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          <dc:title>Polynomials, Quantum Query Complexity, and Grothendieck's Inequality</dc:title>
          <dc:creator>Aaronson, Scott</dc:creator>
          <dc:creator>Ambainis, Andris</dc:creator>
          <dc:creator>Iraids, Janis</dc:creator>
          <dc:creator>Kokainis, Martins</dc:creator>
          <dc:creator>Smotrovs, Juris</dc:creator>
          <dc:subject>quantum algorithms</dc:subject>
          <dc:subject>Boolean functions</dc:subject>
          <dc:subject>approximation by polynomials</dc:subject>
          <dc:subject>Grothendieck's inequality</dc:subject>
          <dc:description>We show an equivalence between 1-query quantum algorithms and representations by degree-2 polynomials. Namely, a partial Boolean function f is computable by a 1-query quantum algorithm with error bounded by epsilon&lt;1/2 iff f can be approximated by a degree-2 polynomial with error bounded by epsilon'&lt;1/2. This result holds for two different notions of approximation by a polynomial: the standard definition of Nisan and Szegedy and the approximation by block-multilinear polynomials recently introduced by Aaronson and Ambainis [Aaronson/Ambainis, STOC 2015]. The proof uses Grothendieck's inequality to relate two matrix norms, with one norm corresponding to polynomial approximations and the other norm corresponding to quantum algorithms.&#13;
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We also show two results for polynomials of higher degree. First, there is a total Boolean function which requires ~Omega(n) quantum queries but can be represented by a block-multilinear polynomial of degree ~O(sqrt(n)). Thus, in the general case (for an arbitrary number of queries), block-multilinear polynomials are not equivalent to quantum algorithms.&#13;
&#13;
Second, for any constant degree k, the two notions of approximation by a polynomial (the standard and the block-multilinear) are equivalent. As a consequence, we solve an open problem from [Aaronson/Ambainis, STOC 2015], showing that one can estimate the value of any bounded degree-k polynomial p:{0,1}^n -&gt; [-1,1] with O(n^{1-1/(2k)) queries.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Scott Aaronson and Andris Ambainis and Janis Iraids and Martins Kokainis and Juris Smotrovs</dc:contributor>
          <dc:date>2016</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 50, 31st Conference on Computational Complexity (CCC 2016)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.CCC.2016.25</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-58394</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2016.25</dc:identifier>
          <dc:language>eng</dc:language>
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