Search Results (1,622)

Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss–DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin–quantum-dot noise modeling. Full article

Optical sensing technologies are revolutionizing global food safety surveillance through exceptional sensitivity, rapid response, and high portability. This review systematically evaluates five major platforms, revealing unprecedented detection capabilities from sub-picomolar to single-cell resolution. Surface plasmon resonance achieves $0.021 \mathrm{ng}/\mathrm{mL}$ detection limits for veterinary drugs with superior molecular recognition. Quantum dot fluorescence sensors reach $0.17 \mathrm{nM}$ sensitivity for pesticides, enabling rapid on-site screening. Surface-enhanced Raman scattering attains $0.2 \mathrm{pM}$ sensitivity for heavy metals, ideal for trace contaminants. Laser-induced breakdown spectroscopy delivers multi-elemental analysis within seconds at $0.0011 \mathrm{mg}/\mathrm{L}$ detection limits. Colorimetric assays provide cost-effective preliminary screening in resource-limited settings. We propose a stratified detection framework that strategically allocates differentiated sensing technologies across food supply chain nodes, addressing heterogeneous demands while eliminating resource inefficiencies from deploying high-precision instruments for routine screening. Integration of microfluidics, artificial intelligence, and mobile platforms accelerates evolution toward multimodal fusion and decentralized deployment. Despite advances, critical challenges persist: matrix interference, environmental robustness, and standardized protocols. Future breakthroughs require interdisciplinary innovation in materials science, intelligent data processing, and system integration, transforming laboratory prototypes into intelligent early warning networks spanning the entire food supply chain. Full article

We present a comprehensive quantum field theoretical analysis of graviton self-energy and mass generation in 3+1 dimensional BTZ black hole spacetime, incorporating axion interactions within the framework of dark matter theory. Using a novel mathematical approach based on ultrahyperfunctions, generalizations of Schwartz tempered distributions to the complex plane, we derive exact quantum relativistic expressions for graviton and axion self-energies without requiring ad hoc regularization procedures. Our approach extends the Gupta–Feynman quantization framework to BTZ gravity while introducing a new constraint that eliminates unitarity violations inherent in previous formulations, thereby avoiding the need for ghost fields. Through systematic application of generalized Feynman parameters, we evaluate both bradyonic and tachyonic graviton modes, revealing distinct quantum correction patterns that depend critically on momentum, energy, and mass parameters. Key findings include (1) natural graviton mass generation through cosmological constant interactions, yielding $m^2=2\left|\Lambda \right|/\kappa (1-\kappa )$; (2) qualitatively different quantum behaviors between bradyonic and tachyonic modes, with bradyonic corrections reaching amplitudes 6 times larger than their tachyonic counterparts; (3) the discovery of momentum-dependent quantum dissipation effects that provide natural ultraviolet regulation; and (4) the first explicit analytical expressions and graphical representations for 17 distinct graviton self-energy contributions. The ultrahyperfunction formalism proves essential for handling the non-renormalizable nature of the theory, providing mathematically rigorous treatment of highly singular integrals while maintaining Lorentz invariance. Our results suggest observable consequences in gravitational wave propagation through frequency-dependent dispersive effects and modifications to black hole thermodynamics, potentially bridging theoretical quantum gravity with experimental constraints. Full article

Fruit sunburn is a major abiotic stress limiting apple production worldwide, with losses potentially reaching 50% due to climate change-driven heat events. This study aimed to evaluate sustainable strategies to mitigate or reduce sunburn on ‘Gala Galaxy Selecta’ apple trees. Field trials conducted in summer 2021 compared eight treatments: silicon-based application (Eckosil^®), foliar fertilization with algae extracts, macro- and micronutrients, and amino acids, increased irrigation (+35% ET_c), mineral particle films (Surround^®, Vegepron Sun^®, Agrowhite^®, Sunstop^®), and an untreated control. Randomized block designs with replicates were used. Agronomic parameters, including particle film coverage, trunk cross-sectional area, yield, and fruit quality (color, sunburn incidence, firmness, soluble solids content, dry matter, starch), were measured at harvest. Physiological responses, such as net photosynthesis, maximum quantum yield of Photosystem II, specific leaf area, fruit surface temperature, photoprotective pigments, antioxidants, and heat shock protein gene expression, were also assessed. Foliar fertilization, Agrowhite^®, and water reinforcement produced the highest yield per trunk cross-sectional area, with increased soluble solids content and enhanced red pigmentation. Surround^® minimized sunburn incidence but reduced photosynthetic activity, as did Vegepron Sun^®. Agrowhite^® balanced sunburn protection with maintenance of fruit quality and physiological function. These findings provide practical guidance for growers to select effective treatments, balancing sunburn mitigation, fruit quality, and tree physiological performance, while offering researchers insights into integrating agronomic and physiological strategies for climate-resilient apple production. Full article

