LAUSR

Quantum 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.Quantum information provides a powerful method for improving imaging beyond classical limits, but the complexity typically scales non-linearly with the number of parameters. Here, the authors demonstrate a method to reduce the number of parameters required and identify an optimal degree of photon correlation for imaging.