Quantum Communication-Query Tradeoffs

TL;DR

This paper establishes a fundamental tradeoff between quantum communication complexity and quantum query complexity for functions, providing near-optimal bounds and applications to problems like finite field square detection and ordered database search.

Contribution

It introduces a new tradeoff relation linking quantum communication and query complexities, extending to distributional settings, with applications to specific quantum problems.

Findings

Proves a near-optimal tradeoff relation for quantum communication and query complexities.

Establishes an  lower bound of   for determining quadratic residues over finite fields.

Provides a simplified proof for the quantum query lower bound in ordered database search.

Abstract

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.)