Search Results (629)

The purpose of this article is to highlight the connections between two seemingly distinct domains: random walks and the distribution of angular-momentum projections in quantum physics (the magnetic quantum numbers m). It is well known that there is indeed a deep mathematical link between the two, via the vector composition of angular momenta and rotational symmetry. Random walks are considered in the framework of an interpretation of the probability of microstates in statistical physics. The ideas presented in this work aim to illustrate the relevance of this perspective for modeling angular momentum in atomic physics. Full article

This article develops a structural–analogical framework to investigate conceptual resonances between Qur’an 24:35—the Verse of Light—and contemporary relational models in physics, while maintaining firm epistemic boundaries between theology, philosophy, and empirical science. The Qur’anic metaphors of niche, glass, tree, oil, and layered light depict a graded ontology of manifestation in which being unfolds through ordered relations grounded in a transcendent divine command (amr). By contrast, modern physics—as represented by quantum field theory, loop quantum gravity, and cosmological models—operates entirely within immanent causality, conceiving spacetime and matter as relational, dynamic, and structurally emergent. Despite their distinct registers, both discourses converge structurally around a shared grammar of potentiality, relation, and manifestation. Drawing on classical Islamic metaphysics—especially al-Ghazālī’s Mishkāt al-Anwār—alongside contemporary relational ontologies in physics (Smolin, Rovelli, Markopoulou), the article argues that “real time” functions as an ontological choice that conditions intelligibility, agency, and novelty. The Qur’anic notion of nūr is interpreted not as physical luminosity but as the metaphysical ground of determinability, while the quantum vacuum is treated as a field of latent potential—without suggesting empirical equivalence. Rather than concordism, the comparison highlights a structural resonance (used here as a heuristic notion indicating pattern-level affinity rather than equivalence, correspondence, or empirical verification): both traditions affirm that reality is neither static nor substance-based, but arises through dynamic relational processes grounded—whether transcendently or immanently—in principled order. Full article

This paper addresses a central conceptual challenge in Quantum-like Cognition and Decision-Making (QCDM) and the broader research program of Quantum-like Modeling (QLM): the interpretation of phases in quantum-like state superpositions. In QLM, system states are represented by normalized vectors in a complex Hilbert space, $|\psi ⟩={\sum }_k{X}_k|k⟩$, where the squared amplitudes $P_k={\left|X_k\right|}^2$ are outcome probabilities. However, the meaning of the phase factors $e^{i{\varphi }_k}$ in the coefficients $X_k=P_k{e}^{i{\varphi }_k}$ has remained elusive, often treating them as purely phenomenological parameters. This practice, while successful in describing cognitive interference effects (the “interference of the mind”), has drawn criticism for expanding the model’s parameter space without a clear physical or cognitive underpinning. Building on a recent framework that connects QCDM to neuronal network activity, we propose a concrete interpretation. We argue that the phases in quantum-like superpositions correspond directly to the phases of random oscillations generated by neuronal circuits in the brain. This interpretation not only provides a natural, non-phenomenological basis for phase parameters within QCDM but also helps to bridge the gap between quantum-like models and classical neurocognitive frameworks, offering a consistent physical analogy for the descriptive power of QLM. Full article

In quasi-Hermitian quantum mechanics (QHQM) of unitary systems, an optimal, calculation-friendly form of Hamiltonian is generally non-Hermitian, $H\ne H^{†}$. This makes its physical interpretation ambiguous. Without altering H, this ambiguity can be resolved either via a transformation of H into its isospectral Hermitian form via a so-called Dyson map $\mathrm{\Omega }:H\to \mathfrak{h}$, or via a (formally equivalent) specification of a nontrivial physical inner-product metric $\mathrm{\Theta }$ in Hilbert space. Here, we focus on the former strategy. Our present construction of the Hermitian isospectral twins $\mathfrak{h}$ of H is exhaustive. As a byproduct, it not only restores the conventional correspondence principle between quantum and classical physics, but it also provides a framework for a systematic classification of all of the admissible probabilistic interpretations of quantum systems using a preselected H in QHQM framework. Full article

The Ehrenfest theorem is a well-known theoretical result of quantum mechanics. It relates the dynamical evolution of the expectation value of a quantum operator to the expectation value of its corresponding commutator with the Hermitian Hamiltonian operator. However, the proof of validity of the Ehrenfest theorem for quantum gravity field theory has remained elusive, while its validation poses challenging conceptual questions. In fact, this presupposes a number of minimum requirements, which include the prescription of quantum Hamiltonian operator, the definition of scalar product, and the identification of dynamical evolution parameter. In this paper, it is proven that the target can be established in the framework of the manifestly covariant quantum gravity theory (CQG theory). This follows as a consequence of its peculiar canonical Hamiltonian structure and the commutator-bracket algebra that characterizes its representation and probabilistic interpretation. The theoretical proof of the theorem for CQG theory permits to elucidate the connection existing between quantum operator variables of gravitational field and the corresponding expectation values to be interpreted as dynamical physical observables set in the background metric space-time. Full article

