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Quantum hardware is progressing at a rapid pace and, alongside thisprogression, it is vital to challenge the capabilities of these machines usingfunctionally complex algorithms.Doing so provides direct insights into thecurrent capabilities of modern quantum hardware and where its breaking pointslie.Stress testing is a technique used to evaluate a system by giving it acomputational load beyond its specified thresholds and identifying the capacityunder which it fails.We conduct a qualitative and quantitative evaluation ofthe Quantinuum H1 ion trap device using a stress test basedprotocol.Specifically, we utilize the quantum machine learning algorithm, theQuantum Neuron Born Machine, as the computationally intensive load for thedevice.Then, we linearly scale the number of repeat until success subroutineswithin the algorithm to determine the load under which the hardware fails andwhere the failure occurred within the quantum stack.Using this proposed method,we assess the hardware capacity to manage a computationally intensive QMLalgorithm and evaluate the hardware performance as the functional complexity ofthe algorithm is scaled.Alongside the quantitative performance results, weprovide a qualitative discussion and resource estimation based on the insightsobtained from conducting the stress test with the QNBM.

Stressing Out Modern Quantum Hardware: Performance Evaluation and Execution Insights