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The emergence of quantum computing poses significant threats to conventional public-key cryptography, driving the urgent need for quantum-resistant cryptographic solutions. While quantum key distribution addresses secure key exchange, its dependency on symmetric keys and point-to-point limitations present scalability constraints. Quantum Asymmetric Encryption (QAE) offers a promising alternative by leveraging quantum mechanical principles for security. This paper presents the first practical implementation of a QAE protocol on IBM Quantum devices, building upon the theoretical framework originally proposed by Yoon et al. We develop a generalized Grover Search Algorithm (GSA) framework that supports non-standard initial quantum states through novel diffusion operator designs, extending its applicability beyond idealized conditions. The complete QAE protocol, including key generation, encryption, and decryption stages, is translated into executable quantum circuits and evaluated on both IBM Quantum simulators and real quantum hardware. Experimental results demonstrate significant scalability challenges, with success probabilities deteriorating considerably for larger systems. The 2-qubit implementation achieves near-perfect accuracy (100% on the simulator, and 93.88% on the hardware), while performance degrades to 78.15% (simulator) and 45.84% (hardware) for 3 qubits, and declines critically to 48.08% (simulator) and 7.63% (hardware) for 4 qubits. This degradation is primarily attributed to noise and decoherence effects in current Noisy Intermediate-Scale Quantum (NISQ) devices, highlighting the limitations of single-iteration GSA approaches. Our findings underscore the critical need for enhanced hardware fidelity and algorithmic optimization to advance the practical viability of quantum cryptographic systems, providing valuable insights for bridging the gap between theoretical quantum cryptography and real-world implementations. Full article

The degradation of the electronic properties of semiconductor materials and electronic devices under neutron irradiation is a critical issue for the development of electronic systems intended for use in nuclear and thermonuclear energy facilities. This study presents a methodology for real-time measurement of the electrical parameters of semiconductor structures during neutron irradiation in a high-flux reactor environment. A specially designed irradiation fixture with an electrical measurement system was developed and implemented at the WWR-K research reactor. The system enables simultaneous measurement of electrical conductivity and the Hall effect, with automatic temperature control and remote data acquisition. The sealed fixture, equipped with radiation-resistant wiring and a temperature control, allows for continuous measurement of remote material properties at neutron fluences exceeding 10¹⁸ cm⁻², eliminating the limitations associated with post-irradiation handling of radioactive samples. The technique was successfully applied to the two different InGaAs-based heterostructures, revealing distinct mechanisms of radiation-induced modification: degradation of mobility and carrier concentration in the InGaAs quantum well structure on GaAs substrate, and transmutation-induced doping effects in the heterostructure on InP substrate. The developed methodology provides a reliable platform for evaluating radiation resistance and optimizing materials for magnetic sensors and electronic components designed for high-radiation environments. Full article

Employing commercial off-the-shelf coherent optical transmission components and methods to design a continuous variable quantum key distribution (CVQKD) system is a promising trend of achieving QKD with high security key rate (SKR) and cost-effectiveness. In this paper, we explore a CVQKD system based on the widely used polarization division multiplexed (PDM) coherent optical transmission structure and pilot-aided digital signal processing methods. A simplified pilot-aided phase noise compensation scheme based on frequency division multiplexing (FDM) is proposed, which introduces less total excess noise than classical pilot-aided schemes based on time division multiplexing (TDM). In addition, the two schemes of training symbol (TS)-aided equalization are compared to find the optimal strategy for TS insertion, where the scheme based on block insertion strategy can provide the SKR gain of around 29%, 22%, and 15% compared with the scheme based on fine-grained insertion strategy at the transmission distance of 5 km, 25 km, and 50 km, respectively. The joint optimization of pilot-aided and TS-aided methods in this work can provide a reference for achieving a CVQKD system with a high SKR and low complexity in metropolitan-scale applications. Full article