The rapid evolution of Artificial Intelligence has enabled significant progress in image classification, with emerging approaches extending traditional deep learning paradigms. This article presents an extended version of a paper originally introduced at ICCSA 2025, providing a broader comparative analysis of classical, spline-based, and quantum machine learning architectures. The study evaluates Convolutional Neural Networks (CNNs), Kolmogorov–Arnold Networks (KANs), Convolutional KANs (CKANs), and Quantum Convolutional Neural Networks (QCNNs) on the Labeled Faces in the Wild dataset. In addition to these baselines, two novel architectures are introduced: a fully quantum Kolmogorov–Arnold model (F-QKAN) and a hybrid KAN–Quantum network (H-QKAN) that combines spline-based feature extraction with variational quantum classification. Rather than targeting state-of-the-art performance, the evaluation focuses on analyzing the behaviour of these architectures in terms of accuracy, computational efficiency, and interpretability under a unified experimental protocol. Results show that the fully quantum F-QKAN achieves a test accuracy above 80%. The hybrid H-QKAN obtains the best overall performance, exceeding 92% accuracy with rapid convergence and stable training dynamics. Classical CNNs models remain state-of-the-art in terms of predictive performance, whereas CKANs offer a favorable balance between accuracy and efficiency. QCNNs show potential in ideal noise-free settings but are significantly affected by realistic noise conditions, motivating further investigation into hybrid quantum–classical designs. Full article

The pharmaceutical industry faces a broken drug development pipeline, characterized by high costs, slow timelines and is prone to high failure rates. The convergence of Artificial Intelligence (AI) and quantum technologies is poised to fundamentally transform this landscape. AI excels in interpreting complex data, optimizing processes and designing drug candidates, while quantum systems enable unprecedented molecular simulation, ultra-sensitive sensing and precise physical control. This convergence establishes an integrated, self-learning ecosystem for the discovery, development, and delivery of therapeutics. This framework co-designs strategies from molecular targeting to formulation stability, compressing timelines and enhancing precision, which may enable safer, faster, and more adaptive medicines. Full article

Omics research is changing the way medicine develops new strategies for diagnosis, prevention, and treatment. With the surge of advanced machine learning models tailored for omicss analysis, recent research has shown improved results and pushed the progress towards personalized medicine. The dissection of multiple layers of genetic information has provided new insights into precision medicine, at the same time raising issues related to data abundance. Studies focusing on single-cell scale have upgraded the knowledge about gene expression, revealing the heterogeneity that governs the functioning of multicellular organisms. The amount of information gathered through such sequencing techniques often exceeds the human capacity for analysis. Understanding the underlying network of gene expression regulation requires advanced computational tools that can deal with the complex analytical data provided. The recent emergence of artificial intelligence-based frameworks, together with advances in quantum algorithms, has the potential to enhance multiomicsc analyses, increasing the efficiency and reliability of the gene expression profile prediction. The development of more accurate computational models will significantly reduce the error rates in interpreting large datasets. By making analytical workflows faster and more precise, these innovations make it easier to integrate and interrogate multi-omics data at scale. Deep learning (DL) networks perform well in terms of recognizing complex patterns and modeling non-linear relationships that enable the inference of gene expression profiles. Applications range from direct prediction of DNA sequence-informed predictive modeling to transcriptomic and epigenetic analysis. Quantum computing, particularly through quantum machine learning methods, is being explored as a complementary approach for predictive modeling, with potential applications to complex gene interactions in increasingly large and high-dimensional biological datasets. Together, these tools are reshaping the study of complex biological data, while ongoing innovation in this field is driving progress towards personalized medicine. Overall, the combination of high-resolution omics and advanced computational tools marks an important shift toward more precise and data-driven clinical decision-making. Full article

The photoconductive properties of an InAs/GaAs quantum dot (QD) superlattice have been characterized using photo–Hall measurements under sub-bandgap illumination. The multi-stacked InAs/GaAs QD structure was grown using molecular beam epitaxy and photo–Hall effect measurements were performed under illumination using light-emitting diodes with three different emission wavelengths: 940 nm, 1300 nm, and 1550 nm. The results have shown that the sign reversal occurs in the Hall coefficient (R_H) as the illumination wavelength changes: R_H is negative at 940 nm and 1300 nm, and positive at 1550 nm. The photocurrent at 940 nm illumination is ascribed to the electron hole pair generation in QDs, whereas the photocurrent at 1550 nm is dominated by the hole current generated through the midgap states in the structure. A simplified rate equation model involving two-step photoexcitation through the midgap states has revealed that the dominant photocarriers and the Hall coefficient can change depending on the photoexcitation power. The steady-state photocurrent behavior including the observed sign reversal in the Hall coefficient has been interpreted by the proposed model. Full article

