A simple linear algebra identity to optimize Large-Scale Neural Network Quantum States

Understand

Neural-network architectures have been increasingly used to represent quantum many-body wave functions.

• These networks require a large number of variational parameters and are challenging to optimize using traditional methods, as gradient descent.
• Stochastic Reconfiguration (SR) has been effective with a limited number of parameters, but becomes impractical beyond a few thousand parameters.
• Here, we leverage a simple linear algebra identity to show that SR can be employed even in the deep learning scenario.