In his seminal paper, 'Simulating physics with computers', Richard Feynman argued that very complex problems, including the many-body problem, would require a new paradigm to be solved. In an era driven by data, there is strong urge to tackle problems so complex - involving a very large number of variables, millions of paths to chose from - tasks too complicated that even the most powerful classical processor and supercomputers cannot handle. These practical needs, combined with advances in our ability to manipulate atomic systems, have enable the developments in quantum computing we have been witnessing. Who would have imagined how much progress would be achieved over the past two decades? To make the much-hyped promise of quantum advantage (aka supremacy) a reality, there is still a lot of work to be done at both hardware and software levels. While limitations in current quantum architectures - specifically scalability and noise - hinder practical applications with true advantage, a new question is posed: what can we do to make the most of classical and quantum algorithms?