A quantum algorithm for learning a graph of bounded degree

TL;DR

A randomized algorithm is presented that, with high probability, learns cycles and matchings in $\tilde{O}(\sqrt{m})$ quantum queries, matching the theoretical lower bound up to logarithmic factors.

Abstract

We are presented with a graph, $G$, on $n$ vertices with $m$ edges whose edge set is unknown. Our goal is to learn the edges of $G$ with as few queries to an oracle as possible. When we submit a set $S$ of vertices to the oracle, it tells us whether or not $S$ induces at least one edge in $G$. This so-called OR-query model has been well studied, with Angluin and Chen giving an upper bound on the number of queries needed of $O(m \log n)$ for a general graph $G$ with $m$ edges. When we allow ourselves to make *quantum* queries (we may query subsets in superposition), then we can achieve speedups over the best possible classical algorithms. In the case where $G$ has maximum degree $d$ and is $O(1)$-colorable, Montanaro and Shao presented an algorithm that learns the edges of $G$ in at most $\tilde{O}(d^2m^{3/4})$ quantum queries. This gives an upper bound of $\tilde{O}(m^{3/4})$ quantum queries when $G$ is a matching or a Hamiltonian cycle, which is far away from the lower bound of $Ω(\sqrt{m})$ queries given by Ambainis and Montanaro. We improve on the work of Montanaro and Shao in the case where $G$ has bounded degree. In particular, we present a randomized algorithm that, with high probability, learns cycles and matchings in $\tilde{O}(\sqrt{m})$ quantum queries, matching the theoretical lower bound up to logarithmic factors.

Theorems & Definitions (17)

• Theorem 1.1: montanaroShao Theorem 7
• Theorem 1.2
• Theorem 1.3
• Theorem 2.1: Chernoff bound
• Remark 1: see e.g. JansonConcentration
• Lemma 2.2
• Theorem 2.3: Vizing's Theorem Vizing
• Theorem 2.4: AngluinChen Theorem 3.1
• Theorem 2.5: belovs Theorem 3
• Remark 2