Search Results (856)

Narrative extraction algorithms construct storylines by finding coherent paths through document collections. The Narrative Trails algorithm frames this as maximum-capacity path optimization, where path quality depends on a coherence function measuring document relationships. We introduce quantum kernels as coherence functions for narrative extraction—to the best of our knowledge, the first systematic characterisation of quantum kernel methods for storyline extraction—and compare them against classical baselines on two corpora using a multi-seed protocol. The sweep covers 93 method evaluations (54 quantum kernels across three encoder families—R_Y+CNOT-ring, IQP/ZZ-feature-map, and a projected quantum kernel—and 39 classical kernels—cosine, RBF, and the cluster-aware Narrative Trails baseline). On 11,215 human navigation paths from Wikispeedia, evaluation metrics divide into two clusters that disagree with each other: alignment-based metrics (length-normalised DTW and per-step DTW similarity) favour methods that produce long alignment-rich paths, while set-overlap metrics (Jaccard and F1) favour methods that produce shorter paths with higher article overlap. On LLM-judged coherence for Cuban news storylines, evaluated under a 12-method × 5-seed × 30-endpoint-pair × 2-judge design (Claude Sonnet 4.5 and GPT-4o, both at $T=0$ via structured tool calling), the cluster-aware classical baseline is the top method in terms of mean overall coherence; the 5-method quantum-kernel pool and the 7-method classical-kernel pool on matched projection input show no significant differences after Holm correction. Cross-task analysis reveals that LLM coherence rank correlates with alignment-cluster Wikispeedia metrics (Spearman $\rho \approx +0.70$) and anti-correlates with overlap-cluster metrics ($\rho \approx -0.62$). A closed-form theoretical analysis shows that the depth-1 R_Y+CNOT-ring kernel reduces to a classical product-of-cosines kernel order equivalent to RBF, explaining the absence of empirical separation at low depth; deeper encoders break the cancellation but exponentially concentrate kernel values, eroding inter-pair distinguishability. Our results characterise quantum coherence kernels as competitive with classical kernels on the same projected input rather than decisively superior, with the cluster-aware classical baseline retaining a modest advantage attributable to its explicit topical structure. Full article

In this study, we propose a quantum-probability-inspired complex-valued model for multilingual stance detection. The model brings together ideas from granular computing and quantum theory to better capture semantic meaning across different languages. The proposed model combines contextual embeddings, graph convolutional networks, and a quantum-inspired feature interaction module (QFIM) to capture complex, high-order, and non-linear relationships in multilingual data. The QFIM models quantum amplitude-like interactions to represent overlapping semantic patterns in the latent space. This design helps the system better distinguish subtle differences in stance expressions. To strengthen the representation, a granulation mechanism based on contextual embedding similarity is employed to extract semantically coherent text granules. These granules expand the feature space and help align different elements of the stance structure more accurately. Experimental results on standard benchmark datasets show that the proposed model consistently achieves better performance than existing state-of-the-art methods. Full article

In the era of data-driven education, educational social networks generate large volumes of high-dimensional and complex-structured data through learner interactions, collaborative activities, and resource-sharing behaviors, posing significant challenges to traditional unsupervised learning methods. Such data often exhibit non-convex distributions, heterogeneity, and noise sensitivity, making conventional clustering approaches insufficient for capturing their intrinsic structural relationships. To address this issue, this paper proposes Quantum Fidelity-Based Graph K-Means (QGKM), a clustering framework for robust pattern recognition in educational social networks. Specifically, QGKM employs quantum state encoding to map complex educational data into a quantum state space and utilizes quantum fidelity as a similarity metric to uncover latent correlations that Euclidean distance cannot effectively capture. In addition, the incorporation of k-nearest neighbor graphs preserves the local geometric structure of learner interaction networks, while a deterministic greedy hierarchical merging strategy eliminates the instability caused by random initialization. Experimental results on seven real-world datasets demonstrate that QGKM consistently outperforms classical K-Means in clustering accuracy. The proposed framework provides an effective solution for learning pattern discovery, learner profiling, and intelligent recommendation in digital education environments. Full article

