Search Results (4,552)

Quantum machine learning (QML) has emerged as a promising approach for enhancing image classification by exploiting quantum computational principles such as superposition and entanglement. However, practical applications on complex datasets like CIFAR-100 remain limited due to the low expressivity of shallow circuits and challenges in circuit optimization. In this study, we propose HQCNN–REGA—a novel hybrid quantum–classical convolutional neural network architecture that integrates data re-uploading and genetic algorithm optimization for improved performance. The data re-uploading mechanism allows classical inputs to be encoded multiple times into quantum states, enhancing the model’s capacity to learn complex visual features. In parallel, a genetic algorithm is employed to evolve the quantum circuit architecture by optimizing gate sequences, entanglement patterns, and layer configurations. This combination enables automatic discovery of efficient parameterized quantum circuits without manual tuning. Experiments on the MNIST and CIFAR-100 datasets demonstrate state-of-the-art performance for quantum models, with HQCNN–REGA outperforming existing quantum neural networks and approaching the accuracy of advanced classical architectures. In particular, we compare our model with classical convolutional baselines such as ResNet-18 to validate its effectiveness in real-world image classification tasks. Our results demonstrate the feasibility of scalable, high-performing quantum–classical systems and offer a viable path toward practical deployment of QML in computer vision applications, especially on noisy intermediate-scale quantum (NISQ) hardware. Full article

Soil salinization severely limits plant growth and productivity. Mulberry (Morus alba L.), an economically and ecologically important tree, is widely cultivated, yet its salt-tolerance mechanisms at the seedling stage remain insufficiently understood. This study investigated the physiological and biochemical responses of two-year-old mulberry (‘Tailai Sang’) seedlings subjected to six NaCl treatments (0, 50, 100, 150, 200, and 300 mmol L⁻¹) for 28 days. Results showed that growth parameters and photosynthetic gas exchange exhibited dose-dependent declines. The reduction in net photosynthetic rate (Pn) was attributed to both stomatal limitations (decreased stomatal conductance) and non-stomatal limitations, as evidenced by a significant decrease in the maximum quantum efficiency of photosystem II (Fv/Fm) under high salinity. To cope with osmotic stress, seedlings accumulated compatible solutes, including soluble sugars, proteins, and proline. Critically, mulberry seedlings demonstrated effective ion homeostasis by sequestering Na⁺ in the roots to maintain a high K⁺/Na⁺ ratio in leaves, a mechanism that was compromised above 150 mmol L⁻¹. Concurrently, indicators of oxidative stress—malondialdehyde (MDA) and H₂O₂—rose significantly with salinity, inducing the activities of antioxidant enzymes (SOD, CAT, APX, and GR), which peaked at 150 mmol L⁻¹ before declining under extreme stress. A biomass-based LC₅₀ of 179 mmol L⁻¹ NaCl was determined. These findings elucidate that mulberry salt tolerance is a coordinated process involving three key mechanisms: osmotic adjustment, selective ion distribution, and a robust antioxidant defense system. This study establishes an indicative tolerance threshold under controlled conditions and provides a physiological basis for further field-based evaluations of ‘Tailai Sang’ mulberry for cultivation on saline soils. Full article

Financial market forecasting remains challenging due to complex nonlinear dynamics and regime-dependent behaviors that traditional models struggle to capture effectively. This research introduces the Adaptive Financial Reservoir Network with Hypernetwork Flow (AFRN–HyperFlow) framework, a novel ensemble architecture integrating Echo State Networks, temporal convolutional networks, mixture density networks, adaptive Hypernetworks, and deep state-space models for enhanced financial time-series prediction. Through comprehensive feature engineering incorporating technical indicators, spectral decomposition, reservoir-based representations, and flow dynamics characteristics, the framework achieves superior forecasting performance across diverse market conditions. Experimental validation on 26,817 balanced samples demonstrates exceptional results with an F1-score of 0.8947, representing a 12.3% improvement over State-of-the-Art baseline methods, while maintaining robust performance across asset classes from equities to cryptocurrencies. The adaptive Hypernetwork mechanism enables real-time regime-change detection with 2.3 days average lag and 95% accuracy, while systematic SHAP analysis provides comprehensive interpretability essential for regulatory compliance. Ablation studies reveal Echo State Networks contribute 9.47% performance improvement, validating the architectural design. The AFRN–HyperFlow framework addresses critical limitations in uncertainty quantification, regime adaptability, and interpretability, offering promising directions for next-generation financial forecasting systems incorporating quantum computing and federated learning approaches. Full article

