SemQBench: Benchmarking Decomposition-Aware Runtime Policies for Quantum-HPC Search

Abstract

Quantum processors are increasingly used as accelerators inside high-performance computing (HPC) workflows. A workflow runtime decides which quantum circuits to run, how to group them into provider jobs, and what to repeat after a failed attempt. Many search problems split into independent parts, but the runtime can use that structure only when the workflow exposes the parts and a validity check for each one. SemQBench measures what a runtime gains from this visibility. It compares seven execution policies for the same search problem, including two controls without amplitude amplification. The policies change one runtime choice at a time: whether to amplify, whether partial results may be combined across runs, whether the parts share one circuit, and what a retry keeps. We run them on the 20-qubit IQM Garnet processor using 106 distinct circuits in three jobs, and report circuit executions, active device time, and provider jobs separately before combining them in a cost model. Amplifying the problem as one whole circuit shows no statistically clear gain over random sampling and uses 6.7-104$\times$ more active device time. Exposing the parts and collecting valid partial results across a fixed batch of runs cuts the executions needed for 95% completion confidence by 2.20-4.00$\times$, and this policy is never worse than the other amplified policies in executions, device time, or jobs; it is selected in 99 of 108 overhead settings. Fewer executions do not always mean less device time: on one workload, executions fall from thirteen to four while active device time rises from 7.44 to 9.81 $μ$s. Exposing problem structure therefore gives the runtime a strong default policy and the measurements needed to decide when the simpler unamplified option is cheaper.