  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.
