All-digital quantum ghost imaging: tutorial

Abstract

Quantum ghost imaging offers many advantages over classical imaging, including the ability to probe an object with one wavelength and record the image with another, while low photon fluxes offer the ability to probe objects with fewer photons, thereby avoiding photo-damage to light sensitive structures such as biological organisms. Progressively, ghost imaging has advanced from single-pixel scanning systems to two-dimensional (2D) digital projective masks, which offer a reduction in image reconstruction times through shorter integration times. In this tutorial, we describe the essential ingredients in an all-digital quantum ghost imaging experiment and guide the user on important considerations and choices to make, aided by practical examples of implementation. We showcase several image reconstruction algorithms using two different 2D projective mask types and discuss the utility of each. We additionally discuss a notable artifact of a specific reconstruction algorithm and projective mask combination and detail how this artifact can be used to retrieve an image signal heavily buried under artifacts. Finally, we end with a brief discussion on artificial intelligence (AI) and machine learning techniques used to reduce image reconstruction times. We believe that this tutorial will be a useful guide to those wishing to enter the field, as well as those already in the field who wish to introduce AI and machine learning to their toolbox.