Hybrid and multi-device quantum machine learning models

Over the past 2-3 years, commercial quantum computing hardware has begun to come online. While emerging quantum processing devices (QPUs) are still small and noisy compared to ideal quantum hardware, they are nevertheless expected to demonstrate quantum supremacy soon. During the same period, quantum machine learning (QML) has emerged as a rapidly expanding research field, perceived as one of the most promising algorithmic paradigms for near-term quantum computers. In this project, the candidate will leverage their skills in machine learning to carry out research in QML. Specifically, the candidate will use PennyLane software to explore and understand a variety of hybrid and multi-QPU machine learning models, including generative adversarial networks, autoencoders, and parallelized models. It is important for Xanadu to identify as many use-cases for QML as can be found, and to demonstrate these with our software PennyLane.

Faculty Supervisor:

Brian Silverman


Safwan Hossain




Computer science




University of Toronto



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