparing Machine‐Learned Matrix Product State as a Wavefunction Ansatz vs. Classifier

ABSTRACT The exact solution of strongly correlated systems is a computationally demanding problem, scaling exponentially with system size. Over the decades, several approximate methods have been developed to address this scaling problem. Among these, matrix product state (MPS) has emerged as physically intuitive and polynomially scaling. Methods such as density matrix renormalization group (DMRG), time evolution block decimation (TEBD), etc., use the underlying MPS wavefunction ansatz to optimize it variationally. MPS can also be utilized as a machine learning model to select the most important electronic configurations, as employed in selected configuration interactions. This work uses machine learning (ML) optimization of MPS wavefunctions to eliminate the need for multiple diagonalizations during the optimization process. We compare the performance of the machine‐learned MPS‐assisted selected configuration interaction approach, often mentioned as a classifier, to the machine‐learned MPS wavefunction ansatz itself. While showing the viability of the machine‐learned MPS ansatz, it is observed that within ML, MPS is best used as a selected configuration interaction.