Organic–inorganic hybrid halide perovskites have attracted extensive attention due to their excellent optoelectronic properties and potential applications in field-effect transistors (FET), light-emitting diodes (LEDs), and photodetectors. However, conventional three-dimensional (3D) perovskites are limited by intrinsic instability and ion migration. Two-dimensional Ruddlesden-Popper (2D RP) perovskites offer improved structural stability, but many systems still suffer from modest photoluminescence efficiency and limited charge-transport performance. In this work, a novel 2D RP perovskite, (C₆H₅NH₃)₂CsPb₂Cl₇, was designed and synthesized, where the anilinium ion (C₆H₅NH₃⁺) serves as the organic spacer. Structural characterization indicates that the material possesses high crystallinity and a smooth surface morphology. Optical measurements reveal a violet emission peak at 411 nm with a single-peak feature and a full width at half maximum (FWHM) of 10 nm. The bandgap is determined to be 3.1 eV. Time-resolved photoluminescence (TRPL) measurements show an average lifetime of 4 ns, and the photoluminescence quantum yield (PLQY) is 29.8%. Based on the measured PLQY and lifetime, the radiative and non-radiative recombination rates were estimated to be K_r ≈ 7.45 × 10⁷ s⁻¹ and K_nr ≈ 1.76 × 10⁸ s⁻¹, respectively, suggesting that radiative recombination is appreciable although non-radiative pathways remain present. FET measurements demonstrate an on/off current ratio of 10⁴ and a carrier mobility of 1.1 cm² V⁻¹ s⁻¹. Without any systematic optimization, (C₆H₅NH₃)₂CsPb₂Cl₇ exhibits relatively favorable emissive behavior and measurable field-effect charge transport performance when compared with structurally similar 2D RP perovskites reported under comparable, non-optimized conditions. This study expands the family of chloride-based 2D perovskites and provides a basis for future improvements in their recombination and field-effect transport properties. Full article

Rotational isomers (rotamers) of substituted aromatic molecules exhibit distinct physicochemical properties that are fundamental to understanding their reactivity and biological functions. However, resolving individual rotamers spectroscopically remains challenging due to their similar transition energies and overlapping spectral features. Herein, we report the conformer-specific identification and characterization of three stable rotamers of m-ethoxyphenol using a combination of resonance-enhanced multiphoton ionization (REMPI), hole-burning (HB) spectroscopy, and mass-analyzed threshold ionization (MATI) techniques, complemented by high-level quantum chemical calculations at the B3PW91/aug-cc-pVTZ and G4 levels of theory. The S₁ ← S₀ electronic origins of rotamers I, IV, and III were determined to be 35,966 ± 2, 36,031 ± 2, and 36,198 ± 2 cm⁻¹, respectively, while their corresponding adiabatic ionization energies (IEs) were precisely measured as 64,574 ± 5, 64,122 ± 5, and 64,994 ± 5 cm⁻¹. The vibrational spectra of both the S₁ excited state and the D₀ cationic ground state were assigned, with most active modes corresponding to in-plane benzene ring vibrations. Structural analysis reveals that the benzene ring undergoes slight expansion upon S₁ ← S₀ excitation and contraction upon D₀ ← S₁ ionization, while the overall molecular geometry remains remarkably similar across all three electronic states, a key factor underlying the excellent agreement between experimental and simulated Franck–Condon spectra. Comparison with m-methoxyphenol demonstrates that the stronger electron-donating ability of the ethoxy group leads to systematically lower excitation and ionization energies. The distinct spectroscopic fingerprints established herein provide a definitive reference for identifying specific m-ethoxyphenol rotamers in future studies of this molecule and its complexes. Full article

