Query complexity in expectation
Abstract
We study the query complexity of computing a function f:{0,1}^n>R_+ in expectation. This requires the algorithm on input x to output a nonnegative random variable whose expectation equals f(x), using as few queries to the input x as possible. We exactly characterize both the randomized and the quantum query complexity by two polynomial degrees, the nonnegative literal degree and the sumofsquares degree, respectively. We observe that the quantum complexity can be unboundedly smaller than the classical complexity for some functions, but can be at most polynomially smaller for functions with range {0,1}. These query complexities relate to (and are motivated by) the extension complexity of polytopes. The linear extension complexity of a polytope is characterized by the randomized communication complexity of computing its slack matrix in expectation, and the semidefinite (psd) extension complexity is characterized by the analogous quantum model. Since query complexity can be used to upper bound communication complexity of related functions, we can derive some upper bounds on psd extension complexity by constructing efficient quantum query algorithms. As an example we give an exponentiallyclose entrywise approximation of the slack matrix of the perfect matching polytope with psdrank only 2^{n^{1/2+epsilon}}. Finally, we show there is a precise sense in which randomized/quantum query complexity in expectation corresponds to the SheraliAdams and Lasserre hierarchies, respectively.
 Publication:

arXiv eprints
 Pub Date:
 November 2014
 arXiv:
 arXiv:1411.7280
 Bibcode:
 2014arXiv1411.7280K
 Keywords:

 Quantum Physics;
 Computer Science  Computational Complexity
 EPrint:
 16 pages LaTeX