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Tomographic completeness and robustness of quantum reservoir networks

Tanjung Krisnanda1,2,*, Huawen Xu1, Sanjib Ghosh3,†, and Timothy C. H. Liew1,4

  • 1School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore, Singapore
  • 2Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543 Singapore, Singapore
  • 3Beijing Academy of Quantum Information Sciences, West Building 3, No. 10 Xibeiwang East Road, Haidian District, Beijing 100193, China
  • 4MajuLab, International Joint Research Unit UMI 3654, CNRS, Université Côte d'Azur, Sorbonne Université, National University of Singapore, Nanyang Technological University, Singapore

  • *tanjung@nus.edu.sg
  • †sanjibghosh@baqis.ac.cn
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Phys. Rev. A 107, 042402 – Published 4 April, 2023

DOI: https://doi.org/10.1103/PhysRevA.107.042402

Abstract

Quantum reservoir processing offers an option to perform quantum tomography of input objects by postprocessing quantities, obtained from local measurements, from a quantum reservoir network that has interacted with the former. We develop a method to assess a tomographic completeness criterion for arbitrary quantum reservoir architectures. Furthermore, we propose a figure of merit that quantifies their robustness against imperfections. Measured quantities from the reservoir nodes correspond to effective observables acting on the input objects, and we provide a way to retrieve them. Finally, we present examples of quantum tomography for demonstration. Our general method offers guidance in optimizing implementations of quantum reservoir processing.

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Information

Phys. Rev. A 107, 042402– Published 4 April, 2023
  • Received 19 July 2022
  • Revised 9 December 2022
  • Accepted 22 March 2023
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