@inproceedings{ab07be2f77e3455b88e5be36c8834c8f,

title = "The Bayesian learner is optimal for noisy binary search (and pretty good for quantum as well)",

abstract = "We use a Bayesian approach to optimally solve problems in noisy binary search. We deal with two variants: Each comparison is erroneous with independent probability 1 - p. At each stage k comparisons can be performed in parallel and a noisy answer is returned. We present a (classical) algorithm which solves both variants optimally (with respect to p and k), up to an additive term of O(loglog n), and prove matching informationtheoretic lower bounds. We use the algorithm to improve the results of Farhi et al. [11], presenting an exact quantum search algorithm in an ordered list of expected complexity less than (log2 n)/3.",

author = "Or, {Michael Ben} and Avinatan Hassidim",

year = "2008",

doi = "10.1109/FOCS.2008.58",

language = "American English",

isbn = "9780769534367",

series = "Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS",

pages = "221--230",

booktitle = "Proceedings of the 49th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2008",

note = "49th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2008 ; Conference date: 25-10-2008 Through 28-10-2008",

}