Spectral Methods in Quantum Kernel Classifiers

Quantum Machine Learning is an emerging field of science, exploring the role that quantum computers can play when combined with machine learning models and methods. Within this field, kernel methods have quickly become one of the most promising and exciting techniques to show benefits in performance, even when using the current primitive quantum computers that are available today. Despite these exciting experiments, a deep understanding of the role played by the quantum system in kernel methods, and the origin of the benefits is still lacking. This project seeks to further develop the fundamental theory and conduct simulations in order to deepen understanding and further improve this emerging technology.

Faculty Supervisor:

Achim Kempf

Student:

Partner:

Xanadu

Discipline:

Computer science

Sector:

Information and cultural industries; Manufacturing; Professional, scientific and technical services

University:

University of Waterloo

Program: