Quantum Machine Learning for Chemistry and Physics

TL;DR

This review discusses recent advances in machine learning and quantum machine learning applied to chemistry and physics, highlighting their impact on material design, electronic structure calculations, and chemical reactivity.

Contribution

It provides a comprehensive overview of classical and quantum ML algorithms in physical sciences, emphasizing their recent developments and potential for cross-disciplinary research.

Findings

ML and DL have revolutionized material design and chemistry.

Quantum ML algorithms show promising results on near-term hardware.

These techniques enhance understanding of phase transitions and reaction dynamics.

Abstract

Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to conclude that ML and its close cousin deep learning (DL) have ushered unprecedented developments in all areas of physical sciences especially chemistry. Not only the classical variants of ML , even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionzed material design and performance of photo-voltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is to not only to foster exposition to the aforesaid techniques but also to empower and promote cross-pollination among future-research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.