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Quantum Chebyshev’s Inequality and Applications

Authors Yassine Hamoudi , Frédéric Magniez

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Author Details

Yassine Hamoudi
  • Université de Paris, IRIF, CNRS, F-75013 Paris, France
Frédéric Magniez
  • Université de Paris, IRIF, CNRS, F-75013 Paris, France

Funding

This research was supported by the French ANR project ANR-18-CE47-0010 (QUDATA) and the QuantERA ERA-NET Cofund project QuantAlgo.

Acknowledgements

The authors want to thank the anonymous referees for their valuable comments and suggestions which helped to improve this paper.

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Yassine Hamoudi and Frédéric Magniez. Quantum Chebyshev’s Inequality and Applications. In 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 132, pp. 69:1-69:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.ICALP.2019.69

Abstract

In this paper we provide new quantum algorithms with polynomial speed-up for a range of problems for which no such results were known, or we improve previous algorithms. First, we consider the approximation of the frequency moments F_k of order k >= 3 in the multi-pass streaming model with updates (turnstile model). We design a P-pass quantum streaming algorithm with memory M satisfying a tradeoff of P^2 M = O~(n^{1-2/k}), whereas the best classical algorithm requires P M = Theta(n^{1-2/k}). Then, we study the problem of estimating the number m of edges and the number t of triangles given query access to an n-vertex graph. We describe optimal quantum algorithms that perform O~(sqrt{n}/m^{1/4}) and O~(sqrt{n}/t^{1/6} + m^{3/4}/sqrt{t}) queries respectively. This is a quadratic speed-up compared to the classical complexity of these problems.
For this purpose we develop a new quantum paradigm that we call Quantum Chebyshev’s inequality. Namely we demonstrate that, in a certain model of quantum sampling, one can approximate with relative error the mean of any random variable with a number of quantum samples that is linear in the ratio of the square root of the variance to the mean. Classically the dependence is quadratic. Our algorithm subsumes a previous result of Montanaro [Montanaro, 2015]. This new paradigm is based on a refinement of the Amplitude Estimation algorithm of Brassard et al. [Brassard et al., 2002] and of previous quantum algorithms for the mean estimation problem. We show that this speed-up is optimal, and we identify another common model of quantum sampling where it cannot be obtained. Finally, we develop a new technique called "variable-time amplitude estimation" that reduces the dependence of our algorithm on the sample preparation time.

Subject Classification

ACM Subject Classification
  • Theory of computation → Quantum computation theory
Keywords
  • Quantum algorithms
  • approximation algorithms
  • sublinear-time algorithms
  • Monte Carlo method
  • streaming algorithms
  • subgraph counting

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