Advances in Quantum Imaging with Machine Intelligence

被引:10
作者
Moodley, Chane [1 ,2 ]
Forbes, Andrew [2 ]
机构
[1] Raphta PTY LTD, QLAB, ZA-2090 Johannesburg, South Africa
[2] Univ Witwatersrand, Sch Phys, Struct Light Lab, ZA-2000 Johannesburg, South Africa
关键词
deep learning; machine intelligence; machine learning; quantum ghost imaging; quantum imaging; ANGULAR-MOMENTUM STATES; EXPERIMENTAL REALIZATION; SIGNAL RECOVERY; ENTANGLEMENT; RECONSTRUCTION; LITHOGRAPHY; MICROSCOPY; TRANSPORT; METROLOGY; PODOLSKY;
D O I
10.1002/lpor.202300939
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Quantum imaging exemplifies the fascinating and counter-intuitive nature of the quantum world, where non-local correlations are exploited for imaging of objects by remote and non-interacting photons. The field has exploded of late, driven by advances in our fundamental understanding of these processes, but also by advances in technology, for instance, efficient single photon detectors and cameras. Accelerating the progress is the nascent intersection of quantum imaging with artificial intelligence and machine learning, promising enhanced speed and quality of quantum images. This review provides a comprehensive overview of the rapidly evolving field of quantum imaging with a specific focus on the intersection of quantum ghost imaging with artificial intelligence and machine learning techniques. The seminal advances made to date and the open challenges are highlighted, and the likely trajectory for the future is outlined. Quantum imaging exemplifies the fascinating and counter-intuitive nature of the quantum world, where non-local correlations are exploited for the imaging of objects by remote and non-interacting photons. The field has exploded of late, driven by advances in the fundamental understanding of these processes, but also by advances in technology. In this review, a comprehensive overview of the rapidly evolving field of quantum imaging with a specific focus on the intersection of quantum ghost imaging with artificial intelligence and machine learning techniques is provided. image
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页数:30
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