To meet rapidly growing communication demands, researchers have focused on structured light-based shift keying techniques. However, higher-order modes are prone to large diffraction divergence and are easily perturbed. In this study, we experimentally demonstrate what we believe to be a novel coherence shaping method for petal-like structures of optical vortices, enabling the generation of non-diffraction interference states between completely coherent and incoherent states. In addition, we propose a coherence shift keying (CSK) scheme enabled by deep learning, and a well-trained model can achieve a high recognition accuracy (>0.997) of interference states under practical conditions, including complex environments. Further experimental validation has confirmed that the minimum achievable visibility-level bandwidth is 0.02. This study provides a new, to the best of our knowledge, platform for low-order structured light mode-based high-capacity and encrypted shift keying communication systems.
To meet rapidly growing communication demands, researchers have focused on structured light-based shift keying techniques. However, higher-order modes are prone to large diffraction divergence and are easily perturbed. In this study, we experimentally demonstrate what we believe to be a novel coherence shaping method for petal-like structures of optical vortices, enabling the generation of non-diffraction interference states between completely coherent and incoherent states. In addition, we propose a coherence shift keying (CSK) scheme enabled by deep learning, and a well-trained model can achieve a high recognition accuracy (>0.997) of interference states under practical conditions, including complex environments. Further experimental validation has confirmed that the minimum achievable visibility-level bandwidth is 0.02. This study provides a new, to the best of our knowledge, platform for low-order structured light mode-based high-capacity and encrypted shift keying communication systems.
Abstract The rich structure of transverse spatial modes of structured light has facilitated their extensive applications in quantum information and optical communication. The Laguerre–Gaussian (LG) modes, which carry a well-defined orbital angular momentum (OAM), consist of a complete orthogonal basis describing the transverse spatial modes of light. The application of OAM in free-space optical communication is restricted due to the experimentally limited OAM numbers and the complex OAM recognition methods. Here, we present a novel method that uses the advanced deep learning technique for LG modes recognition. By discretizing the spatial modes of structured light, we turn the OAM state regression into classification. A proof-of-principle experiment is also performed, showing that our method effectively categorizes OAM states with small training samples and the accuracy exceeds 99% from three-dimensional (3D) to fifteen-dimensional (15D) space. By assigning each category a classical information, we further apply our approach to an image transmission task, achieving a transmission accuracy of 99.58%, which demonstrates the ability to encode large data with low OAM number. This work opens up a new avenue for achieving high-capacity optical communication with low OAM number based on structured light.
In the age of information explosion, the conventional optical communication protocols are rapidly reaching the limits of their capacity, as almost all available degrees of freedom (e.g., wavelength, polarization) for division multiplexing have been explored to date. Recent advances in coherent mode division multiplexing have greatly facilitated high-speed optical communications and secure, high-capacity information storage and transfer. However, coherent mode division multiplexing is quite vulnerable to even minute environmental disturbances which can cause significant information loss. Here, we propose and experimentally demonstrate a paradigm shift to incoherent mode division multiplexing for high-security optical information encryption by harnessing the degree of coherence of structured random light beams. In contrast to the conventional techniques, our approach does not require mode orthogonality to circumnavigate unwanted mode crosstalk. In addition, our protocol has, in principle, no upper bound on its capacity. Thanks to the extreme robustness of structured random light to external perturbations, we are able to achieve highly accurate information encryption and decryption in the adverse environment. The proposed protocol opens new horizons in an array of fields, such as optical communications and cryptography, and it can be relevant for information processing with acoustical, matter as well as other types of waves.
