For any function $f: X \times Y \to Z$, we prove that $Q^{ *\text{ cc}}(f) \cdot Q^{ \text{ OIP}}(f) \cdot (\log Q^{ \text{ OIP}}(f) + \log |Z|) \geq \Omega(\log |X|)$. Here, $Q^{ *\text{ cc}}(f)$ denotes the bounded-error communication complexity of $f$ using an entanglement-assisted two-way qubit channel, and $Q^{ \text{ OIP}}(f)$ denotes the number of quantum queries needed to learn $x$ with high probability given oracle access to the function $f_x(y) \stackrel{ \text{ def}}{ =} f(x, y)$. We show that this tradeoff is close to the best possible. We also give a generalization of this tradeoff for distributional query complexity. As an application, we prove an optimal $\Omega(\log q)$ lower bound on the $Q^{ *\text{ cc}}$ complexity of determining whether $x + y$ is a perfect square, where Alice holds $x \in \mathbf{ F}_q$, Bob holds $y \in \mathbf{ F}_q$, and $\mathbf{ F}_q$ is a finite field of odd characteristic. As another application, we give a new, simpler proof that searching an ordered size-$N$ database requires $\Omega(\log N / \log \log N)$ quantum queries. (It was already known that $\Theta(\log N)$ queries are required.)
Categories: cs.CC quant-ph