# Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning

## TL;DR

This paper shows how machine learning can improve the efficiency of a quantum chemistry method for studying complex systems.

## Contribution

A simple ML model is shown to significantly enhance the performance of the quantum chemical DMRG method.

## Key findings

- A simple ML model can boost the performance of the DMRG method.
- High computational efficiency is maintained without sacrificing accuracy.
- The Δ-ML approach is promising for quantum chemical calculations.

## Abstract

The use of machine
learning (ML) to refine low-level theoretical
calculations to achieve higher accuracy is a promising and actively
evolving approach known as Δ-ML. The density matrix renormalization
group (DMRG) is a powerful variational approach widely used for studying
strongly correlated quantum systems. High computational efficiency
can be achieved without compromising accuracy. Here, we demonstrate
the potential of a simple ML model to significantly enhance the performance
of the quantum chemical DMRG method.