News for January 2020

The first month of 2020 is behind us, and it’s already looking good! Four papers last month, spanning quantum, graphs, and probability distributions.

On Efficient Distance Approximation for Graph Properties, by Nimrod Fiat and Dana Ron (arXiv). In the dense graph (i.e., adjacency-matrix) model, one is given a distance parameter \(\varepsilon\) and granted query access to the adjacency matrix of a graph \(G\), and seeks to determine something about the distance of \(G\) to a prespecified property \(\mathcal{P}\) of interest (i.e., the fraction of the matrix that needs to be changed for \(G\) to satisfy the property). Testing requires to distinguish whether that distance is zero, or at least \(\varepsilon\); many results over the past years have shown that many properties can be tested with a number of queries depending only on \(\varepsilon\) (and not on \(n=|G|\). This work focuses on the harder problem of distance estimation, or, equivalently, tolerant testing: that is, estimate this distance up to \(\pm\varepsilon\). The authors introduce a general framework to get distance approximation algorithms from a “complicated” property by decomposing it into simpler properties and using algorithms for those. Applying this framework to a few flagship properties, they then show that \(P_3\)-freeness, induced \(P_4\)-freeness, and cordiality are properties which have efficient distance estimation algorithms (independent of \(n\), and polynomial in \(1/\varepsilon\)).

Minimax Optimal Conditional Independence Testing, by Matey Neykov, Sivaraman Balakrishnan, and Larry Wasserman (arXiv). Given i.i.d. draws from a triple \((X,Y,Z)\), how hard is it to check whether \(X \perp Y \mid Z\), that is, “\(X\) and \(Y\) are independent conditioned on \(Z\) (or far from it)?” This is the problem on conditional independence testing, which was covered back in the days for the case where \(X,Y,Z\) are discrete. Well, this new work takes the fight out of the discrete world: extending the results and techniques from the discrete case, it provides optimal bound for the continuous case: where \(Z\) is on \([0,1]\), and then when all three \(X,Y, Z\) are continuous.

How symmetric is too symmetric for large quantum speedups?, by Shalev Ben-David and Supartha Podder (arXiv); and Can graph properties have exponential quantum speedup?, by Andrew M. Childs and Daochen Wang (arXiv). Both these works independently study the relation between the (bounded-error) randomized and quantum query complexities of any graph property \(f\), in the dense graph (adjacency-matrix) model. In particular, how much advantage do quantum algorithms provide for those?
As it turns out, not so much: for those functions, both papers show the two quantities are always polynomially related (\(R(f) \leq Q(f) \leq O(R(f)^6))\)) in the dense-graph model. As a corollary, this implies that testing any such property won’t benefit much from quantum (that is, at most polynomially)…. at least in this model. In the adjacency list model (also known as the bounded-degree graph model), the first paper conjectures that exponential query complexity improvements are possible; and the second paper provides an example, establishing it. Altogether, this fully settles an open problem of Ambainis, Childs, and Liu, and Montanaro and de Wolf.

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