MenuEvents for 03/10/2017 from all calendars
Geometry Seminar
Time: 4:00PM - 5:00PM
Location: BLOC 220 - NOTE
Speaker: Scott Aaronson, UT Austin
Title: Boson Sampling and the Permanents of Gaussian Matrices
Abstract: I'll discuss BosonSampling, a proposal by myself and Alex Arkhipov to demonstrate "quantum supremacy" (that is, an exponential computational speedup over classical computers), using a linear-optical setup that falls far short of being a universal quantum computer. The goal, in BosonSampling, is to sample from a certain kind of probability distribution, in which the probabilities are given by the absolute squares of permanents of complex matrices (n-by-n matrices, if there are n photons involved). Of particular interest to mathematicians is that the BosonSampling program leads naturally to rich mathematical questions---some of which we've answered, but many of which remain open---about the permanent itself. (For example: are permanents of i.i.d. Gaussian matrices close to lognormally distributed? Is there an efficient algorithm to estimate them?) I'll focus mainly on those questions. No quantum computation background is needed for this talk.
Linear Analysis Seminar
Time: 4:00PM - 5:00PM
Location: BLOC 220
Speaker: Scott Aaronson, UT Austin
Title: Boson sampling and the permanents of Gaussian matrices
Abstract: I'll discuss BosonSampling, a proposal by myself and Alex Arkhipov to demonstrate "quantum supremacy" (that is, an exponential computational speedup over classical computers), using a linear-optical setup that falls far short of being a universal quantum computer. The goal, in BosonSampling, is to sample from a certain kind of probability distribution, in which the probabilities are given by the absolute squares of permanents of complex matrices (n-by-n matrices, if there are n photons involved). Of particular interest to mathematicians is that the BosonSampling program leads naturally to rich mathematical questions---some of which we've answered, but many of which remain open---about the permanent itself. (For example: are permanents of i.i.d. Gaussian matrices close to lognormally distributed? Is there an efficient algorithm to estimate them?) I'll focus mainly on those questions. No quantum computation background is needed for this talk.
