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Euclidean Distance Matrices and Separations in Communication Complexity Theory

Discrete and Computational Geometry. 2019. Vol. 61. No. 3. P. 653–660.
Shitov Y.

A Euclidean distance matrix D(α) is defined by D_ij=(α_i−α_j)^2, where α=(α_1,…,α_n) is a real vector. We prove that D(α) cannot be written as a sum of [2sqrt(n)−2] nonnegative rank-one matrices, provided that the coordinates of α are algebraically independent. As a corollary, we provide an asymptotically optimal separation between the complexities of quantum and classical communication protocols computing a given matrix in expectation.

Priority areas: IT and mathematics mathematics
Language: English
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Keywords: communication complexitynonnegative matrix factorizationpositive semidefinite rank
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