Sample-Efficient Learning of Quantum Many-Body Systems
Offered By: IEEE via YouTube
Course Description
Overview
Learn about sample-efficient learning of quantum many-body systems in this 22-minute IEEE conference talk. Explore classical probability distributions, quantum state operators, and the motivation behind this research. Delve into the proof, dual optimization, strong convexity, and strong complexity bound. Gain insights from the summary and consider open questions in this field. Based on research by Anurag Anshu, Srinivasan Arunachalam, Tomotaka Kuwahara, and Mehdi Soleimanifar from prestigious institutions including the Institute for Quantum Computing, IBM Research, RIKEN Center, and MIT.
Syllabus
Introduction
Classical probability distributions
Quantum state operators
Motivation
Proof
Dual optimization
Strong convexity
Strong complexity bound
Summary
Open questions
Taught by
IEEE FOCS: Foundations of Computer Science
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