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Metasurface-Assisted Quantum Ghost Discrimination of Polarization Objects

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 Added by Andres Vega
 Publication date 2021
  fields Physics
and research's language is English




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We develop a concept of metasurface-assisted ghost imaging for non-local discrimination between a set of polarization objects. The specially designed metasurfaces are incorporated in the imaging system to perform parallel state transformations in general elliptical bases of quantum-entangled or classically-correlated photons. Then, only four or fewer correlation measurements between multiple metasurface outputs and a simple polarization-insensitive bucket detector after the object can allow for the identification of fully or partially transparent polarization elements and their arbitrary orientation angles. We rigorously establish that entangled photon states offer a fundamental advantage compared to classical correlations for a broad class of objects. The approach can find applications for real-time and low-light imaging across diverse spectral regions in dynamic environments.



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We investigate the effect of turbulence on quantum ghost imaging. We use entangled photons and demonstrate that for a novel experimental configuration the effect of turbulence can be greatly diminished. By decoupling the entangled photon source from the ghost imaging central image plane, we are able to dramatically increase the ghost image quality. When imaging a test pattern through turbulence, this method increased the imaged pattern visibility from V = 0.14 +/- 0.04 to V = 0.29 +/- 0.04.
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In the task of discriminating between nonorthogonal quantum states from multiple copies, the key parameters are the error probability and the resources (number of copies) used. Previous studies have considered the task of minimizing the average error probability for fixed resources. Here we introduce a new state discrimination task: minimizing the average resources for a fixed admissible error probability. We show that this new task is not performed optimally by previously known strategies, and derive and experimentally test a detection scheme that performs better.
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