All reports by Author Aleksandrs Belovs:

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TR16-072
| 4th May 2016
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Anurag Anshu, Aleksandrs Belovs, Shalev Ben-David, Mika G\"o{\"o}s, Rahul Jain, Robin Kothari, Troy Lee, Miklos Santha#### Separations in communication complexity using cheat sheets and information complexity

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TR15-098
| 15th June 2015
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Andris Ambainis, Kaspars Balodis, Aleksandrs Belovs, Troy Lee, Miklos Santha, Juris Smotrovs#### Separations in Query Complexity Based on Pointer Functions

Revisions: 2

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TR14-109
| 14th August 2014
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Aran Nayebi, Scott Aaronson, Aleksandrs Belovs, Luca Trevisan#### Quantum lower bound for inverting a permutation with advice

Revisions: 1

Anurag Anshu, Aleksandrs Belovs, Shalev Ben-David, Mika G\"o{\"o}s, Rahul Jain, Robin Kothari, Troy Lee, Miklos Santha

While exponential separations are known between quantum and randomized communication complexity for partial functions, e.g. Raz [1999], the best known separation between these measures for a total function is quadratic, witnessed by the disjointness function. We give the first super-quadratic separation between quantum and randomized

communication complexity for a ...
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Andris Ambainis, Kaspars Balodis, Aleksandrs Belovs, Troy Lee, Miklos Santha, Juris Smotrovs

In 1986, Saks and Wigderson conjectured that the largest separation between deterministic and zero-error randomized

query complexity for a total boolean function is given by the function $f$ on $n=2^k$ bits defined by a complete binary tree

of NAND gates of depth $k$, which achieves $R_0(f) = O(D(f)^{0.7537\ldots})$. ...
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Aran Nayebi, Scott Aaronson, Aleksandrs Belovs, Luca Trevisan

Given a random permutation $f: [N] \to [N]$ as a black box and $y \in [N]$, we want to output $x = f^{-1}(y)$. Supplementary to our input, we are given classical advice in the form of a pre-computed data structure; this advice can depend on the permutation but \emph{not} on ... more >>>