Group Convolutional Neural Networks Improve Quantum State Accuracy

Understand

Neural networks are a promising tool for simulating quantum many body systems.

• Recently, it has been shown that neural network-based models describe quantum many body systems more accurately when they are constrained to have the correct symmetry properties.
• In this paper, we show how to create maximally expressive models for quantum states with specific symmetry properties by drawing on literature from the machine learning community.
• We implement group equivariant convolutional networks (G-CNN) \cite{cohen2016group}, and demonstrate that performance improvements can be achieved without increasing memory use.