Quantum Kernels for Data Assimilation of Turbulent Flows

Co-processing has played an important role for accelerating computations in the history of supercomputing. Over time, many co-processors become integral parts of modern architectures (floating-point units) or dominate a computation even more than the central processor (distributed GPU-only processing). The quantum co-processor presents unique capabilities with unknown potential for general-purpose high-performance computing. In this study, we explore the potential of quantum co-processing for two different algorithmic kernels within an overall data-assimilation algorithm applied to turbulent flow. A potential hybrid classical-quantum computing algorithm will be designed and experimented to test its feasibility. Specifically, quantum-co-processing kernels are designed to a) find the optimal state for data-assimilation analysis, and b) estimate turbulent subgrid stresses by approximately resolving turbulent subscales.