Cesium halide perovskite nanocrystals (PNCs) have emerged as promising materials for application in high-color-purity displays due to their exceptional optoelectronic properties, which include narrow emission linewidths, tunable bandgaps, and high photoluminescence quantum yields (PLQYs). However, preserving these characteristics during purification remains a major challenge as surface ligand detachment during the washing process can lead to increased defect states, reduced quantum efficiency, and spectral broadening. The choice of anti-solvent plays a crucial role in maintaining the structural and optical integrity of PNCs, as it directly influences ligand retention and material stability. In this study, we propose an optimized purification strategy for mixed-halide perovskite nanocrystals that incorporates post-synthetic ligand supplementation, in which controlled amounts of oleic acid (OA) and oleylamine (OAm) are sequentially introduced into the crude solution prior to anti-solvent treatment. This approach reinforces surface passivation, suppresses trap state formation, and minimizes halide loss. Consequently, a near-unity PLQY with narrow full-width-at-half-maximum emissions is achieved for both green- and red-emissive nanocrystals, markedly enhancing color purity and providing a promising route toward next-generation wide-color-gamut display technologies. Full article

Autonomous navigation in GPS-denied, unstructured environments such as murky waters or complex seabeds remains a formidable challenge for robotic systems, primarily due to sensory degradation and the computational inefficiency of conventional algorithms. Drawing inspiration from the robust navigation strategies of marine species such as the sea turtle’s quantum-assisted magnetoreception, the octopus’s tactile-chemotactic integration, and the jellyfish’s energy-efficient flow sensing this study introduces a novel neuromorphic framework for resilient robotic navigation, fundamentally based on the co-design of marine-inspired sensors and event-based neuromorphic processors. Current systems lack the dynamic, context-aware multisensory fusion observed in these animals, leading to heightened susceptibility to sensor failures and environmental perturbations, as well as high power consumption. This work directly bridges this gap. Our primary contribution is a hybrid sensor fusion model that co-designs advanced sensing replicating the distributed neural processing of cephalopods and the quantum coherence mechanisms of migratory marine fauna with a neuromorphic processing backbone. Enabling real-time, energy-efficient path integration and cognitive mapping without reliance on traditional methods. This proposed framework has the potential to significantly enhance navigational robustness by overcoming the limitations of state-of-the-art solutions. The findings suggest the potential of marine bio-inspired design for advancing autonomous systems in critical applications such as deep-sea exploration, environmental monitoring, and underwater infrastructure inspection. Full article

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based simulated environments demonstrated that when benchmarked against the classical MARL baseline, QI-MARL achieved an 8% improvement in path efficiency, a 12% increase in task success rate, and a 15% reduction in energy consumption. When compared with the rule-based approach, improvements reached 15%, 20%, and 26%, respectively. Ablation studies further confirmed the substantial contributions of the quantum-inspired exploration and energy-sharing mechanisms, while sensitivity and scalability analyses validated the system’s robustness across varying swarm sizes and environmental complexities. The proposed framework effectively integrates quantum-inspired AI, intelligent microgrid management, and autonomous robotics, offering a novel approach to energy coordination in cyber-physical systems. Potential applications include smart buildings, industrial campuses, and distributed renewable energy networks, where the system enables flexible, resilient, and energy-efficient robotic operations within modern electrical engineering contexts. Full article

Quantum-dot light-emitting diodes (QLEDs) have emerged as promising candidates for next-generation display technologies owing to their high color purity and external quantum efficiency. Despite rapid advancements in device performance, operational stability and long-term reliability remain critical challenges, particularly for cadmium-free and blue-emitting QLEDs. This review provides a comprehensive overview of the degradation mechanisms of QLEDs, emphasizing the relationship between environmental factors, such as moisture, oxygen, and thermal stress, and excitonic factors, including charge-injection imbalance, Auger recombination, and interface deterioration. We further highlight the role of nondestructive characterization techniques, including impedance spectroscopy, Fourier transform infrared spectroscopy, transient photoluminescence, transient electroluminescence, transient absorption, and electroabsorption spectroscopy, in probing real-time charge dynamics and material degradation. By integrating the insights from these operando analyses, this review offers a detailed perspective on the origins of device degradation and provides guidance for rational design strategies aimed at enhancing the operational stability and commercialization potential of QLEDs. Full article

