Quantum-assisted machine learning
in near-term quantum devices
Before joining Zapata Computing as a Senior Quantum Scientist, Alejandro was the lead scientist of the quantum machine learning efforts at NASA’s Quantum Artificial Intelligence Laboratory (NASA QuAIL). He was also the Co-Founder of Qubitera LLC, a consulting company acquired by Rigetti Computing where he worked after NASA and before his current appointment with Zapata Computing. He also holds an Honorary Senior Research Associate position at University College London. His research focuses in exploring the computational limits and opportunities of quantum computers for problems in artificial intelligence.
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 course, 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 gate-based (superconducting-qubit and ion-trap) quantum computers and in quantum annealers.