Stability enhancement in random fiber lasers using passive feedback and NP-doped fibers
This paper presents an experimental investigation of the spectral and power stability of a random fiber laser (RFL) based on nanoparticle (NP)-doped fiber, under three different passive optical feedback configurations: retroreflector, Faraday rotator, and fiber Bragg grating (FBG). The NP-doped fiber offers enhanced Rayleigh scattering and intrinsic gain, allowing distributed feedback and laser generation. Spectral analysis revealed that the FBG configuration presented the highest spectral stability, with a 91.25% reduction in wavelength variation compared to the retroreflector. The Faraday rotator also contributed to the spectral stability, with a 37.5% reduction. Regarding power stability, the Faraday rotator presented the best performance, with only 1.62% variation, while the FBG provided the highest output power (8.13 dBm), with a 6.1% variation. The retroreflector configuration demonstrated the worst performance in both spectral and power stability. These results confirm that the combination of NP-doped fibers and well-selected passive feedback elements allows the development of more stable and coherent RFLs, expanding their potential for application in sensing, telecommunications, and optical fiber devices.
Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
The demand for high-capacity communication systems has grown exponentially in recent decades, constituting a technological field in constant change. Data transmission at high rates, reaching tens of Gb/s, and over distances that can reach hundreds of kilometers, still faces barriers to improvement, such as distortions in the transmitted signals. Such distortions include chromatic dispersion, which causes a broadening of the transmitted pulse. Therefore, the development of solutions for the adequate recovery of such signals distorted by the complex dynamics of the transmission channel currently constitutes an open problem since, despite the existence of well-known and efficient equalization techniques, these have limitations in terms of processing time, hardware complexity, and especially energy consumption. In this scenario, this paper discusses the emergence of photonic neural networks as a promising alternative for equalizing optical communication signals. Thus, this review focuses on the applications, challenges, and opportunities of implementing integrated photonic neural networks for the scenario of optical signal equalization. The main work carried out, ongoing investigations, and possibilities for new research directions are also addressed. From this review, it can be concluded that perceptron photonic neural networks perform slightly better in equalizing signals transmitted over greater distances than reservoir computing photonic neural networks, but with signals at lower data rates. It is important to emphasize that photonics research has been growing exponentially in recent years, so it is beyond the scope of this review to address all existing applications of integrated photonic neural networks.
Integrated Photonic Enabling Massive Data Transfer: A Short Review
We have been living in a digital revolution, with the creation of new online services, the popularization of electronics, the advent of generative AI, and a growing trend of virtualization of things, allowing the creation of a digital twin version of a real object. As this revolution pushes society forward, there is a high performance demand for its infrastructure, such as telecommunication networks. There is a constant need to transmit
a massive amount of data, which is increasing with the market demand. The scientific community has been working to bring new solutions to this demand. One possibility is simplifying the edge devices, adding open issues, and reducing system latency. In this context, photonic integrated circuits can play a significant role. This work concisely reviews the current development of photonics integrated circuits (PIC) for optical systems with strict requirements and the demand for massive data transfer. As the current telecommunication scenario rapidly evolves due to market demands and the creation of new requirement-sensitive services, it is crucial to understand the role of integrated photonics in telecommunication networks as discussed in this work. Also, it is presented how open issues, such as chromatic dispersion, can be handled by PIC, leveraging the overall performance of optical telecommunication systems.
Analysis of n-th Order Fully-Optical FIR Filters for Chromatic Dispersion Compensation
This paper presents the analysis of a fully optical Finite Impulse Response filter, designed via direct realization, which enhances data transmission rates in optical communication systems. As optical fibers present issues like chromatic dispersion (CD), our method reduces reliance on complex Digital Signal Processing (DSP) techniques, thereby lowering system costs. The photonic chip aims to lessen overall system costs by using phase shifters, delay lines, and attenuators to compensate for the CD. The promise of optical filters as an effective way to improve performance in optical networks is highlighted by this study.
Network Design in Multiple Non-Overlapping Paths within Quantum Key Distribution Networks
The increasing demand for secure communication makes Quantum Key Distribution (QKD) a compelling technology, as it exploits the fundamental principles of quantum physics to enhance secure data transmission. However, practical deployment faces limitations such as limited transmission distances and dedicated hardware-channel requirements. This paper proposes a linear programming-based model that jointly addresses routing, wavelength assignment, and trusted-node minimization in QKD networks with wavelength division multiplexing (WDM), considering that the secret key rate becomes impractical beyond a given transmission distance, thus requiring the relaying of secret keys through trusted repeaters. In particular, the study contrasts scenarios where traffic is concentrated along a single route with those where key rates are split and routed over multiple nonoverlapping paths (MNOPs) using two mechanisms: Concatenate Multipart (CM) and Bitwise Product Multipath (BPM). While the literature shows that MNOPs can improve security (BPM) and improve the secret key rate (CM), we demonstrate that these mechanisms come with additional deployment costs.
