Analysis of a Quantum Kernel-Based Classifier Using a Tunable Trapped Ion Noisy Simulator

In this work, we develop a tunable trapped-ion noisy simulator to analyze the noise-sensitivity of a relevant quantum machine learning (QML) algorithm with respect to various noise metrics specific to existing and near-term trapped-ion hardware. Investigating the effects of trapped-ion noise on the classification performance of a quantum-enhanced kernel-based classifier is insightful for the future use of these devices for larger-scale machine learning tasks. We explore the noise-sensitivity trade-offs associated with model training in simulated environments with varying amounts of noise. As trapped-ion quantum computers may offer several advantages over superconducting devices in the realm of QML, such as all-to-all connectivity, stable higher energy atomic levels for constructing qudits, and accessible many-qubit entangling gates, it is important that we analyze and explore strategies to mitigate the effects that noise can have on QML algorithms running on these near-term trapped-ion quantum processors.