Abstract:
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. I will also explain how experiments that exploit quantum memory can learn properties of a quantum system far more efficiently than conventional experiments.
Speaker Biography:
John Preskill is the Richard P. Feynman Professor of Theoretical Physics at the California Institute of Technology, and Director of the Institute for Quantum Information and Matter at Caltech. Preskill received his Ph.D. in physics in 1980 from Harvard, and joined the Caltech faculty in 1983. Preskill began his career in particle physics and cosmology, but now his main research area is quantum information science. He's interested in how to build and use quantum computers, and in how our deepening understanding of quantum information can illuminate issues in fundamental physics. You can follow him on Twitter @preskill.
This is an in-person event open to all Columbia University Affiliates.