Aquatic biomass—ranging from fish scales and crustacean shells to various algae species—offers an abundant, renewable source for carbon dot (CD) synthesis, aligning with circular economy principles. This review highlights recent studies for valorizing aquatic biomass into high-performance carbon-based nanomaterials—specifically aquatic biomass-based carbon dots (AB-CDs)—briefly summarizing green synthesis approaches (e.g., hydrothermal carbonization, pyrolysis, and microwave-assisted treatments) that minimize environmental impact. Subsequent sections highlight the varied applications of AB-CDs, particularly in biosensing (including the detection of marine biotoxins), environmental monitoring of water pollutants, and drug delivery systems. Physically AB-CDs show unique optical and physicochemical properties—tunable fluorescence, high quantum yields, enhanced sensitivity, selectivity, and surface bio-functionalization—that make them ideal for a wide array of applications. Overall, the discussion underlines the significance of this approach; indeed, transforming aquatic biomass into carbon dots can contribute to sustainable nanotechnology, offering eco-friendly solutions in sensing, environmental monitoring, and therapeutics. Finally, current challenges and future research directions are discussed to give a perspective of the potential of AB-CDs; the final aim is their integration into multifunctional, real-time monitoring and therapeutic systems—for sustainable nanotechnology innovations. Full article

In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes some of these effects. It shows that QC offers new materials for implementing AI and innovative algorithms for solving optimisation problems and enhancing machine learning algorithms. Additionally, it demonstrates how AI algorithms can help overcome many of the experimental challenges associated with implementing QC. It also outlines several perspectives for the future development of quantum artificial intelligence. Full article

This follow-up scientific case study builds on prior research to explore the computational challenges of applying quantum algorithms to financial asset management, focusing specifically on solving the graph-cut problem for investment recommendation. Unlike our prior study, which focused on idealized QAOA performance, this work introduces a graph compression pipeline that enables QAOA deployment under real quantum hardware constraints. This study investigates quantum-accelerated spectral graph compression for financial asset recommendations, addressing scalability and regulatory constraints in portfolio management. We propose a hybrid framework combining the Quantum Approximate Optimization Algorithm (QAOA) with spectral graph theory to solve the Max-Cut problem for investor clustering. Our methodology leverages quantum simulators (cuQuantum and Cirq-GPU) to evaluate performance against classical brute-force enumeration, with graph compression techniques enabling deployment on resource-constrained quantum hardware. The results underscore that efficient graph compression is crucial for successful implementation. The framework bridges theoretical quantum advantage with practical financial use cases, though hardware limitations (qubit counts, coherence times) necessitate hybrid quantum-classical implementations. These findings advance the deployment of quantum algorithms in mission-critical financial systems, particularly for high-dimensional investor profiling under regulatory constraints. Full article

Maize–soybean intercropping is widely practised to improve land use efficiency, but shading from maize often limits soybean growth and productivity. Melatonin, a plant signaling molecule with antioxidant and growth-regulating properties, has shown potential in mitigating various abiotic stresses, including low light. This study investigated the efficacy of applying foliar melatonin (MT) to enhance shade tolerance and yield performance of soybean under intercropping. Four melatonin concentrations (0, 50, 100, and 150 µM) were applied to soybean grown under mono- and intercropping systems. The results showed that intercropping significantly reduced growth, photosynthetic activity, and yield-related traits. However, the MT application, particularly at 100 µM (MT₁₀₀), effectively mitigated these declines. MT₁₀₀ improved plant height (by up to 32%), leaf area (8%), internode length (up to 41%), grain yield (32%), and biomass dry matter (30%) compared to untreated intercropped plants. It also enhanced SPAD chlorophyll values, photosynthetic rate, stomatal conductance, chlorophyll fluorescence parameters such as Photosystem II efficiency (ɸPSII), maximum PSII quantum yield (Fv/Fm), photochemical quenching (qp), electron transport rate (ETR), Rubisco activity, and soluble protein content. These findings suggest that foliar application of melatonin, especially at 100 µM, can improve shade resilience in soybean by enhancing physiological and biochemical performance, offering a practical strategy for optimizing productivity in intercropping systems. Full article

