J
Jeffrey H. Shapiro
Researcher at Massachusetts Institute of Technology
Publications - 401
Citations - 20076
Jeffrey H. Shapiro is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Photon & Quantum key distribution. The author has an hindex of 65, co-authored 395 publications receiving 17401 citations.
Papers
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Proceedings ArticleDOI
Signal-to-noise ratio of Gaussian-state ghost imaging
TL;DR: In this paper, the signal-to-noise ratios of pseudothermal and biphoton ghost imagers were derived and compared by means of a unified Gaussian-state analysis.
Proceedings ArticleDOI
Sub-Rayleigh imaging via N-photon detection
Fabrizio Guerrieri,Lorenzo Maccone,Franco N. C. Wong,Jeffrey H. Shapiro,Simone Tisa,Franco Zappa +5 more
TL;DR: In this article, the authors demonstrate resolution enhancement beyond the Rayleigh diffraction limit using an N-photon detection strategy that is implemented with a singlephoton imager, which is in good agreement with theory proposed by Giovannetti et al.
Journal Article
Floodlight quantum key distribution: Demonstrating a framework for high-rate secure communication
Majorization Theory Approach to the Gaussian Channel Minimum Entropy Conjecture
TL;DR: In this paper, it was shown that proving a Gaussian minimum entropy conjecture for a quantum-limited amplifier is actually sufficient to confirm this capacity conjecture, and provided a strong argument towards this proof by exploiting a connection between quantum entanglement and majorization theory.
Proceedings ArticleDOI
Multispectral sensor fusion for ground-based target orientation estimation : FLIR, LADAR, HRR
Joseph Kostakis,Matthew Cooper,Thomas J. Green,Michael I. Miller,Joseph A. O'Sullivan,Jeffrey H. Shapiro,Donald L. Snyder +6 more
TL;DR: This paper quantitatively examines both pose-dependent variations in performance, and the relative performance of the aforementioned sensors via mean squared error analysis, using a Lie Group representation of the orientation space and a Bayesian estimation framework.