We propose a general and experimentally accessible framework to quantify transition timing in discrete quantum systems via the time-of-flow (TF) distribution. Defined from the rate of population change in a target state, the TF distribution can be reconstructed through repeated projective measurements at discrete times on independently prepared systems, thus avoiding Zeno inhibition. In monotonic regimes, it admits a clear interpretation as a time-of-arrival (TOA) or time-of-departure (TOD) distribution. We apply this approach to optimize time-dependent Hamiltonians, analyze shortcut-to-adiabaticity (STA) protocols, study non-adiabatic features in the dynamics of a three-level time-dependent detuning model, and derive a transition-based quantum speed limit (TF-QSL) for both closed and open quantum systems. We also establish a lower bound on temporal uncertainty and examine decoherence effects, demonstrating the versatility of the TF framework for quantum control and diagnostics. This method provides both a conceptual tool and an experimental protocol for probing and engineering quantum dynamics in discrete-state platforms. Full article

The basic Salp Swarm Algorithm (SSA) offers advantages such as a simple structure and few parameters. However, it is prone to falling into local optima and remains inadequate for seed classification tasks that involve hyperparameter optimization of machine learning classifiers such as Support Vector Machines (SVMs). To overcome these limitations, an Enhanced Knowledge-based Salp Swarm Algorithm (EKSSA) is proposed. The EKSSA incorporates three key strategies: Adaptive adjustment mechanisms for parameters $c_1$ and $\alpha$ to better balance exploration and exploitation within the salp population; a Gaussian walk-based position update strategy after the initial update phase, enhancing the global search ability of individuals; and a dynamic mirror learning strategy that expands the search domain through solution mirroring, thereby strengthening local search capability. The proposed algorithm was evaluated on thirty-two CEC benchmark functions, where it demonstrated superior performance compared to eight state-of-the-art algorithms, including Randomized Particle Swarm Optimizer (RPSO), Grey Wolf Optimizer (GWO), Archimedes Optimization Algorithm (AOA), Hybrid Particle Swarm Butterfly Algorithm (HPSBA), Aquila Optimizer (AO), Honey Badger Algorithm (HBA), Salp Swarm Algorithm (SSA), and Sine–Cosine Quantum Salp Swarm Algorithm (SCQSSA). Furthermore, an EKSSA-SVM hybrid classifier was developed for seed classification, achieving higher classification accuracy. Full article

Wireless sensor networks (WSNs) play a major role in various applications, but the main challenge is to maintain security and balanced energy efficiency. Classical routing protocols struggle to achieve both energy efficiency and security because they are more vulnerable to security risks and resource limitations. This paper introduces QSEER, a novel approach that uses quantum technologies to overcome these limitations. QSEER employs quantum-inspired optimization algorithms that leverage superposition and entanglement principles to efficiently explore multiple routing possibilities, thereby identifying energy-efficient paths and reducing redundant transmissions. The proposed protocol enhances the security of data transmission against eavesdropping and tampering by using the principles of quantum mechanics, thus mitigating potential security vulnerabilities. Through extensive simulations, we demonstrated the effectiveness of QSEER in achieving both security and energy efficiency objectives, which achieves 15.1% lower energy consumption compared to state-of-the-art protocols while maintaining 99.8% data integrity under various attack scenarios, extending network lifetime by an average of 42%. These results position QSEER as a significant advancement for next-generation WSN deployments in critical applications such as environmental monitoring, smart infrastructure, and healthcare systems. Full article

Motivated by the strategy developed by Hicks and Dresselhaus in a quantum wire corresponding to a single-chain model with $k^2$-dispersion, we study a one-dimensional double-chain model with two carriers of electrons and holes, characterized by $k^4$-dispersion. To understand the role of the enhancement of the density of state derived from $k^4$-dispersion, we calculate an optimized dimensionless thermoelectric figure of merit ($ZT$) depending on the side length of the cross section, a, in the same way as discussed by Hicks and Dresselhaus. We find that $ZT$ enhances as a decreases similarly to the results obtained in the single-chain model, while the enhancement of $ZT$ is smaller than that of single-chain model. We discuss the reason in connection with the difference of electronic state between the single- and double-chain models. Full article

