Computer Science – Information Theory
Scientific paper
2008-02-27
Computer Science
Information Theory
ver 1, 19 pages
Scientific paper
In this paper we provide new bounds on classical and quantum distributional communication complexity in the two-party, one-way model of communication. In the classical model, our bound extends the well known upper bound of Kremer, Nisan and Ron to include non-product distributions. We show that for a boolean function f:X x Y -> {0,1} and a non-product distribution mu on X x Y and epsilon in (0,1/2) constant: D_{epsilon}^{1, mu}(f)= O((I(X:Y)+1) vc(f)), where D_{epsilon}^{1, mu}(f) represents the one-way distributional communication complexity of f with error at most epsilon under mu; vc(f) represents the Vapnik-Chervonenkis dimension of f and I(X:Y) represents the mutual information, under mu, between the random inputs of the two parties. For a non-boolean function f:X x Y ->[k], we show a similar upper bound on D_{epsilon}^{1, mu}(f) in terms of k, I(X:Y) and the pseudo-dimension of f' = f/k. In the quantum one-way model we provide a lower bound on the distributional communication complexity, under product distributions, of a function f, in terms the well studied complexity measure of f referred to as the rectangle bound or the corruption bound of f . We show for a non-boolean total function f : X x Y -> Z and a product distribution mu on XxY, Q_{epsilon^3/8}^{1, mu}(f) = Omega(rec_ epsilon^{1, mu}(f)), where Q_{epsilon^3/8}^{1, mu}(f) represents the quantum one-way distributional communication complexity of f with error at most epsilon^3/8 under mu and rec_ epsilon^{1, mu}(f) represents the one-way rectangle bound of f with error at most epsilon under mu . Similarly for a non-boolean partial function f:XxY -> Z U {*} and a product distribution mu on X x Y, we show, Q_{epsilon^6/(2 x 15^4)}^{1, mu}(f) = Omega(rec_ epsilon^{1, mu}(f)).
Jain Rahul
Zhang Shengyu
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