The focal “hot spot” neuropathologies in COVID-19 infection are revealing footprints of a hidden underlying collapse of a novel ultrafast ultradian Piezo2 signaling system within the nervous system. Paradoxically, the same initiating pathophysiology may underpin the systemic findings in COVID-19 infection, namely the multiorgan SARS-CoV-2 infection-induced vascular pathologies and brain–body-wide systemic pro-inflammatory signaling, depending on the concentration and exposure to infecting SARS-CoV-2 viruses. This common initiating microdamage is suggested to be the primary damage or the acquired channelopathy of the Piezo2 ion channel, leading to a principal gateway to pathophysiology. This Piezo2 channelopathy-induced neural switch could not only explain the initiation of disrupted cell–cell interactions, metabolic failure, microglial dysfunction, mitochondrial injury, glutamatergic synapse loss, inflammation and neurological states with the central involvement of the hippocampus and the medulla, but also the initiating pathophysiology without SARS-CoV-2 viral intracellular entry into neurons as well. Therefore, the impairment of the proposed Piezo2-induced quantum mechanical free-energy-stimulated ultrafast proton-coupled tunneling seems to be the principal and critical underlying COVID-19 infection-induced primary damage along the brain axes, depending on the loci of SARS-CoV-2 viral infection and intracellular entry. Moreover, this initiating Piezo2 channelopathy may also explain resultant autonomic dysregulation involving the medulla, hippocampus and heart rate regulation, not to mention sleep disturbance with altered rapid eye movement sleep and cognitive deficit in the short term, and even as a consequence of long COVID. The current opinion piece aims to promote future angles of science and research in order to further elucidate the not entirely known initiating pathophysiology of SARS-CoV-2 infection. Full article

Optical materials are widely used in large optical systems such as lithography machines and astronomical telescopes. However, optical materials inevitably produce subsurface damage (SSD) during lapping and polishing processes, degrading the laser damage threshold and impacting the service life of the optical system. The large surface roughness of the lapped optical materials further increases the difficulty of the nondestructive detection of SSD. Quantum dots (QDs) show great development potential in the nondestructive detection of SSD in lapped materials. However, existing QD-based SSD detection methods ignore the polarization sensitivity of QDs to excitation light, which affects the detection accuracy of SSD. To address this problem, this paper explores the fluorescence polarization properties of QDs in the SSD of optical materials. First, the detection principle of SSD based on the fluorescence polarization of QDs is investigated. Subsequently, a fluorescence polarization detection system is developed to analyze the fluorescence polarization properties of QDs in SSD. Finally, the SSD is detected based on the studied polarization properties. The results show that the proposed method effectively improves the detection rate of SSD by 10.8% and thus provides guidance for evaluating the quality of optical material and optimizing optical material processing technologies. The research paradigm is equally applicable to biomedicine, energy, optoelectronics, and the environment, where QDs have a wide range of applications. Full article

Silicon-based microcavity quantum dot lasers are attractive candidates for on-chip light sources in photonic integrated circuits due to their small size, low power consumption, and compatibility with silicon photonic platforms. However, integrating components like quantum dot lasers and photodetectors on a single chip remains challenging due to material compatibility issues and mode field mismatch problems. In this work, we have demonstrated monolithic integration of an InAs quantum dot microdisk light emitter, waveguide, and photodetector on a silicon platform using a shared epitaxial structure. The photodetector successfully monitored variations in light emitter output power, experimentally proving the feasibility of this integrated scheme. This work represents a key step toward multifunctional integrated photonic systems. Future efforts will focus on enhancing the light emitter output power, improving waveguide efficiency, and scaling up the integration density for advanced applications in optical communication. Full article

In this paper, we investigate the memory effects introduced by the time-fractional Schrödinger equation proposed by Naber on quantum entanglement and quantum dense coding under amplitude damping noise. Two formulations are analyzed: one with fractional operations applied to the imaginary unit and one without. Numerical results show that the formulation without fractional operations on the imaginary unit may be more suitable for describing non-Markovian (power-law) behavior in dissipative environments. This finding provides a more physically meaningful interpretation of the memory effects in time-fractional quantum dynamics and indirectly addresses fundamental concerns regarding the violation of unitarity and probability conservation in such frameworks. Our work offers a new perspective for the application of fractional quantum mechanics to realistic open quantum systems and shows promise in supporting the theoretical modeling of decoherence and information degradation. Full article

