arXiv subjects
Publications and source records attributed to Stacey Jeffery.
We present an extension to the quantum walk search framework that facilitates quantum walks with nested updates. We apply it to give a quantum walk algorithm for 3-Distinctness with query complexity ~O(n^{5/7}), matching the best known upper bound (obtained via learning graphs) up to log factors. Furthermore, our algorithm has time complexity ~O(n^{5/7}), improving the previous ~O(n^{3/4}).
We develop a new framework that extends the quantum walk framework of Magniez, Nayak, Roland, and Santha, by utilizing the idea of quantum data structures to construct an efficient method of nesting quantum walks. Surprisingly, only classical data structures were considered before for searching via quantum walks. The recently proposed learning graph framework of Belovs has yielded improved upper bounds for several problems, including triangle finding and more general subgraph detection. We exhibit the power of our framework by giving a simple explicit constructions that reproduce both the $O(n^{35/27})$ and $O(n^{9/7})$ learning graph upper bounds (up to logarithmic factors) for triangle finding, and discuss how other known upper bounds in the original learning graph framework can be converted to algorithms in our framework. We hope that the ease of use of this framework will lead to the discovery of new upper bounds.
The quantum query complexity of Boolean matrix multiplication is typically studied as a function of the matrix dimension, n, as well as the number of 1s in the output, \ell. We prove an upper bound of O (n\sqrt{\ell}) for all values of \ell. This is an improvement over previous algorithms for all values of \ell. On the other hand, we show that for any \eps < 1 and any \ell <= \eps n^2, there is an Ω(n\sqrt{\ell}) lower bound for this problem, showing that our algorithm is essentially tight. We first reduce Boolean matrix multiplication to several instances of graph collision. We then provide an algorithm that takes advantage of the fact that the underlying graph in all of our instances is very dense to find all graph collisions efficiently.
We present three voting protocols with unconditional privacy and correctness, without assuming any bound on the number of corrupt participants. All protocols have polynomial complexity and require private channels and a simultaneous broadcast channel. Unlike previously proposed protocols in this model, the protocols that we present deterministically output the exact tally. Our first protocol is a basic voting scheme which allows voters to interact in order to compute the tally. Privacy of the ballot is unconditional in the sense that regardless of the behavior of the dishonest participants nothing can be learned through the protocol that could not be learned in an ideal realisation. Unfortunately, a single dishonest participant can make the protocol abort, in which case the dishonest participants can nevertheless learn the outcome of the tally. Our second protocol introduces voting authorities which improves the communication complexity by limiting interaction to be only between voters and authorities and among the authorities themselves; the simultaneous broadcast is also limited to the authorities. In the second protocol, as long as a single authority is honest, the privacy is unconditional, however, a single corrupt authority or a single corrupt voter can cause the protocol to abort. Our final protocol provides a safeguard against corrupt voters by enabling a verification technique to allow the authorities to revoke incorrect votes without aborting the protocol. Finally, we discuss the implementation of a simultaneous broadcast channel with the use of temporary computational assumptions, yielding versions of our protocols that achieve everlasting security.