Efficiently reconstructing compound objects by quantum imaging with higher-order correlation functions
AuthorsMikhalychev, A. B.
Karuseichyk, I. L.
Sakovich, A. A.
Lyakhov, D. A.
Michels, Dominik L.
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Science and Engineering Division, 4700 King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Visual Computing Center (VCC)
Permanent link to this recordhttp://hdl.handle.net/10754/660212
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AbstractQuantum imaging has a potential of enhancing the precision of objects reconstruction by exploiting quantum correlations of the imaging field, in particular for imaging with low-intensity fields up to the level of a few photons. However, it generally leads to nonlinear estimation problems. The complexity of these problems rapidly increases with the number of parameters describing the object and the correlation order. Here we propose a way to drastically reduce the complexity for a wide class of problems. The key point of our approach is to connect the features of the Fisher information with the parametric locality of the problem, and to reconstruct the whole set of parameters stepwise by an efficient iterative inference scheme that is linear on the total number of parameters. This general inference procedure is experimentally applied to quantum near-field imaging with higher-order correlated light sources, resulting in super-resolving reconstruction of grey compound transmission objects.
CitationMikhalychev, A. B., Bessire, B., Karuseichyk, I. L., Sakovich, A. A., Unternährer, M., Lyakhov, D. A., … Mogilevtsev, D. (2019). Efficiently reconstructing compound objects by quantum imaging with higher-order correlation functions. Communications Physics, 2(1). doi:10.1038/s42005-019-0234-5
SponsorsA.M., B.B., I.K., A.A.S., A.S., and D.M. acknowledge support from the EU project Horizon-2020 SUPERTWIN id.686731. The authors are thankful to Leonardo Gasparini, Majid Zarghami, and Matteo Perenzoni from the Fondazione Bruno Kessler FBK in Trento, Italy, for the provision of SuperEllen. A.M., I.K., A.A.S., and D.M. acknowledge support from the National Academy of Sciences of Belarus program “Convergence”. A.M., I.K., and A.A.S. acknowledge support from BRFFR project F18U-006.
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