Uniformity testing when you have the source code

TL;DR

The basic task of uniformity testing, which is to decide if the output distribution is uniform on $d$ or $\epsilon$-far from uniform in total variation distance, is considered, and the upper bound is improved to $O(\min\{d^{1/3}/\epsilon^{4/3}, d^{1/2}/\epsilon\})$, which is conjecture is optimal.

Abstract

We study quantum algorithms for verifying properties of the output probability distribution of a classical or quantum circuit, given access to the source code that generates the distribution. We consider the basic task of uniformity testing, which is to decide if the output distribution is uniform on $[d]$ or $ε$-far from uniform in total variation distance. More generally, we consider identity testing, which is the task of deciding if the output distribution equals a known hypothesis distribution, or is $ε$-far from it. For both problems, the previous best known upper bound was $O(\min\{d^{1/3}/ε^{2},d^{1/2}/ε\})$. Here we improve the upper bound to $O(\min\{d^{1/3}/ε^{4/3}, d^{1/2}/ε\})$, which we conjecture is optimal.

Theorems & Definitions (20)

• Theorem 1.1
• Conjecture 1
• Corollary 1
• Theorem 1.2
• Theorem 3.1
• Theorem 4.1
• proof
• Lemma 1
• proof
• Theorem 5.1