Performance Models for Split-execution Computing Systems

TL;DR

This paper analyzes the performance of split-execution systems combining classical CPUs and quantum processing units, highlighting the quantum-classical interface as the main bottleneck and cost factor.

Contribution

It introduces behavioral performance models for hybrid classical-quantum systems, emphasizing the translation costs and interface bottlenecks in split-execution computing.

Findings

Quantum-classical interface is the primary performance bottleneck.

Translation costs dominate the overall execution time.

Quantum processor behavior has minimal impact on total performance.

Abstract

Split-execution computing leverages the capabilities of multiple computational models to solve problems, but splitting program execution across different computational models incurs costs associated with the translation between domains. We analyze the performance of a split-execution computing system developed from conventional and quantum processing units (QPUs) by using behavioral models that track resource usage. We focus on asymmetric processing models built using conventional CPUs and a family of special-purpose QPUs that employ quantum computing principles. Our performance models account for the translation of a classical optimization problem into the physical representation required by the quantum processor while also accounting for hardware limitations and conventional processor speed and memory. We conclude that the bottleneck in this split-execution computing system lies at the quantum-classical interface and that the primary time cost is independent of quantum processor behavior.