Abstract Quantum machine learning (QML) offers a breakthrough by producing atypical patterns that classical systems cannot efficiently produce. This study explores quantum neural networks (QNN) for classifying orbital angular momentum (OAM) modes. A fully quantum model achieving 99.8% accuracy across 10 OAM classes and a hybrid ConvQuantum model reaching accuracy levels of 99.8% under atmospheric turbulence strength (AT) of is developed. The ConvQuantum model significantly reduces prediction time, averaging 0.00025 s per sample, which is essential for real‐time free space optical (FSO) communication. To demonstrate quantum technology's potential in OAM‐FSO communication, a 100 × 100 grayscale image is successfully transmitted, and compared the results with several known models in the field, including feedforward neural networks (FNN), convolutional neural network (CNN), and a vision transformer (ViT) model. The findings indicate that the QNN models outperform classical approaches in calculation time, showcasing the efficiency and enhanced processing power of quantum computing in OAM‐FSO communication.
Abstract Quantum machine learning (QML) offers a breakthrough by producing atypical patterns that classical systems cannot efficiently produce. This study explores quantum neural networks (QNN) for classifying orbital angular momentum (OAM) modes. A fully quantum model achieving 99.8% accuracy across 10 OAM classes and a hybrid ConvQuantum model reaching accuracy levels of 99.8% under atmospheric turbulence strength (AT) of is developed. The ConvQuantum model significantly reduces prediction time, averaging 0.00025 s per sample, which is essential for real‐time free space optical (FSO) communication. To demonstrate quantum technology's potential in OAM‐FSO communication, a 100 × 100 grayscale image is successfully transmitted, and compared the results with several known models in the field, including feedforward neural networks (FNN), convolutional neural network (CNN), and a vision transformer (ViT) model. The findings indicate that the QNN models outperform classical approaches in calculation time, showcasing the efficiency and enhanced processing power of quantum computing in OAM‐FSO communication.
ABSTRACT We present a hybrid quantum–classical convolutional neural network (QC‐CNN) for classifying orbital angular momentum (OAM) modes in free‐space optical (FSO) channels affected by atmospheric turbulence. The approach embeds a convolutional quantum (quanvolutional) preprocessing layer that encodes 2 × 2 image patches into parameterized quantum circuits with rotation and entangling operations, yielding quantum‐extracted features that feed into conventional convolutional layers. We employ a two‐stage training protocol: large‐scale pretraining on 940 000 intensity images spanning a range of turbulence strengths, followed by task‐specific fine‐tuning with selective layer freezing. Turbulence was modeled using a modified Hill–Andrews phase screen and evaluated at multiple strengths over 1000 m propagation distance. The hybrid model achieves high classification accuracies (>97% across strong turbulence scenarios) and reduces bit‐error rates (BER) from up to 0.11 without pretraining to 0.001–0.08 after quantum‐layer pretraining. Results demonstrate that quantum‐inspired preprocessing combined with transfer learning significantly improves resilience to severe atmospheric distortions and offers a scalable route toward real‐time, high‐fidelity OAM demodulation in FSO systems.
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.
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
Orbital angular momentum (OAM) of light has enabled quantum encoding in higher dimensional Hilbert spaces, opening up new opportunities for high capacity satellite-to-ground quantum communications. In this paper, machine learning based recognition of OAM modes for satellite-to-ground communication is proposed. Most of the machine learning models for OAM mode detection reported in literature directly take intensity and phase images of OAM modes as inputs. A novel attempt is made in the proposed work, wherein Histogram of Gradients (HOG) features are extracted from these images and fed to machine learning models. Two architectures are proposed in this paper for OAM mode detection and their performances are reported. The performances of proposed models are also compared under different atmospheric turbulence conditions, propagation distances and the results are reported. In order to evaluate the efficacy of proposed models, performance results of OAM mode detection using various other methods reported in literature are compared with the results obtained from proposed work. It is observed that the performance of proposed models are almost on par with the models reported in literature. Maximum recognition accuracy of 100% with a prediction time ranging between 83 - 157ms, was achieved using the proposed models. The proposed OAM mode detection models can be used for various other quantum communication applications too, including underwater communication.
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.
The emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapid development of quantum computing has spurred interest in quantum machine learning (QML), which integrates the strengths of quantum computation with the representational power of deep learning. In QML, parameterized quantum circuits offer the potential to capture complex image features, define complex decision boundaries, and provide other computational advantages. This paper investigates hybrid quantum-classical vision architectures, with a focus on hybrid quantum-classical convolutional neural networks and hybrid quantum-classical vision transformers. These hybrid models explore both quantum pre-processing and post-processing of data, respectively, where quantum circuits are strategically integrated into the data pipeline to enhance model performance. Our results suggest that these hybrid models can enhance accuracy and computational efficiency in vision-related tasks, even with the constraints of current noisy intermediate-scale quantum devices.