Optimal design of measurement settings for quantum-state-tomography experiments

Jun Li, Shilin Huang, Zhihuang Luo, Keren Li, Dawei Lu, and Bei Zeng
Phys. Rev. A 96, 032307 – Published 6 September 2017

Abstract

Quantum state tomography is an indispensable but costly part of many quantum experiments. Typically, it requires measurements to be carried out in a number of different settings on a fixed experimental setup. The collected data are often informationally overcomplete, with the amount of information redundancy depending on the particular set of measurement settings chosen. This raises a question about how one should optimally take data so that the number of measurement settings necessary can be reduced. Here, we cast this problem in terms of integer programming. For a given experimental setup, standard integer-programming algorithms allow us to find the minimum set of readout operations that can realize a target tomographic task. We apply the method to certain basic and practical state-tomographic problems in nuclear-magnetic-resonance experimental systems. The results show that considerably fewer readout operations can be found using our technique than by using the previous greedy search strategy. Therefore, our method could be helpful for simplifying measurement schemes to minimize the experimental effort.

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  • Received 17 May 2017

DOI:https://doi.org/10.1103/PhysRevA.96.032307

©2017 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & TechnologyAtomic, Molecular & Optical

Authors & Affiliations

Jun Li1,2,*, Shilin Huang3,2,†, Zhihuang Luo1,2, Keren Li4,2, Dawei Lu5,2, and Bei Zeng6,2,‡

  • 1Beijing Computational Science Research Center, Beijing 100193, China
  • 2Institute for Quantum Computing, University of Waterloo, Waterloo N2L 3G1, Ontario, Canada
  • 3Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
  • 4Department of Physics, Tsinghua University, Beijing 100084, China
  • 5Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China
  • 6Department of Mathematics and Statistics, University of Guelph, Guelph N1G 2W1, Ontario, Canada

  • *lijunwu@mail.ustc.edu.cn
  • eurekash.thu@gmail.com
  • zengb@uoguelph.ca

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Issue

Vol. 96, Iss. 3 — September 2017

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