Spin squeezing can suppress quantum projection noise via interparticle entanglement, therefore enabling measurement sensitivities beyond the standard quantum limit. In practice, however, the Gaussian and finite intensity profiles of the optical probe beam induce spatially inhomogeneous atom-light interactions. As polarized atoms move within a vapor cell, they experience position-dependent optical intensities, generating transit noise that limits spin squeezing performance. Here, we investigate the transit noise in a coated rubidium vapor cell through combined theoretical analysis and experimental measurements. By varying the probe beam diameter, we quantify the dependence of transit noise on beam size and atomic Larmor frequency. Our results show that, for a vapor cell with fixed dimensions, the transit noise increases as the probe beam spot area decreases. Moreover, when the Larmor frequency is below the characteristic linewidth of the transit noise, the noise contribution becomes larger. We further calculated and measured spin squeezing for different beam sizes and found an experimental difference of $2.7\pm 0.2$ dB between 2 mm and 0.6 mm, similar to the theoretical prediction of $3.0\pm 0.3$ dB. Theoretical analysis under conditions of stronger squeezing shows that transit noise becomes an even more critical limiting factor. These results provide practical guidance for optimizing probe beam parameters and suppressing transit noise in spin squeezing experiments. Full article

Organic-type solar cells containing an active layer of block copolymer donor PTB7-Fx (x = 0, 20, and 100), based on benzo [1,2-b:4,5-b’]dithiophene and variably fluorinated thieno [3,4-b]thiophene units, and fullerene acceptor [6,6]phenyl-C₇₁-methylbutyrate, were constructed. The active layer thin film of the solar cells was obtained from a dichlorobenzene solution at an established concentration via spin-coating of the donor–acceptor mixture in the presence of solvent additives such as 3% diiodooctane and 1% triethyl phosphate. Organic photovoltaic elements with normal device architecture were prepared on glass substrates using an indium tin oxide anode, a spin-coated hole transporting layer of poly(ethylene dioxythiophene):polystyrenesulfonate, the aforementioned active layer, followed by an electron transporting layer of zinc oxide nanoparticles, and finally a magnetron sputtered silver (Ag) top-electrode. The optical properties, thin film morphology, and the thickness of the active layers were investigated. Additionally, current density–voltage characteristics and impedance spectra of photovoltaic devices were measured. It was found that PTB7-Fx:PC₇₁BM-based solar cells processed in the presence of two types of solvent additives, diiodooctane and triethyl phosphate, with a sputtered Ag top-electrode display similar absorption and quantum efficiency spectra, as well as comparable current density–voltage characteristics and efficiencies to the same devices fabricated without additives. The diiodooctane solvent additive preferably dissolves the fullerene component and has a positive effect on fill factor enhancement, impedance spectra improvement, and amelioration in charge carrier transport and collection, whereas the triethyl phosphate solvent additive preferentially dissolves the copolymer donor and has a more pronounced impact on the refined morphology of the thin film active layers. Full article

Reliable high-fidelity video transmission over noisy quantum channels remains challenging, especially due to temporal dependencies introduced by modern video compression standards. These codecs, such as versatile video coding (VVC), employ inter-frame prediction and group-of-pictures (GOP) structures, which are highly sensitive to channel noise and can lead to error propagation across frames. Conventional quantum encoding schemes, such as Hadamard-based superposition encoding, use fixed real-valued basis transformations that provide limited phase diversity and underutilize the multi-qubit state-space, reducing robustness under noisy quantum channels. To overcome these limitations, this study proposes a multi-qubit complex-valued orthogonal unitary superposition (COUS) encoding framework for quantum video transmission. In the proposed system, VVC-compressed video bitstreams are first protected using classical channel encoding, then segmented and mapped onto multi-qubit COUS quantum states, enabling joint amplitude and phase representation with improved resilience to quantum noise. At the receiver, transmitted quantum states undergo sequential COUS decoding, channel decoding, and VVC bitstream reconstruction to recover the original video frames. The simulation results show that COUS-based multi-qubit system outperforms the Hadamard encoding-based multi-qubit system, achieving peak signal-to-noise ratio (PSNR) up to 47.22 dB, structural similarity index measure (SSIM) up to 0.9905, and video multi-method assessment fusion (VMAF) up to 96.49. Even single-qubit COUS encoding achieves 3–4 dB channel SNR gain, while higher-qubit configurations further enhance robustness and reconstructed video quality. These results confirm that the proposed framework is scalable, noise-resilient, and provides high-fidelity quantum video transmission over noisy channels. Full article

