Optical wireless communication (OWC) is in high demand due to its potential for high-speed data transmission and spectrum relief in congested radio frequencies. However, real-world implementations face significant challenges, particularly due to atmospheric turbulence, which distorts the optical signal, making reliable OWC difficult to achieve. In this study, we propose a novel methodology to mitigate the effects of turbulence by investigating the application of orbital angular momentum (OAM) and deep learning for robust OWC. In this study, we propagate information in the form of optical Laguerre-Gaussian (LG) beams through turbulent atmosphere and use the conjugate light field (CLF) method to retrieve the information. This process is further integrated into a deep learning framework to enhance the OWC systems performance. This integration reduces the computational load by minimizing the network classes to two CLF outcomes instead of using all LG beam modes as classes. The proposed method achieves faster processing time by reducing computational load, essential for real-time applications. An optical tabletop experiment also verifies the proposed technique, where we achieve a bit error rate (BER) of 2.44 × 10⁻⁴, demonstrating a performance that surpasses baseline algorithms by over ∼99% for a given set of parameters. To the best of the author's knowledge, this is the first time this concept has been presented for the use of OWC in turbulent atmospheric conditions.

Recent research has increasingly focused on Laguerre-Gaussian (LG) beams carrying a topological charge (l) due to their demonstrated ability to enhance signal transmission through turbid media. This promising phenomenon has spurred considerable interest in understanding the complex propagation dynamics of such beams, with the goal of optimizing optical systems for highly scattering environments. In this study, we experimentally investigate the transmission characteristics of LG beams with various topological charges as they propagate through a turbid multiple-scattering medium at different depths. By analyzing the resulting transmission data, we calculate the scattering mean free path (\(\:{{\Lambda\:}}^{*}\)) for each LG mode and show that \(\:{{\Lambda\:}}^{*}\) increases with increasing values of l. Moreover, we show that beams with higher topological charges traverse greater distances within diffuse media while retaining their characteristic donut shape using Monte Carlo simulations. Our findings provide an empirical characterization of the relationship between topological charge and scattering mean free path and also explain the improved transmission with increasing l, at least until optical depths of ~ 40 \(\:{{\Lambda\:}}^{*}\), highlighting potential trends that warrant further investigation regarding the potential of applying Laguerre-Gaussian beams for enhanced optical imaging and spectroscopy through highly scattering media.

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.

We propose a novel Quasi Quantum Neural Network (Q²NN) architecture that integrates classical convolutional networks with variational quantum circuits to decrypt grayscale images encrypted via a multilayer chaotic cryptosystem. This hybrid framework addresses the limitations of classical models when applied to highly nonlinear, permutation-diffusion encrypted data. Q²NN adopts a dual-branch encoder-decoder design, comprising a classical convolutional autoencoder and a variational quantum subnetwork, fused via an adaptive, learnable module that unifies classical and quantum representations. For evaluation, we implement a custom encryption pipeline combining Arnold Cat Map-based spatial permutation, Logistic Map-based pixel-level diffusion, and zigzag chaotic transformations. These operations produce ciphertexts that are key-sensitive, structurally obfuscated, and statistically complex, presenting a significant challenge for conventional decryption models. Leveraging supervised training with known ciphertext-plaintext pairs, Q²NN approximates an inverse mapping from the encrypted domain back to the original image space by co-learning in a joint classical–quantum feature space. Experimental validation on the MNIST dataset demonstrates near-perfect decryption fidelity (MSE < 0.004; SSIM > 0.96), outperforming classical and quantum-only baselines. The underlying chaotic encryption scheme further shows strong cryptographic resilience, with Shannon entropy ≈ 7.96, NPCR > 99.5%, UACI ≈ 33.3%, and negligible spatial correlation. These results highlight the potential of Q²NNs for secure and explainable image decryption, and point to promising directions for hybrid intelligent cryptographic systems in the post-quantum era.

Abnormal gait associated with neuromuscular and musculoskeletal disorders represents a growing clinical burden, particularly in aging populations. This study presents a modular, low-cost Smart Rehabilitation Walker (SRW) that integrates multimodal sensing and real-time haptic feedback to enable simultaneous gait monitoring and corrective intervention in both clinical and home environments. The system combines force-sensing resistors for bilateral load symmetry assessment, inertial measurement units for fall detection, and surface electromyography (sEMG) for neuromuscular activity monitoring within a closed-loop assistive feedback architecture. A 15-day pilot study involving ten individuals with rheumatoid arthritis and clinically observed neurological gait abnormalities demonstrated measurable improvements in gait biomechanics. The Force Symmetry Index (FSI), calculated using the Robinson symmetry metric, decreased from an average of 0.9691 to 0.2019, corresponding to a 79.26% average reduction in inter-limb load asymmetry. Concurrently, sEMG measurements showed a substantial increase in neuromuscular activation (ΔEMG = 4.28), with statistical analysis confirming a significant improvement across participants (paired t-test: t(9) = 13.58, p < 0.001). To model rehabilitation trajectories, a nonlinear predictive framework based on Gaussian Process Regression achieved high predictive accuracy (R² ≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring.