Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88427
Title: Separating quantum communication and approximate rank
Authors: Anshu, Anurag
Garg, Ankit
Kothari, Robin
Ben-David, Shalev
Jain, Rahul
Lee, Troy
Keywords: DRNTU::Science::Mathematics
Communication Complexity
Quantum Computing
Issue Date: 2017
Source: Anshu, A., Ben-David, S., Garg, A., Jain, R., Kothari, R., & Lee, T. (2017). Separating quantum communication and approximate rank. Leibniz International Proceedings in Informatics (LIPIcs), 79, 24-. doi: 10.4230/LIPIcs.CCC.2017.24
Series/Report no.: Leibniz International Proceedings in Informatics
Abstract: One of the best lower bound methods for the quantum communication complexity of a function H (with or without shared entanglement) is the logarithm of the approximate rank of the communication matrix of H. This measure is essentially equivalent to the approximate gamma-2 norm and generalized discrepancy, and subsumes several other lower bounds. All known lower bounds on quantum communication complexity in the general unbounded-round model can be shown via the logarithm of approximate rank, and it was an open problem to give any separation at all between quantum communication complexity and the logarithm of the approximate rank. In this work we provide the first such separation: We exhibit a total function H with quantum communication complexity almost quadratically larger than the logarithm of its approximate rank. We construct H using the communication lookup function framework of Anshu et al. (FOCS 2016) based on the cheat sheet framework of Aaronson et al. (STOC 2016). From a starting function F, this framework defines a new function H=F_G. Our main technical result is a lower bound on the quantum communication complexity of F_G in terms of the discrepancy of F, which we do via quantum information theoretic arguments. We show the upper bound on the approximate rank of F_G by relating it to the Boolean circuit size of the starting function F.
URI: https://hdl.handle.net/10356/88427
http://hdl.handle.net/10220/45784
ISSN: 1868-8969
DOI: https://dx.doi.org/10.4230/LIPIcs.CCC.2017.24
Rights: © Anurag Anshu, Shalev Ben-David, Ankit Garg, Rahul Jain, Robin Kothari, and Troy Lee; licensed under Creative Commons License CC-BY 32nd Computational Complexity Conference (CCC 2017). Editor: Ryan O’Donnell; Article No. 24; pp. 24:1–24:33
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
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