PERM EQ x GRAPH EQ: Equivariant Neural Networks for Quantum Molecular Learning
This work provides incremental guidance for selecting quantum machine learning models for molecular geometry tasks.
The paper compared quantum machine learning models with different symmetry equivariance properties on two molecular datasets (LiH and NH3), finding that permutational symmetric embedding was the most generalizable model for geometric learning and that graph embedding improved trainability.
In hierarchal order of molecular geometry, we compare the performances of Geometric Quantum Machine Learning models. Two molecular datasets are considered: the simplistic linear shaped LiH-molecule and the trigonal pyramidal molecule NH3. Both accuracy and generalizability metrics are considered. A classical equivariant model is used as a baseline for the performance comparison. The comparative performance of Quantum Machine Learning models with no symmetry equivariance, rotational and permutational equivariance, and graph embedded permutational equivariance is investigated. The performance differentials and the molecular geometry in question reveals the criteria for choice of models for generalizability. Graph embedding of features is shown to be an effective pathway to greater trainability for geometric datasets. Permutational symmetric embedding is found to be the most generalizable quantum Machine Learning model for geometric learning.