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How to learn a quantum state

March 13, 2019 - 11:00am
Speaker: 
John Wright
Institution: 
MIT
In the area of quantum state learning, one is given a small number of "samples" of a quantum state, and the goal is use them to determine a feature of the state.  Examples include learning the entire state ("quantum state tomography"), determining whether it equals a target state ("quantum state certification"), or estimating its von Neumann entropy.  These are problems which are not only of theoretical interest, but are also commonly used in current-day implementation and verification of quantum technologies. In this talk, I will describe my work giving efficient algorithms for a variety of these problems, including the first optimal algorithms for tomography and state certification.  My results make use of a new connection between quantum state learning and longest increasing subsequences of random words, a famous topic in combinatorics dating back to a 1935 paper of Erdős and Szekeres.  Motivated by this connection, I will show new and optimal bounds on the length of the longest increasing subsequence of a random word.
 
I will also discuss some recent work in quantum complexity theory.
ATL 3100A