Nonadaptive quantum query complexity

We study the power of nonadaptive quantum query algorithms, which are algorithms whose queries to the input do not depend on the result of previous queries. First, we show that any bounded-error nonadaptive quantum query algorithm that computes a total boolean function depending on n   variables must make Ω(n)Ω(n) queries to the input in total. Second, we show that, if there exists a quantum algorithm that uses k nonadaptive oracle queries to learn which one of a set of m   boolean functions it has been given, there exists a nonadaptive classical algorithm using O(klogm) queries to solve the same problem. Thus, in the nonadaptive setting, quantum algorithms for these tasks can achieve at most a very limited speed-up over classical query algorithms.

Research highlights
► Nonadaptive quantum query algorithms are almost no better than classical.
► Nonadaptive quantum learning algorithms can only achieve a limited speed-up.
► Existing nonadaptive quantum algorithms are essentially optimal.