Photodetectors have undergone widespread, gradual application. Correlation detectors with varying properties are used in diverse fields. This review systematically summarizes the principles, properties, and applications of various photoelectric detectors reported in the past five years, compares their similarities and differences, and further discusses their respective advantages and disadvantages, applicable scenarios, and development prospects. The review covers self-powered detectors, which are very convenient and widely used in consumer electronics and portable wearable devices, and discusses the structural design and photoelectric performance of devices based on P–N junctions, perovskites, silicon–polymer hybrid composites, graphene, hybrid graphene/PbS quantum dot systems, and other novel material architectures. Compound photoelectric detectors enable multifunctional integration and intellectualization. At the same time, their high sensitivity and broad-spectrum response can expand the detection wavelength range to cover the ultraviolet, visible, and infrared bands and enhance the detection of weak optical signals. Finally, this review summarizes current challenges, including cumbersome fabrication processes, susceptibility of detection stability to environmental interference, and limited functionality, and focuses on recent advances in various photodetectors, where breakthroughs are expected. Full article

While the quantum emergence of spacetime is becoming a major research topic in physics, the quantum emergence of intelligence has not been widely researched in quantum information science (QIS). Following causal-logical quantum gravity theory, bipolar entropy vs. entropy and negative entropy (or negentropy) are reviewed and distinguished for quantum emergence/submergence of quantum agent (QA) and quantum intelligence (QI) in algebraic terms. This work refers to QA as an entangled bipolar string/superstring in bipolar dynamic equilibrium (BDE) and QI being centered on logically definable causality in regularity, mind-light-matter unity, and brain-universe similarity. ER = EPR is extended to ER ≥≥ EPR for the mathematical scalability of bipolar strings and their collective entanglement. The extension leads to a number of conjectures, testable predictions, and theorems. The term “equilibraton” is proposed as a type of EPR or bipolar generic string to serve as an entropic stitch to collectively hold the universe together as a quantum entanglement in BDE with ubiquitous, regulated local emergence and submergence of QA&QI. Equilibraton leads to the concept of bipolar entropy square—a complete entropic solution to the background issue in quantum gravity. With complete background independence, energy/information conservational bipolar entropy, energy/information invariance, bipolar entropy non-additivity, and equilibrium-based plateau concavity are introduced. The nature of the one-dimensional arrow of time is conjectured. As a unification of order and disorder for equilibrium-based regulation, bipolar entropy bridges QA&QI to agentic AI, where quantum-bio-economics can be viewed as a topological intervention of a natural dynamic equilibrium in a social or natural world. Use cases are reviewed to illustrate the practical and theoretical aspects of bipolar entropy in business management, quantum-bio-economics, quantum cryptography, physics, and biology. Eddington–Einstein’s comments on entropy are revisited. It is expected that bipolar entropy will bring quantum emergence/submergence to agentic AI&QI for entangled machine thinking and imagination as a naturally scalable and testable foundation of real-world quantum gravity, quantum information science (QIS), quantum cognition and quantum biology (QCQB) to enhance Large Language AI Models (LLMs) and machine intelligence. Full article

We present Quantum-Informational History Optimization Theory (QIHOT) as a formal proposal for selecting a single realized quantum history from a space of dynamically admissible histories subject to boundary constraints. In the present paper, we restrict attention to finite-dimensional and toy-model settings, where the framework can be stated explicitly. QIHOT separates two levels: a dynamical prior over admissible histories generated by standard quantum evolution, and an informational selection rule that reweights those histories by an entropy-based cost functional. Within this structure, we show that standard Born statistics are recovered in symmetric-cost measurement scenarios when the prior is the usual Hilbert-space quantum prior. We further formulate conditions under which operational no-signaling is preserved, provided the selection functional factorizes locally for spacelike-separated regions. A fully worked two-outcome model illustrates how the framework interpolates between coherent evolution and measurement-like branch selection. We contrast QIHOT with the Many-Worlds Interpretation, the Transactional Interpretation, the Consistent Histories formalism, the Schwinger–Keldysh formalism, and Lagrangian-based retrocausal models, highlighting structural similarities and key differences. We emphasize that the present paper develops QIHOT as a scoped formal proposal with partial consistency results rather than as a complete replacement for quantum theory. Possible extensions to consciousness and cosmology are deferred to brief outlook-level discussion. Full article

