The classical shadow formalism and (some) implications for quantum machine learning

Abstract: Extracting important information from a quantum system as efficiently and tractably as possible is an important subroutine in most near-term applications of quantum hardware. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a classical shadow, can be used to predict many different properties. The required number of measurements is independent of the system size and saturates information-theoretic lower bounds. If time permits, I will also illustrate how one can combine classical shadows with machine learning (ML). This combination showcases that training data obtained from quantum experiments can be very empowering for classical ML methods.

This is joint work with Robert Huang and John Preskill (both Caltech).

quantum computing and information

Audience: researchers in the topic

Comments: To request the zoom link, please send a message cqsiadmin@uts.edu.au using your institution/organisation/business email address.

HOSTED BY: Dr Mária Kieferová, Centre for Quantum Software and Information, University of Technology Sydney, Australia

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Centre for Quantum Software and Information Seminar Series

Series comments: To request the zoom link, please send a message to: cqsiadmin@uts.edu.au using your business/organisation/institution email address. Watch previous seminars on YouTube: - QSI Seminar Series 2021 (https://youtube.com/playlist?list=PLux7B14QYkPbDDOpqKSWScHXHodiBwr48) - QSI Seminar Series 2020 (https://youtube.com/playlist?list=PLux7B14QYkPZREUXReOq01ewLl02QXBXa)

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