Operator quantum machine learning: Navigating the chemical space of response properties

TL;DR

This paper discusses an operator formalism in quantum machine learning that enhances data efficiency for predicting chemical response properties across chemical compound space, aiming to accelerate quantum property predictions without losing accuracy.

Contribution

The paper introduces and reviews an operator formalism that significantly improves data efficiency in QML models for chemical response properties.

Findings

Operator formalism enhances data efficiency in QML models

Improved predictions across chemical compound space

Potential for faster quantum property estimation

Abstract

The identification and use of structure property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves the data efficiency for QML models of common response properties.