A continuous-variable quantum key distribution (CVQKD) can be realized over the seawater channel, but the transmission of quantum signals in seawater media exhibits significant attenuation effects. Therefore, we propose an N-symbol amplitude and phase shift keying (N-APSK) modulation scheme to enhance the transmission performance of the CVQKD over the seawater channel. Specifically, an optimal N-APSK modulation scheme is designed based on the principle of maximizing the minimum Euclidean distance (MED). The simulation results show that the CVQKD protocol based on N-APSK modulation achieves a longer transmission distance over the seawater channel compared to the Gaussian modulation protocol. Additionally, increasing the value of N simultaneously expands the number of rings in the constellation diagram, further enhancing the communication distance. This study transfers modulation methods from the field of classical communications to the field of quantum communications, achieving a substantial improvement in communication distance and thereby promoting the integration of quantum communications and classical communications. Full article

Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to salt-induced corrosion, storm surges, and wind damage. These challenges call for monitoring solutions that are not only accurate but also scalable and privacy-preserving. To address this need, Q-MobiGraphNet, a quantum-inspired multimodal classification framework, is proposed for federated coastal vulnerability analysis and solar infrastructure assessment. The framework integrates IoT sensor telemetry, UAV imagery, and geospatial metadata through a Multimodal Feature Harmonization Suite (MFHS), which reduces heterogeneity and ensures consistency across diverse data sources. A quantum sinusoidal encoding layer enriches feature representations, while lightweight MobileNet-based convolution and graph convolutional reasoning capture both local patterns and structural dependencies. For interpretability, the Q-SHAPE module extends Shapley value analysis with quantum-weighted sampling, and a Hybrid Jellyfish–Sailfish Optimization (HJFSO) strategy enables efficient hyperparameter tuning in federated environments. Extensive experiments on datasets from Norwegian coastal solar farms show that Q-MobiGraphNet achieves 98.6% accuracy, and 97.2% F1-score, and 90.8% Prediction Agreement Consistency (PAC), outperforming state-of-the-art multimodal fusion models. With only 16.2 M parameters and an inference time of 46 ms, the framework is lightweight enough for real-time deployment. By combining accuracy, interpretability, and fairness across distributed clients, Q-MobiGraphNet offers actionable insights to enhance the resilience of coastal renewable energy systems. Full article

The advent of graphene has catalyzed extensive exploration into two-dimensional (2D) materials, among which gallium selenide (GaSe)—a layered semiconductor—stands out for its promise in optoelectronic and nanoscale device applications. To elucidate the intricate correlation between structure and electronic properties, and to enable performance optimization at the atomic scale, we employ advanced characterization methodologies. In this work, atomic-resolution Scanning Transmission Electron Microscopy (STEM) and Electron Energy Loss Spectroscopy (EELS) are utilized to investigate the structural and electronic characteristics of GaSe. STEM imaging confirms the atomic-level uniformity and verifies the β-GaSe phase, while EELS measurements reveal a thickness-dependent, tunable bandgap that decreases from 3.8 eV to 2.4 eV as the crystal thickness increases from approximately 1 nm to 30 nm—a trend attributable to quantum confinement effects. Full article

Quantum computers are emerging as a major tool in the computation field, leveraging the principles of quantum mechanics to solve specific problems currently beyond the capability of classical computers. This technology holds significant promise in edge-main cloud deployments, where it can enable low-latency data processing and secure communication. This paper aims to establish a research foundation by integrating quantum computing with classical edge-cloud environments to promote performance across a range of applications that scientists are actively investigating. However, the successful deployment of hybrid quantum–classical edge-clouds requires a comprehensive evaluation framework to ensure their alignment with the performance requirements. This study first proposes a novel quantum benchmarking framework, including two distinct methods to evaluate latency scores based on the quantum transpilation levels across different quantum-edge-cloud platforms. The framework is then validated for the edge-cloud environment by benchmarking several well-known and useful quantum algorithms potentially useful in this domain, including Shor’s, Grover’s, and the Quantum Walks algorithm. An optimal transpilation level is eventually suggested to achieve maximum performance in quantum-edge-cloud environments. In summary, this research paper provides critical insights into the current and prospective capabilities of QPU integration, offering a novel benchmarking framework and providing a comprehensive assessment of their potential to enhance edge-cloud performance under varying parameters, including fidelity and transpilation levels. Full article

