Quantum-Assisted Machine Learning with Near-Term Quantum Devices

Explore the potential of quantum-assisted machine learning in this 35-minute conference talk from the Toronto Machine Learning Series. Delve into the challenges and opportunities presented by near-term quantum devices in enhancing intractable machine learning tasks. Learn about the disconnect between quantum ML proposals, industry needs, and current quantum technology capabilities. Discover concrete examples of how quantum computing could revolutionize unsupervised and semi-supervised learning, particularly in generative models. Gain insights into recent experimental implementations of quantum generative models using superconducting-qubit and ion-trap quantum computers. Led by Alejandro Perdomo Ortiz, Lead Quantum Applications at Zapata Computing Inc., this talk bridges the gap between quantum computing advancements and practical machine learning applications, offering a glimpse into the future of quantum-enhanced AI.