# News for March 2021

A somewhat quieter month by recent standards. Three Two papers: graph property testing and quantum distribution testing. (Ed: The distribution testing paper was a revision of a paper we already covered in Sept 2020.)

Robust Self-Ordering versus Local Self-Ordering by Oded Goldreich (ECCC). In Nov 2020, we covered a paper that uses a tool called self-ordered graphs, that transferred bit string function lower bounds to graph property testing. Consider a labeled graph. A graph is self-ordered if its automorphism group only contains the identity element (it has no non-trivial isomorphisms). A graph is robustly self-ordered, if every permutation of the vertices leads to a (labeled) graph that is sufficiently “far” according to edit distance. Given a self-ordered graph $$G$$, a local self-ordering procedure is the following. Given access to a copy $$G’$$ of $$G$$ and a vertex $$v \in V(G’)$$, this procedure determines the (unique) vertex in $$V(G)$$ that corresponds to $$v$$ with sublinear queries to $$G$$. In other words, it can locally “label” the graph. Intuitively, one would think that more robustly self-ordered graphs will be easier to locally label. This paper studies the relation between robust and local self-ordering. Curiously, this paper refutes the above intuition for bounded-degree graphs, and (weakly) confirms it for dense graphs. Roughly speaking, there are bounded degree graphs that are highly robustly self-ordered, for which any local self-ordering procedure requires $$\omega(\sqrt{n})$$ queries. Moreover, there are bounded degree graphs with $$O(\log n)$$-query local self-ordering procedures, yet are not robustly self-ordered even for weak parameters. For dense graphs, the existence of fast non-adaptive local self-ordering procedures implies robust self-ordering.

Testing identity of collections of quantum states: sample complexity analysis by Marco Fanizza, Raffaele Salvia, and Vittorio Giovannetti (arXiv). This paper takes identity testing to the quantum setting. One should think of a $$d$$-dimensional quantum state as a $$d \times d$$ density matrix (with some special properties). To learn the state entirely up to error $$\varepsilon$$ would require $$O(\varepsilon^{-2} d^2)$$ samples/measurements. A recent result of Badescu-O’Donnell-Wright proves that identity testing to a known state can be done significantly faster using $$O(\varepsilon^{-2} d)$$ measurements. This paper takes this result a step further by consider a set of $$N$$ quantum states. A “sample” is like a classical sample, where one gets a sample from a distribution of quantum states. The YES (“uniform”) case is when all the states are identical. The NO (“far from uniform”) case is when they are “far” from being the same state. This paper proves that $$O(\varepsilon^{-2}\sqrt{N}d)$$ samples suffices for distinguishing these cases.