This paper provides a review on quantum computing for computational problems in materials science and a perspective on the challenges to face in order to solve representative use cases, and new suggested directions.
We develop a performance model to project the classical hardware requirements required for real-time decoding of large-scale quantum computations. Based on this model, we estimate that the equivalent of a petaflop-scale system will be required for real-time decoding of applications relavent to condensed matter physics and quantum chemistry.
This paper proposes a nested state preparation circuit construction with a high degree of quantum parallelism. We test this circuit to load a variety of data sets stemming from applications and process them directly on a real QPU.
In this paper, we explore the use of real-time evolution for computing the trace of a broad class of operators, including matrix functions of the target Hamiltonian.