Robust quantum spatial search

Robust quantum spatial search Quantum spatial search has been widely studied with most of the study focusing on quantum walk algorithms. We show that quantum walk algorithms are extremely sensitive to systematic errors. We present a recursive algorithm which offers significant robustness to certain systematic errors. To search N items, our recursive algorithm can tolerate errors of size $$O(1{/}\sqrt{\ln N})$$ O ( 1 / ln N ) which is exponentially better than quantum walk algorithms for which tolerable error size is only $$O(\ln N{/}\sqrt{N})$$ O ( ln N / N ) . Also, our algorithm does not need any ancilla qubit. Thus our algorithm is much easier to implement experimentally compared to quantum walk algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quantum Information Processing Springer Journals

Robust quantum spatial search

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Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Physics; Quantum Information Technology, Spintronics; Quantum Computing; Data Structures, Cryptology and Information Theory; Quantum Physics; Mathematical Physics
ISSN
1570-0755
eISSN
1573-1332
D.O.I.
10.1007/s11128-016-1322-z
Publisher site
See Article on Publisher Site

Abstract

Quantum spatial search has been widely studied with most of the study focusing on quantum walk algorithms. We show that quantum walk algorithms are extremely sensitive to systematic errors. We present a recursive algorithm which offers significant robustness to certain systematic errors. To search N items, our recursive algorithm can tolerate errors of size $$O(1{/}\sqrt{\ln N})$$ O ( 1 / ln N ) which is exponentially better than quantum walk algorithms for which tolerable error size is only $$O(\ln N{/}\sqrt{N})$$ O ( ln N / N ) . Also, our algorithm does not need any ancilla qubit. Thus our algorithm is much easier to implement experimentally compared to quantum walk algorithms.

Journal

Quantum Information ProcessingSpringer Journals

Published: Apr 25, 2016

References

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