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Twenty-Second Annual IEEE Conference on Computational Complexity (CCC'07)
On Computation and Communication with Small Bias
San Diego, California
June 13-March 16
ISBN: 0-7695-2780-9
Harry Buhrman, CWI Amsterdamn and University of Amsterdam, Netherlands
Nikolay Vereshchagin, Moscow State University, Russia
Ronald de Wolf, CWI Amsterdam, Netherlands
We present two results for computational models that allow error probabilities close to 1/2.

First, most computational complexity classes have an analogous class in communication complexity. The class PP in fact has two, a version with weakly restricted bias called PP^cc, and a version with unrestricted bias called UPP^cc. Ever since their introduction by Babai, Frankl, and Simon in 1986, it has been open whether these classes are the same. We show that PP^cc \varsubsetneq UPP^cc. Our proof combines a query complexity separation due to Beigel with a technique of Razborov that translates the acceptance probability of quantum protocols to polynomials.

Second, we study how small the bias of minimal-degree polynomials that sign-represent Boolean functions needs to be. We show that the worst-case bias is at worst double-exponentially small in the sign-degree (which was very recently shown to be optimal by Podolski), while the averagecase bias can be made single-exponentially small in the sign-degree (which we show to be close to optimal).

Citation:
Harry Buhrman, Nikolay Vereshchagin, Ronald de Wolf, "On Computation and Communication with Small Bias," ccc, pp.24-32, Twenty-Second Annual IEEE Conference on Computational Complexity (CCC'07), 2007
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