Machine Learning at the Quantum/Classical Computational Frontier - Antonio Mezzacapo

Speaker: Antonio Mezzacapo
Host: Zlatko Minev, Ph.D.

Title: Machine Learning at the Quantum/Classical Computational Frontier

Abstract: I will describe some problems at the intersection of quantum and classical computations, showing two ways in which efficient retrieval and manipulation of quantum data can improve quantum algorithms. These methods rely on efficient representations of partial information of a quantum state, and are aimed at increasing the precision of observable estimations. The first one is based on the concept of locally-biased classical shadows of a quantum state, while the second one relies on neural-networks quantum states. Last, I will finish by showing that quantum computing fermion-to-qubit encodings can be used in the context of computational machine learning methods.

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The Quantum Computing Seminar Series is a deep dive into various academic and research topics within the quantum community. It will feature community members and leaders every Friday, 12 PM EDT.

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