Quantum illumination with gaussian states and detection

Rights statement
Awarding institution
  • University of Strathclyde
Date of award
  • 2023
Thesis identifier
  • T16776
Person Identifier (Local)
  • 201777153
Qualification Level
Qualification Name
Department, School or Faculty
  • Quantum illumination is a technique that uses quantum states of radiation, often quantum entangled states, for object detection. Potential applications are in the development of quantum radar or quantum rangefinding devices. This thesis investigates quantum illumination theory with photodetection. Inclusion of detection, by first measuring the idler radiation mode that is entangled with another signal radiation mode, can condition the remaining signal mode into a nonclassical radiation state with a gain in signal energy. This can leads to results showing quantum signals increasing the likelihood of successful detection events of target-reflected signals, even in situations with low signal energy and a noisy background, compared to coherent state signals. The analysis uses the Gaussian quantum information framework, which models the statistical properties of the radiation states as Gaussian distributions in quantum phase space. Optimal measurement of object-reflected signals is presented via state discrimination theory, show that the entangled two-mode squeezed vacuum state is most effective at reducing the discrimination error between the reflected signal-plus- background noise vs. background noise alone, compared to using a classical coherent signal of the same energy or against all single-mode Gaussian states. But with a detection limited, sub-optimal measurement analysis of quantum illumination, there are certain regimes where a coherent state can outperform a two-mode squeezed vacuum in terms of raising signal detection probability. Finally, sequential detection results comparing performance of classical vs. quantum illumination were modelled by Monte Carlo simulations, in order to show estimated object presence or absence using conditional detection probabilities and Bayes’ theorem.
Advisor / supervisor
  • Pritchard, Jonathan
  • Jeffers, John
Resource Type