Attend LIONS seminar with guest Duong Do, March 20

Join the Learning, Information, Optimization, Networks and Statistics, or LIONS, group a for a seminar with Duong Do, a fifth-year doctoral student in the School of Electrical, Computer and Energy Engineering, part of the Ira A. Fulton Schools of Engineering at Arizona State University.

Quantum support vector machines, or QSVMs, based on fidelity kernels can work well on small datasets, but training typically requires O(n^2) kernel evaluations and performance may drop under changes such as class imbalance, label noise or shifts in the data input distribution. In this study, we present a distributionally robust quantum kernel learning, or DRQKL, method for QSVMs that aims to reduce the cost of kernel learning and improve reliability when test data are not drawn from the same distribution as the training data.

Using low-rank Nyström approximation with $m << n$ landmark points, we construct a positive semidefinite approximate kernel with O(nm) kernel evaluations, which enables training at larger scale. We then train the QSVM with a distributionally robust optimization, or DRO, objective that minimizes the worst-case expected hinge loss over a neighborhood of the empirical distribution, using either a \chi^2-divergence or CVar constraints. We provide an analysis that relates the Nyström approximation error and the DRO radius to the classifier’s risk, showing how approximation and robustness affect generalization under bounded distribution shift.

Our host for this LIONS Seminar is Duong Nguyen, an assistant professor in the School of Electrical, Computer and Energy Engineering.

Visit the LIONS Seminar website for more information.

LIONS Seminar: DRQKL: Distributionally Robust Quantum Kernel Learning via Low-Rank Nyström Approximation
Friday, March 20, 2026
1:30–2:30 p.m.
Goldwater Center (GWC) 487, Tempe campus
Register to attend