  In molecular research, simulation \& design of molecules are key areas with
significant implications for drug development, material science, and other
fields. Current classical computational power falls inadequate to simulate any
more than small molecules, let alone protein chains on hundreds of peptide.
Therefore these experiment are done physically in wet-lab, but it takes a lot
of time \& not possible to examine every molecule due to the size of the search
area, tens of billions of dollars are spent every year in these research
experiments. Molecule simulation \& design has lately advanced significantly by
machine learning models, A fresh perspective on the issue of chemical synthesis
is provided by deep generative models for graph-structured data. By optimising
differentiable models that produce molecular graphs directly, it is feasible to
avoid costly search techniques in the discrete and huge space of chemical
structures. But these models also suffer from computational limitations when
dimensions become huge and consume huge amount of resources. Quantum Generative
machine learning in recent years have shown some empirical results promising
significant advantages over classical counterparts.
