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IEEE Transactions on Quantum Engineering

Physics-Informed Weighted Conjugate Light Field Processing and Quantum Neural Networks for Turbulence-Distorted OAM Beam Detection

Gokul Manavalan, Shlomi Arnon

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1109/tqe.2026.3740921

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This project presents a comparative study between a classical convolutional neural network (CNN) and a hybrid quantum neural network (QNN) for orbital angular momentum (OAM) image classification. The analysis focuses on parameter efficiency, showing that the QNN achieves competitive (and in some cases improved) classification performance while using significantly fewer trainable parameters—approximately 8× fewer than the CNN (1,204 vs. 9,928 parameters). This reduction arises from fundamental differences in representation: CNNs rely on large numbers of localized weights in convolutional and fully connected layers, whereas QNNs use parameterized quantum circuits in which a small set of rotation angles governs the evolution of quantum states in a high-dimensional Hilbert space. Through quantum properties such as superposition, entanglement, and global unitary transformations, QNNs enable compact yet expressive feature representations, effectively capturing complex correlations with fewer parameters. The comparison is conducted under controlled conditions, including identical dataset, preprocessing, training splits, and optimization settings, ensuring fairness. The results highlight that model capacity is not solely determined by parameter count, and demonstrate the potential of hybrid quantum models for parameter-efficient learning in challenging visual classification tasks involving distorted OAM modes. cite following paper if you are using this codes: Manavalan, G., Arnon, S. Quantum neural network-based compensation of distorted orbital angular momentum beams in complex media. Sci Rep 16, 1361 (2026). https://doi.org/10.1038/s41598-025-31021-1 Manavalan, G., Arnon, S. Decrypting chaotic visual ciphers via quasi quantum neural networks (Q²NNs). Sci Rep 16, 9937 (2026). https://doi.org/10.1038/s41598-026-41513-3

•Mar 26, 2026•0 citations
Quantum computing is emerging as a transformative tool for communication systems, offering the potential to overcome long-standing physical limitations. In free-space optical networks, orbital angular momentum (OAM) multiplexing promises massive capacity gains, but its practical use is fundamentally constrained by multiphysics degradations such as atmospheric turbulence, volumetric Mie scattering, and stochastic quantum noise. These effects induce nonlinear modal crosstalk and severe beam distortions, against which classical approaches—most notably convolutional neural networks (CNNs)—provide only partial and non-scalable compensation. To address this gap, we report the first use of variational quantum neural networks (QNNs) for adaptive OAM beam compensation in realistic channels. By embedding parameterized entangling layers into a supervised regression pipeline, our QNN achieves end-to-end reconstruction of distorted Laguerre–Gaussian beams with topological charges l ∈ {1,4,8,12}. Using experimentally validated channel parameters, QNNs achieve mean squared error as low as 4.0 × 10− 6, SSIM above 0.99, and bit-error rates suppressed by > 99.9% (0.0125% BER). To ensure scalability, we introduce the quasi-quantum neural network (QqNN), a classical surrogate that emulates quantum dynamics via tensorial projections, achieving near-optimal performance (0.0375% BER) at reduced complexity. This hybrid framework positions QNNs as a quantum-resilient paradigm for OAM decoding and establishes QqNNs as the first scalable surrogate for near-term deployment.
A high-dimensional quantum key distribution (QKD), which adopts degrees of freedom of the orbital angular momentum (OAM) states, is beneficial to realize secure and high-speed QKD. However, the helical phase of a vortex beam that carries OAM is sensitive to the atmospheric turbulence and easily distorted. In this paper, an adaptive compensation method using deep learning technology is developed to improve the performance of OAM-encoded QKD schemes. A convolutional neural network model is first trained to learn the mapping relationship of intensity profiles of inputs and the turbulent phase, and such mapping is used as feedback to control a spatial light modulator to generate a phase screen to correct the distorted vortex beam. Then an OAM-encoded QKD scheme with the capability of real-time phase correction is designed, in which the compensation module only needs to extract the intensity distributions of the Gaussian probe beam and thus ensures that the information encoded on OAM states would not be eavesdropped. The results show that our method can efficiently improve the mode purity of the encoded OAM states and extend the secure distance for the involved QKD protocols in the free-space channel, which is not limited to any specific QKD protocol.
This project presents a comparative study between a classical convolutional neural network (CNN) and a hybrid quantum neural network (QNN) for orbital angular momentum (OAM) image classification. The analysis focuses on parameter efficiency, showing that the QNN achieves competitive (and in some cases improved) classification performance while using significantly fewer trainable parameters—approximately 8× fewer than the CNN (1,204 vs. 9,928 parameters). This reduction arises from fundamental differences in representation: CNNs rely on large numbers of localized weights in convolutional and fully connected layers, whereas QNNs use parameterized quantum circuits in which a small set of rotation angles governs the evolution of quantum states in a high-dimensional Hilbert space. Through quantum properties such as superposition, entanglement, and global unitary transformations, QNNs enable compact yet expressive feature representations, effectively capturing complex correlations with fewer parameters. The comparison is conducted under controlled conditions, including identical dataset, preprocessing, training splits, and optimization settings, ensuring fairness. The results highlight that model capacity is not solely determined by parameter count, and demonstrate the potential of hybrid quantum models for parameter-efficient learning in challenging visual classification tasks involving distorted OAM modes. cite following paper if you are using this codes: Manavalan, G., Arnon, S. Quantum neural network-based compensation of distorted orbital angular momentum beams in complex media. Sci Rep 16, 1361 (2026). https://doi.org/10.1038/s41598-025-31021-1 Manavalan, G., Arnon, S. Decrypting chaotic visual ciphers via quasi quantum neural networks (Q²NNs). Sci Rep 16, 9937 (2026). https://doi.org/10.1038/s41598-026-41513-3