Stressing Out Modern Quantum Hardware: Performance Evaluation and Insights

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 Quantinuum’s H1 ion-trap

device using a stress test-based protocol. Specifically, we utilize the quantum machine learning (QML) algorithm,

the Quantum Neuron Born Machine, as the computationally intensive load for the device. Then, we linearly

scale the number of repeat-until-success sub-routines 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 (a) assess

the hardware’s capacity to manage a computationally intensive QML algorithm and (b) evaluate the hardware

performance as the functional complexity of the algorithm is scaled. Alongside the quantitative performance

results, we provide a qualitative discussion based on the insights obtained from conducting the stress test with

the QNBM - i.e. we put forth the specific roadblocks that appear throughout the quantum computing stack when

scaling the functional complexity of the algorithm.