The development of compact, energy-efficient integrated lasers operating at 1.3 µm re-mains a critical focus in silicon photonics, essential for advancing data communications and optical interconnect technologies. This paper presents a numerical study of distributed Bragg reflector (DBR) hybrid III-V-on-silicon lasers, analyzing design trade-offs and optimization strategies based on supermode theory. The III-V section of the design incorporates InAs/(Al)GaAs quantum dots (QDs), which offer improved temperature insensitivity at the cost of more complex III-V/Si optical coupling, due to the high refractive index of (Al)GaAs. Consequently, many current laser designs rely on silicon waveguides with a thickness exceeding 220 nm, which helps coupling but limits their compatibility with standard CMOS technologies. To address this challenge, we perform detailed simulations focusing on 220-nm-thick silicon waveguides. We first examine how the mode profiles jointly depend on the silicon waveguide dimensions and the geometry and composition of the III-V stack. Based on this analysis, we propose a novel epitaxial design that enables effective III-V/Si coupling, with the optical mode strongly confined within the III-V waveguide in the gain section and efficiently transferred to the silicon waveguide in the passive sections. Moreover, the final design is shown to be robust to fabrication-induced deviations from nominal parameters. Full article

Quantum dots (QDs) have emerged as promising nanomaterials in cancer therapeutics owing to their tunable optical properties, versatile surface functionalization, and potential for simultaneous imaging and drug delivery. This review focuses on targeted quantum dots (TQDs), highlighting their role in overcoming the limitations of passive drug delivery strategies, such as poor specificity, high systemic toxicity, and limited therapeutic efficacy. We begin by outlining the fundamentals of QDs, including their types, heterostructures, and biomedical formulations. Recent advances in tailoring QD physicochemical properties to the cancer microenvironment are discussed, with emphasis on routes of administration and targeting strategies. The review critically examines different molecular targeting approaches—such as folate receptors, transferrin receptors, aptamers, antibodies, peptides, and hyaluronic acid—used to enhance therapeutic precision. Furthermore, we summarize progress in TQD-based combination therapies, including chemotherapy–photodynamic therapy, photothermal therapy, radiotherapy, and multimodal platforms that integrate therapy with imaging. Special attention is given to the role of QDs in theranostic, hydrogels, nanocomposites, and hybrid systems that enable controlled drug release and real-time monitoring. Despite significant advancements, challenges remain regarding biocompatibility, safety, and regulatory approval. Overall, this review provides an integrative perspective on the design, functionalization, and biomedical applications of TQDs, underscoring their potential to improve cancer treatment outcomes through enhanced specificity, reduced side effects, and multifunctional theranostic capabilities. Highlight of novelty: This review uniquely emphasizes the latest advances in targeted quantum dots (TQDs), particularly in surface functionalization, hybrid nanostructures, biodistribution, and multimodal theranostic applications, providing an updated perspective that extends beyond conventional QD-based cancer therapies. Full article

Industrial Internet of Things (IIoT) systems face severe security and trust challenges, particularly under cross-domain data sharing and federated orchestration. We present QuantumTrust-FedChain, a cyber-resilient federated learning framework integrating quantum variational trust modeling, blockchain-backed provenance, and Byzantine-robust aggregation for secure IIoT collaboration in 6G networks. The architecture includes a Quantum Graph Attention Network (Q-GAT) for modeling device trust evolution using encrypted device logs. This consensus-aware federated optimizer penalizes adversarial gradients using stochastic contract enforcement, and a shard-based blockchain for real-time forensic traceability. Using datasets from SWaT and TON IoT, experiments show 98.3% accuracy in anomaly detection, 35% improvement in defense against model poisoning, and full ledger traceability with under 8.5% blockchain overhead. This framework offers a robust and explainable solution for secure AI deployment in safety-critical IIoT environments. Full article

Metal-organic frameworks (MOFs), a class of crystalline porous materials featuring a high specific surface area, tunable pore structures, and functional surfaces, exhibit remarkable potential in fluorescent sensing. This review systematically summarizes recent advances in the construction strategies, sensing mechanisms, and applications of MOF-based fluorescent sensors. It begins by highlighting the diverse degradation pathways that MOFs encounter in practical applications, including hydrolysis, acid/base attack, ligand displacement by coordinating anions, photodegradation, redox processes, and biofouling, followed by a detailed discussion of corresponding stabilization strategies. Subsequently, the review elaborates on the construction of sensors based on individual MOFs and their composites with metal nanomaterials, MOF-on-MOF heterostructures, covalent organic frameworks (COFs), quantum dots (QDs), and fluorescent dyes, emphasizing the synergistic effects of composite structures in enhancing sensor performance. Furthermore, key sensing mechanisms such as fluorescence quenching, fluorescence enhancement, Stokes shift, and multi-mechanism coupling are thoroughly examined, with examples provided of their application in detecting biological analytes, environmental pollutants, and food contaminants. Finally, future directions for MOF-based fluorescent sensors in food safety, environmental monitoring, and clinical diagnostics are outlined, pointing to the development of high-performance, low-cost MOFs; the integration of multi-technology platforms; and the construction of intelligent sensing systems as key to enabling their practical deployment and commercialization. Full article

