Lens-free quantum ghost imaging

Dmitriy A. Balakin, Aleksandr V. Belinsky

Ying-Chuan Liu, Le‐Man Kuang

In this paper, we propose a theoretical scheme of many-ghost imaging in terms of $N$th-order correlated thermal light. We obtain the Gaussian thin lens equations in the many-ghost imaging protocol. We show that it is possible to produce $N\ensuremath{-}1$ ghost images of an object at different places in a nonlocal fashion by means of a higher order correlated imaging process with an $N$th-order correlated thermal source and correlation measurements. We investigate the visibility of the ghost images in the scheme and obtain the upper bounds of the visibility for the $N$th-order correlated thermal-light ghost imaging. It is found that the visibility of the ghost images can be dramatically enhanced when the order of correlation becomes larger. It is pointed out that the many-ghost imaging phenomenon is an observable physical effect induced by higher order coherence or higher order correlations of optical fields.

R. I. Khakimov, B. M. Henson, D. K. Shin +4 more

Andres Vega, Sina Saravi, Thomas Pertsch +1 more

We theoretically analyze quantum ghost diffraction of a double-slit, and find that for certain parameters an image of the double-slit without interference can be obtained, similar to classical incoherent lens-less imaging.

Mikhail I. Kolobov

Tailong Xiao, Xinliang Zhai, Xiaoyan Wu +2 more

Abstract Demonstrating the practical advantage of quantum computation remains a long-standing challenge whereas quantum machine learning becomes a promising application that can be resorted to. In this work, we investigate the practical advantage of quantum machine learning in ghost imaging by overcoming the limitations of classical methods in blind object identification and imaging. We propose two hybrid quantum-classical machine learning algorithms and a physical-inspired patch strategy to allow distributed quantum learning with parallel variational circuits. In light of the algorithm, we conduct experiments for imaging-free object identification and blind ghost imaging under different physical sampling rates. We further quantitatively analyze the advantage through the lens of information geometry and generalization capability. The numerical results showcase that quantum machine learning can restore high-quality images but classical machine learning fails. The advantage of identification rate are up to 10% via fair comparison with the classical machine learning methods. Our work explores a physics-related application capable of practical quantum advantage, which highlights the prospect of quantum computation in the machine learning field.