Optimization and Analysis of Resource Allocation in Quantum-Centric Supercomputing Environments

Abstract

Advances in quantum computing hardware and quantum algorithms are likely to cause major paradigm shifts in highperformance supercomputing environments. These shifts include foundational changes to system infrastructures that integrate both quantum and classical computational substrates through a combination of quantum processing units (QPUs), graphical processing units (GPUs), and central processing units (CPUs). These shifts also include foundational changes in application workloads that combine di!erent patterns of computation across the various processing units, resulting in jobs that require only classical GPUs and CPUs, only quantum QPUs, or a mixture of GPUs, CPUs, and QPUs. Meanwhile, although there have been numerous research studies of application workloads based on GPUs and CPUs, our theoretical understanding of the mathematical optimization and analysis of resource allocation in high-performance supercomputing environments is far more limited and largely unexplored under QPU-only and QPU-GPU-combined workloads, especially with respect to (w.r.t.) the latter.