Separations in Query Complexity Based on Pointer Functions

TL;DR

This paper constructs new total boolean functions demonstrating super-quadratic and super-linear separations between various query complexity measures, challenging previous conjectures and establishing optimal bounds up to poly-logarithmic factors.

Contribution

It provides the first examples of total functions with super-quadratic quantum vs. deterministic gaps and super-linear randomized vs. zero-error randomized gaps, refining the understanding of query complexity relationships.

Findings

First total function with super-quadratic quantum vs. deterministic gap.

First super-linear separation between zero-error and bounded-error randomized complexities.

Multiple new separations among quantum, randomized, and approximate degree complexities.

Abstract

In 1986, Saks and Wigderson conjectured that the largest separation between deterministic and zero-error randomized query complexity for a total boolean function is given by the function $f$ on $n=2^k$ bits defined by a complete binary tree of NAND gates of depth $k$, which achieves $R_0(f) = O(D(f)^{0.7537\ldots})$. We show this is false by giving an example of a total boolean function $f$ on $n$ bits whose deterministic query complexity is $\Omega(n/\log(n))$ while its zero-error randomized query complexity is $\tilde O(\sqrt{n})$. We further show that the quantum query complexity of the same function is $\tilde O(n^{1/4})$, giving the first example of a total function with a super-quadratic gap between its quantum and deterministic query complexities. We also construct a total boolean function $g$ on $n$ variables that has zero-error randomized query complexity $\Omega(n/\log(n))$ and bounded-error randomized query complexity $R(g) = \tilde O(\sqrt{n})$. This is the first super-linear separation between these two complexity measures. The exact quantum query complexity of the same function is $Q_E(g) = \tilde O(\sqrt{n})$. These two functions show that the relations $D(f) = O(R_1(f)^2)$ and $R_0(f) = \tilde O(R(f)^2)$ are optimal, up to poly-logarithmic factors. Further variations of these functions give additional separations between other query complexity measures: a cubic separation between $Q$ and $R_0$, a $3/2$-power separation between $Q_E$ and $R$, and a 4th power separation between approximate degree and bounded-error randomized query complexity. All of these examples are variants of a function recently introduced by \goos, Pitassi, and Watson which they used to separate the unambiguous 1-certificate complexity from deterministic query complexity and to resolve the famous Clique versus Independent Set problem in communication complexity.