Description
I will review an experimentally feasible procedure for converting a quantum state into a succinct classical description of the state, its classical shadow. Classical shadows can be applied to predict efficiently many properties of interest, including expectation values of local observables and few-body correlation functions. Efficient classical machine learning algorithms using classical shadows can address quantum many-body problems such as classifying quantum phases of matter.
Panel discussion: Scott Aaronson (UT Austin), Sergio Boixo (Google), Joseph Emerson (Quantum Benchmark), Steven Flammia (AWS Center for QC), Umesh Vazirani (UC Berkeley; moderator)
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