Quantum-assisted Machine Learning in Near-Term Quantum Devices

Alejandro Perdomo-Ortiz - Department of Computer Science, University College London
Date and time
Monday, April 8, 2019 at 2:30 PM - Aula Verde
Programme Director
External reference
Publication date
March 4, 2019
Computer Science  


With quantum computing technologies nearing the era of commercialization and quantum advantage, machine learning (ML) has been proposed as one of the promising killer applications. Despite significant effort, there has been a disconnect between most quantum ML proposals, the needs of ML practitioners, and the capabilities of near-term quantum devices towards a conclusive demonstration of a meaningful quantum advantage in the near future. In this talk, we provide concrete examples of intractable ML tasks that could be enhanced with near-term devices. We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning tasks. We focus on hybrid quantum-classical approaches and illustrate some of the key challenges we foresee for near-term implementations. We will present as well recent experimental implementations of these quantum ML models in both, superconducting-qubit and ion-trap quantum computers.

Short Bio: Alejandro did his graduate studies, M.A and Ph.D. in Chemical Physics, at Harvard University. Over the past 10+ years, he has worked on the implementation of quantum computing algorithms, enhancing their performance with physics-based approaches while maintaining a practical, application-relevant perspective. Before joining Rigetti as a Senior Research Scientist, Alejandro was the lead scientist of the Quantum Machine Learning effort at NASA's Quantum Artificial Intelligence Laboratory (NASA QuAIL), where he worked during 5+ years. He also holds an Honorary Senior Research Associate position at University College London. His latest research involves the design of hybrid quantum-classical algorithms to solve hard optimization problems and intractable machine learning subroutines.

Contact Person: Prof. Alessandra Di Pierro

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