Vortex beams (VBs) shift keying is a digital communication technique that encodes data by rapidly switching or shifting the orbital angular momentum (OAM) state of a light beam, improving the performance of free-space optical communication. However, current VB-based shift keying secure systems still face several issues, such as limited capacity, the need for model retraining during key renewal and in some cases, vulnerability to ciphertext-only or known-plaintext attacks. In this article, we address these critical challenges by integrating VB array, phase encoding, spatial coherence modulation and deep learning techniques. By utilizing 9 spatial channels each with 16-ary phase differences in the proof-of-principle experiments, the information content carried by a single symbol reach 36 bits. Moreover, a double-encryption transmission scheme is proposed, leveraging the coherence structure and spatial reading trajectory of the VB array. This method has been experimentally verified to provide ultra-high security at the physical layer, effectively resisting both ciphertext-only and known-plaintext attacks. The corresponding bit error rates and pixel error rates can highly reach at least 3.7 × 10−1 and 8.6 × 10−1, respectively. Our work offers the potential for next generation VB-array-based high security FSO communication. Vortex-beam-based shift keying secure systems enhance secure digital communications, while it faces challenges such as limited capacity and vulnerability to attacks. This paper presents a double encryption scheme leveraging coherent structures and spatial reading trajectories whichenables key updates without retraining, and maintains high security.
Structured light with customized complex topological pattern inspires diverse classical and quantum investigations underpinned by accurate detection techniques. However, the current detection schemes are limited to vortex beam with simple phase singularity. The precise recognition of general structured light with multiple singularities remains elusive. Here, we report a deep learning (DL) framework that can unveil multi-singularity phase structures in an end-to-end manner after feeding only two intensity patterns upon beam propagation captured via a camera, thus unleashing intuitive information of twisted photons. The DL toolbox can also acquire phases of Laguerre-Gaussian (LG) modes with single singularity and other general phase objects likewise. Leveraging this DL platform, a phase-based optical secret sharing (OSS) protocol is proposed, which is based on a more general class of multi-singularity modes than conventional LG beams. The OSS protocol features strong security, wealthy state space and convenient intensity-based measurements. This study opens new avenues for vortex beam communications, laser mode analysis, microscopy, Bose-Einstein condensates characterization, etc.
Optical encryption inherently provides strong security advantages, with hybrid optoelectronic systems offering additional degrees of freedom by integrating optical and algorithmic domains. However, existing optical encryption schemes heavily rely on electronic computation, limiting overall efficiency, while the physical keys are susceptible to damage, compromising both security and system stability. To overcome these challenges, we introduce the Quasi Periodic Optical Key (QPOK), which combines long range order with short range disorder, enabling enhanced security and robustness against damage within a single platform. By leveraging diffraction symmetry, our design enables optics-driven encryption, effectively shifting the optoelectronic balance toward photonic processing. Moreover, we innovatively apply deep learning to reconstruct the complex optical ciphertext field using only amplitude data and cryptographic keys, simultaneously achieving data compression and improved security. Within this framework, the key space includes continuously tunable parameters such as wavelength, propagation distance, phase modulation, and Q-POK geometry, significantly expanding cryptographic diversity. Our system also demonstrates robust cryptographic reliability by reducing inter-class distances by over 50% and tolerating up to 20% ciphertext loss. Our framework represents a new generation of physically grounded, algorithmically enhanced optical cryptosystems, laying a foundational pathway for scalable, hardware-integrated information security paradigms.
Optical encryption inherently provides strong security advantages, with hybrid optoelectronic systems offering additional degrees of freedom by integrating optical and algorithmic domains. However, existing optical encryption schemes heavily rely on electronic computation, limiting overall efficiency, and the physical keys are susceptible to damage, compromising both security and system stability. To overcome these challenges, we introduce the quasi-periodic optical key (Q-POK), which combines long-range order with short-range disorder, enabling enhanced security and robustness against damage within a single platform. By leveraging diffraction symmetry, our design enables optics-driven encryption, effectively shifting the optoelectronic balance toward photonic processing. Moreover, we innovatively apply deep learning to reconstruct the complex optical ciphertext field using only amplitude data and cryptographic keys, simultaneously achieving data compression and improved security. Within this framework, the key space includes continuously tunable parameters such as wavelength, propagation distance, phase modulation, and Q-POK geometry, significantly expanding cryptographic diversity. Our system also demonstrates robust cryptographic reliability by reducing inter-class distances by over 50% and tolerating up to 20% ciphertext loss. Our framework represents a generation of physically grounded, algorithmically enhanced optical cryptosystems—laying a foundational pathway for scalable, hardware-integrated information security paradigms.