Quantum Radar: A Brief Review of Current Progress and New Methods of Understanding and Signal Processing, Validated by Experimental Results

Matthew J. Brandsema, Xavier Szigethy

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Modern approaches to quantum radar implementation utilize the intrinsic correlations of two-mode squeezed vacuum photon pairs emerging from a nonlinear interaction. The most popular approach has been the use of delay lines for the idler and performing joint measurements on the idler and returning signal together. In this paper, it is argued and shown that this sort of implementation is not necessary to extract the quantum cross correlation terms. Immediate detection of the idler and later cross correlation on a large enough data set will yield identical covariance terms. Moreover, immediate idler detection facilitates the use of conventional radar signal processing which allows existing waveform toolboxes of classical radar to be utilized for quantum radar. This allows a much more relaxed set of constraints on the implementation of quantum radar techniques. This paper discusses these concepts, including new detection techniques from the author, and validates the framework with some preliminary experimental data. The presented data, as well as the recent work of others allows for the possibility of a much larger quantum advantage than previously thought, particularly when comparing to real-world practical classical sensors.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXVII
EditorsAbigail S. Hedden, Gregory J. Mazzaro, Ann Marie Raynal
ISBN (Electronic)9781510661844
StatePublished - 2023
EventRadar Sensor Technology XXVII 2023 - Orlando, United States
Duration: May 1 2023May 3 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceRadar Sensor Technology XXVII 2023
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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