MCAIM Graduate Seminar Seminar

With the success of machine learning algorithms and the early promise of quantum computing, it is natural to ask whether efficient quantum learning is possible. Are there learning contexts where we observe a quantum (exponential) speed-up? Are there instances where it is not possible for quantum computers to (significantly) outperform classical ones? In this talk, we begin a discussion on these questions by reviewing the results for several learning problems. In particular, we begin with quantum exact, PAC, and agnostic learning of classical Boolean functions. Thereafter, we will consider statistical and adversarial learning of quantum states. We end with a brief overview of broader quantum machine learning results, including the quest for a quantum neural network. Speaker(s): Preetham Mohan (University of Michigan)