Operator Learning Renormalization Group

Understand

In this paper, we present a general framework for quantum many-body simulations called the operator learning renormalization group (OLRG).

• Inspired by machine learning perspectives, OLRG is a generalization of Wilson's numerical renormalization group and White's density matrix renormalization group, which recursively builds a simulatable system to approximate a target system of the same number of sites via operator maps.
• OLRG uses a loss function to minimize the error of a target property directly by learning the operator map in lieu of a state ansatz.
• This loss function is designed by a scaling consistency condition that also provides a provable bound for real-time evolution.