  Computational models are an essential tool for the design, characterization,
and discovery of novel materials. Hard computational tasks in materials science
stretch the limits of existing high-performance supercomputing centers,
consuming much of their simulation, analysis, and data resources. Quantum
computing, on the other hand, is an emerging technology with the potential to
accelerate many of the computational tasks needed for materials science. In
order to do that, the quantum technology must interact with conventional
high-performance computing in several ways: approximate results validation,
identification of hard problems, and synergies in quantum-centric
supercomputing. In this paper, we provide a perspective on how quantum-centric
supercomputing can help address critical computational problems in materials
science, the challenges to face in order to solve representative use cases, and
new suggested directions.
