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 sum-of-squares 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 exponentially-close entrywise approximation of the slack matrix of the perfect matching polytope with psd-rank 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 Sherali-Adams and Lasserre hierarchies, respectively.
Additional Metadata
THEME Null option (theme 11)
Publisher Springer
Persistent URL dx.doi.org/10.1007/978-3-662-47672-7_62
Series Lecture Notes in Computer Science
Project Quantum Algorithmics , Progress in quantum computing:Algorithms, communication, and applications
Conference International Colloquium on Automata, Languages and Programming
Grant This work was funded by the European Commission 7th Framework Programme; grant id erc/615307 - Progress in quantum computing: Algorithms, communication, and applications (QPROGRESS)
Citation
Kaniewski, J, Lee, T. J, & de Wolf, R. M. (2015). Query complexity in expectation. In Proceedings of International Colloquium on Automata, Languages and Programming 2015 (ICALP 42) (pp. 761–772). Springer. doi:10.1007/978-3-662-47672-7_62