Entanglement-enhanced learning of quantum processes at scale
Abstract: Learning unknown processes affecting a quantum system reveals underlying physical mechanisms and enables suppression, mitigation, and correction of unwanted effects. Generally, learning quantum processes requires exponentially many measurements. We show how entanglement with an ideal auxiliary quantum memory can provide an exponential advantage in learning certain quantum processes. We discuss practical limitations of entanglement-enhanced protocols, and show, both theoretically and experimentally, that even in the presence of noise, entanglement with auxiliary quantum memory combined with error mitigation considerably enhances the learning of quantum processes.
Bio: Alireza Seif is a Research Scientist at IBM. He received his Ph.D. in Physics from the University of Maryland, where he conducted research at the Joint Quantum Institute. After completing his Ph.D., he joined the University of Chicago as a Prize Postdoctoral Fellow in Theoretical Quantum Sciences. His research interests include quantum optics, quantum information theory, and the application of machine learning in physics.
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