0000000000482020
AUTHOR
Shigeru Yamashita
Average/Worst-Case Gap of Quantum Query Complexities by On-Set Size
This paper considers the query complexity of the functions in the family F_{N,M} of N-variable Boolean functions with onset size M, i.e., the number of inputs for which the function value is 1, where 1<= M <= 2^{N}/2 is assumed without loss of generality because of the symmetry of function values, 0 and 1. Our main results are as follows: (1) There is a super-linear gap between the average-case and worst-case quantum query complexities over F_{N,M} for a certain range of M. (2) There is no super-linear gap between the average-case and worst-case randomized query complexities over F_{N,M} for every M. (3) For every M bounded by a polynomial in N, any function in F_{N,M} has quantum que…
Quantum Identification of Boolean Oracles
The oracle identification problem (OIP) is, given a set S of M Boolean oracles out of 2 N ones, to determine which oracle in S is the current black-box oracle. We can exploit the information that candidates of the current oracle is restricted to S. The OIP contains several concrete problems such as the original Grover search and the Bernstein-Vazirani problem. Our interest is in the quantum query complexity, for which we present several upper bounds. They are quite general and mostly optimal: (i) The query complexity of OIP is \(O(\sqrt{N {\rm log} M {\rm log} N}{\rm log log} M)\) for anyS such that M = |S| > N, which is better than the obvious bound N if M \(< 2^{N/log^3 N}\). (ii) It is \…
Quantum Query Complexity of Boolean Functions with Small On-Sets
The main objective of this paper is to show that the quantum query complexity Q(f) of an N-bit Boolean function f is bounded by a function of a simple and natural parameter, i.e., M = |{x|f(x) = 1}| or the size of f's on-set. We prove that: (i) For $poly(N)\le M\le 2^{N^d}$ for some constant 0 < d < 1, the upper bound of Q(f) is $O(\sqrt{N\log M / \log N})$. This bound is tight, namely there is a Boolean function f such that $Q(f) = \Omega(\sqrt{N\log M / \log N})$. (ii) For the same range of M, the (also tight) lower bound of Q(f) is $\Omega(\sqrt{N})$. (iii) The average value of Q(f) is bounded from above and below by $Q(f) = O(\log M +\sqrt{N})$ and $Q(f) = \Omega (\log M/\log N+ \sqrt{N…