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

  • Authors:

  • Ming Zhang;Min Lin;S. G. Schirmer;Hong-Yi Dai;Zongtan Zhou;Dewen Hu

  • Affiliations:

  • Department of Automatic Control, College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, People's Republic of China 410073;Department of Automatic Control, College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, People's Republic of China 410073;Department of Physics, College of Science, Swansea University, Swansea, UK SA2 8PP;Department of Physics, College of Science, National University of Defense Technology, Changsha, People's Republic of China 410073;Department of Automatic Control, College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, People's Republic of China 410073;Department of Automatic Control, College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, People's Republic of China 410073

  • Venue:
  • Quantum Information Processing
  • Year:
  • 2012

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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.