Quantum Lower Bound for Inverting a Permutation with Advice
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Aaronson_Quantum lower.pdf
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Author(s)
Nayebi, Aran
Aaronson, Scott
Belovs, Aleksandrs
Trevisan, Luca
Date Issued
September 2015
Journal
Quantum Information & Computation
Publisher
Rinton Press
Citation
Nayebi, Aran, Scott Aaronson, Aleksandrs Belovs, and Luca Trevisan. "Quantum Lower Bound for Inverting a Permutation with Advice." Quantum Information and Computation 15(11&12), 901-913.
Version
Author's final manuscript
Abstract
Given a random permutation f : [N] → [N] as a black box and y ∈ [N], we want to output x = f[superscript −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 not on the input y. Classically, there is a data structure of size [~ over O](S) and an algorithm that with the help of the data structure, given f(x), can invert f in time [~ over O](T), for every choice of parameters S, T, such that S · T ≥ N. We prove a quantum lower bound of T[superscript 2] · S = [~ over Ω](εN) for quantum algorithms that invert a random permutation f on an ε fraction of inputs, where T is the number of queries to f and S is the amount of advice. This answers an open question of De et al.
We also give a Ω(√N/m) quantum lower bound for the simpler but related Yao’s box problem, which is the problem of recovering a bit x[subscript j], given the ability to query an N-bit string x at any index except the j-th, and also given m bits of classical advice that depend on x but not on j.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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http://www.rintonpress.com/journals/qiconline.html#v15n1112
