Span programs and quantum query complexity: The general adversary bound is nearly tight for every boolean function

TL;DR

This paper establishes a near-tight relationship between the general adversary bound and quantum query complexity using span programs, leading to optimal quantum algorithms for boolean functions and formulas.

Contribution

It introduces an SDP-based method to construct span programs with optimal witness size, linking them to the general adversary bound, and develops a quantum algorithm with logarithmic overhead for evaluating span programs.

Findings

The SDP outputs span programs with optimal witness size matching the adversary bound.

Quantum algorithms for span program evaluation have only logarithmic query overhead.

The approach yields optimal algorithms for balanced boolean formulas.

Abstract

The general adversary bound is a semi-definite program (SDP) that lower-bounds the quantum query complexity of a function. We turn this lower bound into an upper bound, by giving a quantum walk algorithm based on the dual SDP that has query complexity at most the general adversary bound, up to a logarithmic factor. In more detail, the proof has two steps, each based on "span programs," a certain linear-algebraic model of computation. First, we give an SDP that outputs for any boolean function a span program computing it that has optimal "witness size." The optimal witness size is shown to coincide with the general adversary lower bound. Second, we give a quantum algorithm for evaluating span programs with only a logarithmic query overhead on the witness size. The first result is motivated by a quantum algorithm for evaluating composed span programs. The algorithm is known to be optimal for evaluating a large class of formulas. The allowed gates include all constant-size functions for which there is an optimal span program. So far, good span programs have been found in an ad hoc manner, and the SDP automates this procedure. Surprisingly, the SDP's value equals the general adversary bound. A corollary is an optimal quantum algorithm for evaluating "balanced" formulas over any finite boolean gate set. The second result extends span programs' applicability beyond the formula evaluation problem. A strong universality result for span programs follows. A good quantum query algorithm for a problem implies a good span program, and vice versa. Although nearly tight, this equivalence is nontrivial. Span programs are a promising model for developing more quantum algorithms.