Sunset yellow (SY) is a synthetic azo dye widely used in food and cosmetics. However, concerns have been raised about its potential health risks, including its nephrotoxicity and genotoxicity, when used in excessive amounts. Illegal addition of SY may cause allergic reactions or genetic damage. Therefore, a rapid method for detecting SY is needed. To develop a rapid detection method for sunset yellow (SY) with the aim of preventing its illegal addition in food, this study utilized agricultural waste asparagus peel (AP) as a carbon source and synthesized amino-functionalized carbon quantum dots (AP-CDs) via a green hydrothermal method. A highly sensitive detection platform was established based on the fluorescence quenching mechanism of AP-CDs in the presence of SY. The microstructure of AP-CDs was characterized using transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). Their optical properties were assessed via ultraviolet–visible absorption spectroscopy (UV-vis) and fluorescence spectroscopy (FS). Furthermore, key experimental parameters affecting SY detection were systematically optimized. Results revealed that the synthesized AP-CDs possessed surface hydrophilic functional groups, including hydroxyl, amide, and carboxyl groups, and were composed of carbon (C), oxygen (O), and nitrogen (N) elements. Optical performance studies demonstrated that AP-CDs exhibited a strong fluorescence emission at 470 nm under 380 nm excitation, with a quantum yield (Φ) of 15.9%. Under the optimized conditions (pH 7.0, 0.5 mg/mL AP-CDs), the fluorescence intensity showed a linear response to the concentration of SY over the range of 0.1 to 100 μM (R² = 0.9929), achieving a detection limit of 0.92 μM. This strategy not only enables sustainable resource utilization but also provides a sensitive and practical approach for food safety monitoring, demonstrating significant potential for real-world applications. Full article

Measurement uncertainty limits how precisely information can be extracted from quantum systems due to inherent quantum indeterminacy. On the other hand, dense coding capacity quantifies the amount of classical information that can be sent using shared entanglement, thereby enhancing communication efficiency beyond classical limits. In this paper, we investigate these two concepts for a spin-star network under an external magnetic field under a thermal regime, considering both homogeneous and inhomogeneous models. We reveal that under certain conditions, dense coding capacity not only becomes valid but is also optimized, implying that measurement uncertainty is significantly suppressed. Furthermore, we analyze the local quantum uncertainty of the thermal state under the influence of decoherence channels to assess the effectiveness of the approach studied. Full article

Colloidal quantum dots (CQDs) have attracted significant attention in optoelectronics due to their size-tunable bandgap, high photoluminescence quantum yield, and solution processability, which enable integration into compact and energy-efficient systems. This review consolidates recent progress in multifunctional CQD-based light-emitting devices and on-chip integration strategies. This review systematically examines fundamental CQD properties (quantum confinement, carrier dynamics, and core–shell heterostructures), key synthesis methods including hot injection, ligand-assisted reprecipitation, and microfluidic flow synthesis, and device innovations such as light-emitting field-effect transistors, light-emitting solar cells, and light-emitting memristors, alongside on-chip components including ongoing electrically pumped lasers and photodetectors. This review concludes that synergies in material engineering, device design, and system innovation are pivotal for next-generation optoelectronics, though challenges such as environmental instability, Auger recombination, and CMOS compatibility require future breakthroughs in atomic-layer deposition, 3D heterostructures, and data-driven optimization. Full article

This study explores the thermochemical properties and formation mechanisms of reactive oxygen species (ROS) relevant to photocatalytic processes, aiming to clarify their molecular characteristics and reaction dynamics. The research focuses on key ROS, including the superoxide anion radical (^•O₂⁻), hydrogen peroxide (H₂O₂), singlet oxygen (¹O₂), and hydroxyl radical (^•OH), employing Møller–Plesset second-order perturbation theory (MP2)-level quantum chemical calculations. Solvent effects were modeled using water to simulate conditions commonly found in photocatalytic environments. The computed energetic profiles and stabilities of the ROS offer insights into their relative reactivities and possible interconversion pathways. These findings enhance the understanding of how ROS behave under photocatalytic conditions, with implications for their role in degradation mechanisms and redox cycles. Overall, the results support the development and optimization of photocatalytic technologies for environmental applications, including pollutant degradation and disinfection of water and air. Full article