Application of organic ligand 2-(3-ethyl-pyrazin-2-yl)quinoline-4-carboxylate with N/O donor atoms enabled solvothermal synthesis of a 2D Cu(II) coordination polymer, {Cu(L)BF₄}_n (L = deprotonated 2-(3-ethyl-pyrazin-2-yl)quinoline-4-carboxylate). Both the ligand and its coordination polymer have been characterized. The condensed ring system of the applied ligand promotes the formation of coordination polymers rather than mononuclear species. The obtained 2D coordination polymer is photoluminescent with bathochromic/hypsochromic shifts in ligand absorption bands leading to a single absorption band at 465 nm. Density Functional Theory was employed to provide a theoretical description of the possible conformational changes within the ligand, with emphasis on the difference between the ligand conformation in its hydrochloride salt and in the polymer. Two models of polymer fragments were constructed to describe the electronic structure and non-covalent interactions. The Quantum Theory of Atoms in Molecules (QTAIM) was applied for this purpose. Using the obtained results, we were able to develop potential energy profiles for various conformations of the ligand. For the set of the studied systems, we detected non-covalent interactions, which are responsible for the spatial conformation. Concerning the models of polymers, electron spin density distribution has been visualized and discussed. Full article

Introducing an additional term into the thermodynamic potential density of the quark matter system, as required for thermodynamic consistency, resolves the inconsistency that arises in the conventional perturbative quantum chromodynamics (QCD) model. In this work, we use a revised, thermodynamically consistent perturbative QCD model to compute the stability window and equation of state of up-down ($ud$) quark matter at zero temperature. Our results indicate that the measured tidal deformability for GW170817 places an upper limit on the maximum mass of $ud$ quark stars, but does not rule out the possibility of such stars with a mass of about two solar masses. However, when the maximum mass of $ud$ quark stars significantly exceeds two solar masses, such as the compact object with a mass in the range of 2.50–2.67 $M_{\odot }$ observed in the GW190814 event, it cannot be identified as a $ud$ quark star according to the revised perturbative QCD model. Full article

The increasing integration of wind and photovoltaic energy into power systems brings about large fluctuations and significant challenges for power absorption. Wind–solar–hydro–storage multi-energy complementary systems, especially joint dispatching strategies, have attracted wide attention due to their ability to coordinate the advantages of different resources and enhance both flexibility and economic efficiency. This paper develops a capacity optimization model for a wind–solar–hydro–storage multi-energy complementary system. The objectives are to improve net system income, reduce wind and solar curtailment, and mitigate intraday fluctuations. We adopt the quantum particle swarm algorithm (QPSO) for outer-layer global optimization, combined with an inner-layer stepwise simulation to maximize life cycle benefits under multi-dimensional constraints. The simulation is based on the output and load data of typical wind, solar, water, and storage in Yunnan Province, and verifies the effectiveness of the proposed model. The results show that after the wind–solar–hydro–storage multi-energy complementary system is optimized, the utilization rate of new energy and the system economy are significantly improved, which has a wide range of engineering promotion value. The research results of this paper have important reference significance for the construction of new power systems and the engineering design of multi-energy complementary projects. Full article

Fibrate pharmaceuticals (fibrates), as a widespread class of emerging contaminants, pose potential risks to both ecological systems and human health. The photocatalytic system based on nitrogen (N) and fluorine (F) co-doped TiO₂ nanotube arrays (NF-TNAs) provides a renewable solution for fibrate pharmaceutical removal from water, powered by inexhaustible sunlight. In this study, the degradation of two typical fibrates, i.e., bezafibrate (BZF) and ciprofibrate (CPF), under simulated sunlight irradiation through NF-TNAs were investigated. The photocatalytic degradation of BZF/CPF was achieved through combined radical and non-radical oxidation processes, while the generation and reaction mechanisms of associated reactive oxygen species (ROS) were examined. Electron paramagnetic resonance detection and quenching tests confirmed the existence of h⁺, •OH, O₂^•−, and ¹O₂, with O₂^•− playing the predominant role. The transformation products (TPs) of BZF/CPF were identified through high-resolution mass spectrometry analysis combined with quantum chemical calculations to elucidate the degradation pathways. The influence of co-existing ions and typical natural organic matters (NOM) on BZF/CPF degradation were also tested. Eventually, the ecological risk of BZF/CPF transformation products was assessed through quantitative structure–activity relationship (QSAR) modeling, and the results showed that the proposed photocatalytic system can largely alleviate fibrate toxicity. Full article