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Complex Light and Optical Forces

Arbitrary control of orbital angular momentum entanglement using a diffractive deep neural network-based inverse design technique

Youngbin Na, Do‐Kyeong Ko

Published Jan 24, 2025• 0 citations
Publisher
DOI
10.1117/12.3044246

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The Orbital angular momentum (OAM) of light is regarded as a valuable resource in quantum technology, especially in quantum communication and quantum sensing and ranging. However, the OAM state of light is susceptible to undesirable experimental conditions such as propagation distance and phase distortions, which hinders the potential for the realistic implementation of relevant technologies. In this article, we exploit an enhanced deep learning neural network to identify different OAM modes of light at multiple propagation distances with phase distortions. Specifically, our trained deep learning neural network can efficiently identify the vortex beam’s topological charge and propagation distance with 97% accuracy. Our technique has important implications for OAM based communication and sensing protocols.
High-dimensional quantum entanglement is an important resource for fundamental research and quantum technological application. Recently, the orbital angular momentum (OAM) of light has received much attention to realize high-dimensional quantum systems, and generating and manipulating the OAM entanglement has become an important topic. Here, we propose an inverse design technique to manipulate the OAM entanglement of the photon pair generated through spontaneous parametric down-conversion. The proposed technique directly tailors the OAM states of entangled photon pairs by shaping the incident pump beam. In particular, we construct a physics-informed model by leveraging the backpropagation algorithm and adhering to the overlap integral relation. Our model automatically optimizes diffractive masks, allowing the structured pump to quantitatively modulate the OAM distribution. The generation of maximally entangled states in different dimensions is demonstrated numerically and experimentally.
Information transfer rates in optical communications may be dramatically increased by making use of spatially non-Gaussian states of light. Here, we demonstrate the ability of deep neural networks to classify numerically generated, noisy Laguerre-Gauss modes of up to 100 quanta of orbital angular momentum with near-unity fidelity. The scheme relies only on the intensity profile of the detected modes, allowing for considerable simplification of current measurement schemes required to sort the states containing increasing degrees of orbital angular momentum. We also present results that show the strength of deep neural networks in the classification of experimental superpositions of Laguerre-Gauss modes when the networks are trained solely using simulated images. It is anticipated that these results will allow for an enhancement of current optical communications technologies.