Photonic Computing Towards Energy-Efficient AI Solutions Based on Photoactivated Materials
Artificial intelligence (AI) and neuromorphic computing are driving the development of hardware platforms that combine high energy efficiency with physically informed data processing. Photonic technologies offer distinct advantages, particularly when coupled with photoactivated materials that allow light-matter interactions to be directly exploited for computation. Inspired by synaptic mechanisms in biological systems, a neuromorphic photonic layer based on a luminescent material was developed to mimic synaptic memory via the material’s optical afterglow. The information-processing capability of the photonic front-end was evaluated by coupling its output responses to a digital machine learning model, achieving 76.9% test accuracy on the MNIST handwritten digit recognition task. This approach demonstrates the potential of hardware AI systems that tightly integrate material-level functionalities with neuromorphic computation by exploiting synapse-like optical memory in luminescent materials.
Probing shift-current responses in noncentrosymmetric materials using quantum algorithms
Accessing the geometric origin of shift current in noncentrosymmetric quantum materials is essential for understanding nonlinear photoresponses governed by band geometry and Berry connections. We present a quantum-algorithmic approach, implemented on a classical simulator of quantum circuits, to compute the shift current in a phase-coherent and gauge-consistent manner. By expressing the geometric ingredients of the bulk photovoltaic effect as observables accessible within a quantum-circuit framework, the method enables the evaluation of the second-order dc photocurrent without explicit gauge fixing. Using the one-dimensional Rice–Mele model as a benchmark, we obtain shift-current spectra in quantitative agreement with classical calculations and exhibiting smooth behavior across the Brillouin zone. These results position quantum algorithms as a promising framework for addressing shift-current responses and, more broadly, nonlinear optical phenomena in quantum materials.
Enabling the bulk photovoltaic effect in centrosymmetric materials through an external electric field
We develop a practical approach to electrically tuning the nonlinear photoresponse of two-dimensional semiconductors by explicitly incorporating a static out-of-plane electric field into the electronic ground state prior to optical excitation, as a gate bias. The method is implemented by dressing a Wannier-interpolated Hamiltonian with the field through its position matrix elements, which allows the gate bias to modify orbital hybridization and band dispersion beyond perturbative treatments. Within the independent-particle approximation, the resulting second-order (shift) conductivity is evaluated for both centrosymmetric and noncentrosymmetric layered systems. Applied to MoS2, the approach captures the emergence of a finite shift current in centrosymmetric bilayers and the tunability of intrinsic responses in polar structures. The shift conductivity rises linearly at small fields and saturates at higher intensities, reflecting the competition between the growing shift vector and the weakening interband coupling as resonant transitions move away from high-symmetry valleys. A Taylor expansion of the field-dressed conductivity connects this behavior to the third-order optical response, revealing a unified picture of field-induced nonlinearities. These results establish field dressing of Wannier Hamiltonians as a practical route to model and predict nonlinear photocurrents in layered materials.
Design and fabrication of 3D-printed Y-branch power splitters based on the conjugated polymer PFeF8
Conjugated polymers are essential materials employed in organic optoelectronics. Their application in photonic circuits is a subject of increasing relevance and study. In this work, we present the design, numerical simulation, fabrication, and experimental characterization of a Y-branch optical power splitter based on the PFeF8 conjugated polymer, a basic building block used in such circuits. The blend with PMMA enables the control and reduction of the refractive index and offers a large transparency region above 550 nm, which is useful for the design of waveguide structures based on materials with a low extinction coefficient. Measurements in samples of the PFeF8/PMMA blend at concentrations of 5% and 10% revealed propagation losses of 0.022 dB/mm (R2 = 0.96) and 0.025 dB/mm (R2 = 0.90), respectively. The splitters fabricated with the DLP 3D printing technique show excess loss from 4.78 dB to 6.36 dB, most of it due to scattering on the guiding structure. An average 0.16 dB power imbalance between the two arms indicates the uniformity of the splitting ratio.
Security-aware non-uniform multipath traffic partitioning for QKD networks: A MILP-based squeezing protection approach
Quantum Key Distribution (QKD) enables information-theoretic secure key exchange, but the deployment of large-scale QKD networks introduces new challenges related to routing, resource allocation, and security exposure in trusted-node-based architectures. In multipath QKD networks, the secret key rate can be partitioned and transmitted over multiple link-disjoint paths, so that an adversary must simultaneously compromise all paths to reconstruct the complete secret key. However, traffic is often equally distributed across the available disjoint paths, although such uniform partitioning may be suboptimal when candidate paths exhibit heterogeneous vulnerability levels. This paper proposes a security-aware optimization framework for QKD-enabled optical networks that determines a non-uniform multipath traffic partitioning strategy aimed at minimizing the expected amount of eavesdropped traffic. The proposed approach is formulated as a Mixed-Integer Linear Programming (MILP) model that integrates probabilistic secrecy metrics derived from trusted node compromise probabilities, a traffic vulnerability threshold that limits partial information exposure, and adaptive bandwidth squeezing protection to ensure service continuity under failure scenarios. The model jointly optimizes traffic allocation and excess key capacity, allowing the protection overhead to be dynamically determined according to security and resilience requirements. Numerical evaluations conducted on both a toy topology and the Pan-European network demonstrate that the proposed exposure-aware partitioning strategy significantly reduces the expected eavesdropped traffic compared to conventional equal-splitting approaches while maintaining resilience guarantees. These results highlight the potential of security-driven traffic engineering for the design of resilient and efficient multipath QKD networks.