Quantum-Enhanced Machine Learning with Near-Term Devices

Relatore:  Marta Mauri (Zapata Computing)

Abstract

With quantum computing technologies approaching the era of
commercialization and quantum advantage, machine learning (ML) is typically touted as one of the most promising fields for their application. In this talk, we focus on the challenging research area of generative models and we provide concrete examples of ML tasks that could be enhanced with near-term quantum devices. We show how to tackle such problems with novel hybrid quantum-classical ML methods and we highlight some of the key challenges in these approaches and how to overcome them using Orquestra: Zapata’s quantum workflow management platform. We will demonstrate the power of Orquestra to run and manage such complex quantum machine learning tasks due to its modularity and extensibility.