Regression of high-dimensional angular momentum states of light

The orbital angular momentum (OAM) of light is an infinite-dimensional degree of freedom of light with several applications in both classical and quantum optics. However, to fully take advantage of the potential of OAM states, reliable detection platforms to characterize generated states in experimental conditions are needed. Here, we present an approach to reconstruct input OAM states from measurements of the spatial intensity distributions they produce. To obviate issues arising from intrinsic symmetry of Laguerre-Gauss modes, we employ a pair of intensity profiles per state projecting it only on two distinct bases, showing how this allows to uniquely recover input states from the collected data. Our approach is based on a combined application of dimensionality reduction via principal component analysis, and linear regression, and thus has a low computational cost during both training and testing stages. We showcase our approach in a real photonic setup, generating up-to-four-dimensional OAM states through quantum walk dynamics. The high performance and versatility of the demonstrated approach make it an ideal tool to characterize high-dimensional states in quantum information protocols.

Automated summary

The orbital angular momentum (OAM) of light is an infinite-dimensional degree of freedom with various applications in optics. This study presents an approach to reconstruct input OAM states by analyzing the spatial intensity distributions they produce. By using two intensity profiles per state, the inherent symmetry issue of Laguerre-Gauss modes is avoided, and the input states can be uniquely recovered from the collected data. The demonstrated approach, based on dimensionality reduction and linear regression, shows high performance and versatility in characterizing high-dimensional states in quantum information protocols.