Vanadyl octa-(4-tert-butylphenyl)phthalocyanine (VOPc(t-BuPh)₈) and vanadyl octa-(4-tert-butylphenyl)tetrapyrazinoporphyrazine (VOTPyzPz(t-BuPh)₈) complexes were synthesized for the first time and confirmed by IR and UV-Vis spectroscopy and MALDI-TOF spectrometry. The method of synthesis of their precursors, 4,5-bis(4-tert-butylphenyl)phthalonitrile ((t-BuPh)₂PN) and 5,6-bis(4-tert-butylphenyl)pyrazine-2,3-dicarbonitrile ((t-BuPh)₂PDC), was modified, resulting in higher yields. For the vanadyl complexes, the basic properties were studied, and it was found that the red shift in the Q band in the first protonation step is approximately two times greater than that of previously known complexes. An electrochemical study showed the influence of aza-substitution on the redox properties and on the energies of the frontier orbitals of all the compounds presented. For all four considered compounds, quantum chemical calculations of the molecular structure, IR spectra, and electronic absorption spectra were carried out using density functional theory (DFT) and time-dependent density functional theory (TDDFT and simplified sTDDFT) approaches. According to the DFT calculations, vanadyl macrocyclic complexes have dome-shaped distorted structures. Experimental and theoretical IR and electronic absorption spectra were compared and interpreted. Full article

Quantum anomalies are traditionally understood as classical symmetries that fail to survive quantization, while experimental “anomalies” denote deviations between theoretical predictions and measured values. In this work, we develop a unified framework in which both phenomena can be interpreted through the lens of algebraic quantum field theory (AQFT). Building on the renormalization group viewed as an extension problem, we show that renormalization ambiguities correspond to nontrivial elements of Hochschild cohomology, giving rise to a deformation of the observable algebra $A\ast B=AB+\epsilon \omega (A,B)$, where $\omega$ is a Hochschild 2-cocycle. We interpret $\omega$ as an intrinsic algebraic curvature of the net of local algebras, namely the (local) Hochschild class that measures the obstruction to trivializing infinitesimal scheme changes by inner redefinitions under locality and covariance constraints. The transported product is associative; its first-order expansion is associative up to $\mathcal{O}\left({\epsilon }^2\right)$ while preserving the ∗-structure and Ward identities to the first order. We prove the existence of nontrivial cocycles in the perturbative AQFT setting, derive the conditions under which the deformed product respects positivity and locality, and establish the compatibility with current conservation. The construction provides a direct algebraic bridge to standard cohomological anomalies (chiral, trace, and gravitational) and yields correlated deformations of physical amplitudes. Fixing the small deformation parameter $\epsilon$ from the muon $(g-2)$ discrepancy, we propagate the framework to predictions for the electron $(g-2)$, charged lepton EDMs, and other low-energy observables. This approach reduces reliance on ad hoc form-factor parametrizations by organizing first-order scheme-induced deformations into correlation laws among low-energy observables. We argue that interpreting quantum anomalies as manifestations of algebraic curvature opens a pathway to a unified, testable account of renormalization ambiguities and their phenomenological consequences. We emphasize that the framework does not eliminate renormalization or quantum anomalies; rather, it repackages the finite renormalization freedom of pAQFT into cohomological data and relates it functorially to standard anomaly classes. Full article

Multimodal forecasting is increasingly needed to maintain energy levels, storage capacity, and compute efficiency in off-grid, renewable-powered cloud environments. Variable sensor quality, uncertain interactions with renewable energy, and rapidly changing weather patterns make real-time forecasting difficult. Current transformer, GNN, and CNN systems suffer from sensor noise instability, multimodal temporal–spectral correlation issues, and challenges in the interpretability of operational decision-making. In this research, Q-RCANeX, a quantum-guided residual convolutional attention network for off-grid cloud infrastructures, estimates battery state of charge, renewable energy sources, and microgrid efficiency to overcome these restrictions. The system uses a Hybrid Quantum–Bayesian Evolutionary Optimizer, quantum feature embedding, temporal–spectral attention, residual convolutional encoding, and signal decomposition preprocessing. These parameters reinforce features, reduce noise, and align forecasting behavior with microgrid dynamics. Q-RCANeX obtains 98.6% accuracy, 0.992 AUC, and 0.986 R³ values for REAF, WGF, SOC-F, and EEIF forecasting tasks, according to a statistical study. Additionally, it determines inference latency to 4.9 ms and model size to 18.5 MB. Even with 20% of sensor data missing or noisy, the model outperforms 12 state-of-the-art baselines and maintains 96.8% accuracy using ANOVA, Wilcoxon, Nemenyi, and Holm tests. The findings indicate that the forecasting framework has high accuracy, clarity, and resilience to failures. This makes it useful for real-time, off-grid management of renewable cloud microgrids. Full article

