On the Power of Non-adaptive Learning Graphs
Aleksandrs Belovs, Ansis Rosmanis · 2013
We introduce a notion of the quantum query complexity of a certificate structure. This is a formalisation of a well-known observation that many quantum query algorithms only require the knowledge of the disposition of possible certificates in the input string, not the precise values therein. Next, we derive a dual formulation of the complexity of a non-adaptive learning graph, and use it to show that non-adaptive learning graphs are tight for all certificate structures. By this, we mean that there exists a function possessing the certificate structure and such that a learning graph gives an optimal quantum query algorithm for it. For a special case of certificate structures generated by certificates of bounded size, we construct a relatively general class of functions having this property. The construction is based on orthogonal arrays, and generalizes the quantum query lower bound for the k-sum problem derived recently. Finally, we use these results to show that the best known learning graph for the triangle problem is almost optimal in these settings. This also gives a quantum query lower bound for the triangle-sum problem.