This article investigates the exact meaning of a quantum derivative result and the corresponding definition of a quantum definite integral in multiplicative calculus from a geometrical viewpoint. After this critical analysis, we give an accurate definition of the $\mathsf{q}$-multiplicative definite integral and the corresponding derivative result. Additionally, an example pertaining to $\mathsf{q}$-multiplicative definite integrals is presented, and rigorous analysis to prove several fundamental results is provided. In addition, two other concepts are defined: the left $\mathsf{q}$-multiplicative derivative and definite integral and the right $\mathsf{q}$-multiplicative derivative and definite integral. Finally, several $\mathsf{q}$-multiplicative Hermite–Hadamard-type inequalities are constructed, and related examples are shown to support our recent findings. Full article

This study presents a novel ratiometric fluorescent sensor based on Förster resonance energy transfer (FRET) between glutathione (GSH)-coated CdTe quantum dots (CdTe/GSH QDs) and bovine serum albumin (BSA)-coated Au nanoclusters (AuNCs/BSA) for dopamine (DA) detection. The nanoparticles were characterized using transmission electron microscopy (TEM), zeta potential measurements, Fourier transform infrared (FTIR) spectroscopy, UV-Vis absorption and fluorescence spectroscopy. Key FRET parameters, including energy transfer efficiency (E), donor–acceptor distance (r), Förster distance (R₀), and the overlap integral (J), were determined. The interactions between the CdTe/GSH-AuNCs/BSA conjugate and DA were investigated, revealing a dual mechanism of QDs fluorescence quenching that involves both energy and electron transfer. The average lifetime values and spectral profiles of CdTe/GSH QDs, both in the absence and presence of DA, suggest a dynamic fluorescence quenching process. The variation in the ratiometric signal with increasing DA concentration demonstrated a linear response within the range of 0–250 µM, with a correlation coefficient of 0.9963 and a detection limit of 6.9 nM. This proposed nanosensor exhibited selectivity against potential interfering substances, including urea, glucose, BSA, GSH, citric acid, and metal ions such as Na⁺ and Ca²⁺. The conjugate also demonstrates excellent cytocompatibility and enhances cell proliferation in HeLa epithelial cells, making it suitable for biological applications. It was successfully employed for DA detection in urine samples, achieving recoveries ranging from 99.1% to 104.2%. The sensor is highly sensitive, selective, rapid, and cost-effective, representing a promising alternative for DA detection across various sample types. Full article

In response to China’s “Dual Carbon” goals, this paper proposes a Transmission Network Expansion Planning (TNEP) model that explicitly incorporates the operational flexibility of Virtual Power Plants (VPPs). Unlike conventional approaches that focus mainly on transmission investment, the proposed method accounts for the aggregated dispatchable capability of VPPs, providing a more accurate representation of distributed resources. The VPP aggregation model is characterized by the inclusion of electric vehicles, which act not only as load-side demand but also as flexible energy storage units through vehicle-to-grid interaction. By coordinating EV charging/discharging with photovoltaics, wind generation, and other distributed resources, the VPP significantly enhances system flexibility and provides essential support for grid operation. The vertex search method is employed to delineate the boundary of the VPP’s dispatchable feasible region, from which an equivalent model is established to capture its charging, discharging, and energy storage characteristics. This model is then integrated into the TNEP framework, which minimizes the comprehensive cost, including annualized line investment and the operational costs of both the VPP and the power grid. The resulting non-convex optimization problem is solved using the Quantum Particle Swarm Optimization (QPSO) algorithm. A case study based on the Garver-6 bus and Garver-18 bus systems demonstrates the effectiveness of the approach. The results show that, compared with traditional planning methods, strategically located VPPs can save up to 6.65% in investment costs. This VPP-integrated TNEP scheme enhances system flexibility, improves economic efficiency, and strengthens operational security by smoothing load profiles and optimizing power flows, thereby offering a more reliable and sustainable planning solution. Full article