Stressing Out Modern Quantum Hardware: Performance Evaluation and Execution Insights

Abstract

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