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TR19-062 | 18th April 2019 21:44

Quantum Lower Bounds for Approximate Counting via Laurent Polynomials


Authors: Scott Aaronson, Robin Kothari, William Kretschmer, Justin Thaler
Publication: 21st April 2019 16:57
Downloads: 153


This paper proves new limitations on the power of quantum computers to solve approximate counting---that is, multiplicatively estimating the size of a nonempty set $S\subseteq [N]$.

Given only a membership oracle for $S$, it is well known that approximate counting takes $\Theta(\sqrt{N/|S|})$ quantum queries. But what if a quantum algorithm is also given "QSamples"---i.e., copies of the state $|S\rangle = \sum_{i\in S}|i\rangle$---or even the ability to apply reflections about $|S\rangle$? Our first main result is that, even then, the algorithm needs either $\Theta(\sqrt{N/|S|})$ queries or else $\Theta(\min\{|S|^{1/3},\sqrt{N/|S|}\})$ reflections or samples. We also give matching upper bounds.

We prove the lower bound using a novel generalization of the polynomial method of Beals et al. to Laurent polynomials, which can have negative exponents. We lower-bound Laurent polynomial degree using two methods: a new "explosion argument" and a new formulation of the dual polynomials method.

Our second main result rules out the possibility of a black-box Quantum Merlin-Arthur (or QMA) protocol for proving that a set is large. We show that, even if Arthur can make $T$ quantum queries to the set $S$, and also receives an $m$-qubit quantum witness from Merlin in support of $S$ being large, we have $Tm=\Omega(\min\{|S|,\sqrt{N/|S|}\})$. This resolves the open problem of giving an oracle separation between SBP and QMA.

Note that QMA is "stronger" than the queries+QSamples model in that Merlin's witness can be anything, rather than just the specific state $|S\rangle$, but also "weaker" in that Merlin's witness cannot be trusted. Intriguingly, Laurent polynomials also play a crucial role in our QMA lower bound, but in a completely different manner than in the queries+QSamples lower bound. This suggests that the "Laurent polynomial method" might be broadly useful in complexity theory.

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