Efficient and reliable transmission of compressed images over noisy channels remains a significant challenge due to the high sensitivity to noise. Quantum communication offers a promising solution by encoding classical information into quantum states; however, these states are still susceptible to noise and quantum decoherence. To address these limitations, we propose a complex-valued orthogonal unitary superposition (COUS) encoding integrated with a three-qubit quantum error correction (QEC) framework for robust and low-complexity quantum image transmission. The COUS encoding preserves both amplitude and phase information, enhancing reconstruction fidelity while maintaining practical scalability. In the proposed system, images are first compressed using either the joint photographic experts group (JPEG) standard or the high-efficiency image file (HEIF) standard and encoded into quantum states. Quantum channel coding is then applied to protect against quantum noise, followed by COUS encoding prior to transmission. At the receiver, the transmitted data undergoes COUS decoding, quantum error correction, quantum decoding, and source decoding to reconstruct the images. Performance improvements are observed across peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI) metrics. Simulation results demonstrate that the proposed approach outperforms conventional Hadamard encoding-based three-qubit QEC schemes, achieving maximum channel signal-to-noise ratio (SNR) gains of up to 6 dB, and surpasses bandwidth-equivalent classical communication systems employing polar codes, achieving channel SNR gains of up to 12 dB. These results highlight the potential of the proposed method as a practical solution for high-fidelity quantum image communication, overcoming the limitations of existing approaches. Full article

Quantum image transmission over noisy communication channels remains a challenge due to the fragility of quantum states and their susceptibility to channel impairments. Existing quantum encoding schemes often exhibit limited noise resilience, while advanced approaches introduce computational and implementation complexity. To address these limitations, this paper proposes a circular polarization-based quantum encoding framework for image transmission over error-prone channels. In the proposed approach, source images are compressed and source-encoded using standard image coding formats, including the joint photographic experts group (JPEG) standard and the high-efficiency image file format (HEIF), and converted into classical bitstreams. The resulting bitstreams are protected using channel coding and mapped onto quantum states via circular polarization representations, where left- and right-hand circularly polarized states encode binary information. The encoded quantum states are transmitted over noisy quantum channels to model channel impairments. At the receiver, appropriate quantum decoding and channel decoding operations are applied to recover the classical bitstream, followed by source decoding to reconstruct the image. The performance of the proposed framework is evaluated using image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Simulation results demonstrate that the proposed circular polarization-based encoding scheme outperforms existing quantum image encoding techniques, achieving channel SNR gains of 4 dB over state-of-the-art Hadamard-based encoding and 3 dB over frequency-domain quantum encoding methods under severe noise conditions. These results indicate that circular polarization-based quantum encoding provides improved noise robustness and reconstruction fidelity for practical quantum image transmission systems. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

Zinc and iron are essential micronutrients in crop nutrition, and polymer-based nanogels have emerged as promising carriers to modulate their availability in sustainable agricultural systems. Here, a polymeric model receptor was designed to investigate how the nature and position of electron-donating (–NH₂) and electron-withdrawing (–NO₂) substituents control the recognition of Zn²⁺ and Fe²⁺ cations. Using a combination of density functional theory calculations, energy decomposition analysis with natural orbitals for chemical valence (EDA–NOCV), electrostatic potential (ESP) mapping, and quantum theory of atoms in molecules (QTAIM) method, the receptor–cation interactions are dissected into electrostatic, Pauli repulsion, orbital, and dispersion contributions. The results show that complex stability is governed mainly by orbital and electrostatic terms, with Fe²⁺ forming the most stable complex (−393.57 kcal mol⁻¹) with regard to a Zn²⁺ similar complex (−288.80 kcal mol⁻¹). Zn²⁺ complexes exhibit a broad tunability with substituent pattern. Electron-donating groups systematically strengthen both electrostatic and orbital components, whereas nitro substituents display a pronounced positional effect, ranging from strong destabilization to significant stabilization of Zn²⁺ binding. These findings establish molecular-level guidelines for engineering polymeric nanogels with tunable affinity and selectivity toward micronutrient cations in agricultural applications. Full article