On the role of a priori knowledge in the optimization of quantum information processing

On the role of a priori knowledge in the optimization of quantum information processing This paper explores the role of a priori knowledge in the optimization of quantum information processing by investigating optimum unambiguous discrimination problems for both the qubit and qutrit states. In general, a priori knowledge in optimum unambiguous discrimination problems can be classed into two types: a priori knowledge of discriminated states themselves and a priori probabilities of preparing the states. It is clarified that whether a priori probabilities of preparing discriminated states are available or not, what type of discriminators one should design just depends on what kind of the classical knowledge of discriminated states. This is in contrast to the observation that choosing the parameters of discriminators not only relies on the a priori knowledge of discriminated states, but also depends on a priori probabilities of preparing the states. Two types of a priori knowledge can be utilized to improve optimum performance but play the different roles in the optimization from the view point of decision theory. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quantum Information Processing Springer Journals

On the role of a priori knowledge in the optimization of quantum information processing

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Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media, LLC
Subject
Physics; Theoretical, Mathematical and Computational Physics; Mathematics, general; Quantum Physics; Physics, general; Computer Science, general
ISSN
1570-0755
eISSN
1573-1332
D.O.I.
10.1007/s11128-011-0278-2
Publisher site
See Article on Publisher Site

Abstract

This paper explores the role of a priori knowledge in the optimization of quantum information processing by investigating optimum unambiguous discrimination problems for both the qubit and qutrit states. In general, a priori knowledge in optimum unambiguous discrimination problems can be classed into two types: a priori knowledge of discriminated states themselves and a priori probabilities of preparing the states. It is clarified that whether a priori probabilities of preparing discriminated states are available or not, what type of discriminators one should design just depends on what kind of the classical knowledge of discriminated states. This is in contrast to the observation that choosing the parameters of discriminators not only relies on the a priori knowledge of discriminated states, but also depends on a priori probabilities of preparing the states. Two types of a priori knowledge can be utilized to improve optimum performance but play the different roles in the optimization from the view point of decision theory.

Journal

Quantum Information ProcessingSpringer Journals

Published: Aug 25, 2011

References

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