Hello all,
Tomorrow, Alejandro Perdomo-Ortiz will speak at the group meeting. See below for title,
abstract, and short biography. The Toronto side will meet in SS571 at 2:30pm.
Those in Boston or elsewhere, please send me your Skype information prior to the meeting.
See you all there,
Riley
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Title: Quantum-assisted Machine Learning in Near-Term Quantum Devices.
Abstract: 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.
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). 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.
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