We formalize an inverse, data-conditioned variant of the Variational Quantum Eigensolver (VQE) for clinical biomarker discovery. Given patient-encoded quantum states, we construct a task-specific Hamiltonian whose coefficients are inferred from clinical associations and interpret its expectation value as a calibrated energy score for prognosis and treatment monitoring. The method integrates coefficient estimation, ansatz specification with basis rotations, commuting-group measurements, and a practical shot budget analysis. Evaluated on public infectious disease datasets under severe class imbalance, the approach yields consistent gains in balanced accuracy and precision–recall over strong classical baselines, with stability across random seeds and feature ablations. This variational energy scoring framework bridges Hamiltonian learning and clinical risk modeling, offering a compact, interpretable, and reproducible route to biomarker prioritization and decision support. Full article

The environmental persistence of antibiotic residues in aquatic systems represents a critical global challenge, with photolysis serving as a primary abiotic degradation pathway. Traditional approaches to studying antibiotic photodegradation and transformation product (TP) identification face significant limitations, including complex reaction mechanisms, multiple concurrent pathways, and analytical challenges in characterizing unknown metabolites. The integration of artificial intelligence (AI) and machine learning (ML) technologies has begun to transform this field, offering new capabilities for predicting photodegradation kinetics, elucidating transformation pathways, and identifying novel metabolites. This comprehensive review examines current applications of AI/ML in antibiotic photolysis research, analyzing developments from 2020 to 2025. Key advances include quantitative structure–activity relationship (QSAR) models for photodegradation prediction, deep learning approaches for automated mass spectrometry interpretation, and hybrid computational–experimental frameworks. Machine learning algorithms, particularly Random Forests, support vector machines, and Neural Networks, have demonstrated capabilities in handling multi-dimensional environmental datasets across diverse antibiotic classes, including fluoroquinolones, β-lactams, tetracyclines, and sulfonamides. Despite progress in this field, challenges remain in model interpretability, standardization of datasets, validation protocols, and integration with regulatory frameworks. Future directions include machine-learning-enhanced quantum dynamics for improving mechanistic understanding, real-time AI-guided experimental design, and predictive tools for environmental risk assessment. Full article

Background/Objectives: Skin cancer is one of the most common diseases in the world, whose early and accurate detection can have a survival rate more than 90% while the chance of mortality is almost 80% in case of late diagnostics. Methods: A modified EfficientNet-B0 is developed based on mobile inverted bottleneck convolution with squeeze and excitation approach. The 3 × 3 convolutional layer is used to capture low-level visual features while the core features are extracted using a sequence of Mobile Inverted Bottleneck Convolution blocks having both 3 × 3 and 5 × 5 kernels. They not only balance fine-grained extraction with broader contextual representation but also increase the network’s learning capacity while maintaining computational cost. The proposed architecture hyperparameters and extracted feature vectors of standard benchmark datasets (HAM10000, ISIC 2019 and MSLD v2.0) of dermoscopic images are optimized with the quantum-behaved particle swarm optimization algorithm (QBPSO). The merit function is formulated by the training loss given in the form of standard classification cross-entropy with label smoothing, mean fitness value (mf^val), average accuracy (mAcc), mean computational time (mCT) and other standard performance indicators. Results: Comprehensive scenario-based simulations were performed using the proposed framework on a publicly available dataset and found an mAcc of 99.62% and 92.5%, mfval of 2.912 × 10⁻¹⁰ and 1.7921 × 10⁻⁸, mCT of 501.431 s and 752.421 s for HAM10000 and ISIC2019 datasets, respectively. The results are compared with state of the art, pre-trained existing models like EfficentNet-B4, RegNetY-320, ResNetXt-101, EfficentNetV2-M, VGG-16, Deep Lab V3 as well as reported techniques based on Mask RCCN, Deep Belief Net, Ensemble CNN, SCDNet and FixMatch-LS techniques having varying accuracies from 85% to 94.8%. The reliability of the proposed architecture and stability of QBPSO is examined through Monte Carlo simulation of 100 independent runs and their statistical soundings. Conclusions: The proposed framework reduces diagnostic errors and assists dermatologists in clinical decisions for an improved patient outcomes despite the challenges like data imbalance and interpretability. Full article