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.