Search Results (433)

The rapid growth of digital communication has intensified spam-related threats, including phishing and malware, which employ advanced evasion tactics. Traditional filtering methods struggle to keep pace, driving the need for sophisticated machine learning (ML) solutions. The effectiveness of ML models hinges on selecting high-quality input features, especially in high-dimensional datasets where irrelevant or redundant attributes impair performance and computational efficiency. Guided by principles of symmetry to achieve an optimal balance between model accuracy, complexity, and interpretability, this study proposes an Enhanced Hybrid Quantum-Inspired Firefly and Artificial Bee Colony (EHQ-FABC) algorithm for feature selection in spam detection. EHQ-FABC leverages the Firefly Algorithm’s local exploitation and the Artificial Bee Colony’s global exploration, augmented with quantum-inspired principles to maintain search space diversity and a symmetrical balance between exploration and exploitation. It eliminates redundant attributes while preserving predictive power. For interpretability, Shapley Additive Explanations (SHAPs) are employed to ensure symmetry in explanation, meaning features with equal contributions are assigned equal importance, providing a fair and consistent interpretation of the model’s decisions. Evaluated on the ISCX-URL2016 dataset, EHQ-FABC reduces features by over 76%, retaining only 17 of 72 features, while matching or outperforming filter, wrapper, embedded, and metaheuristic methods. Tested across ML classifiers like CatBoost, XGBoost, Random Forest, Extra Trees, Decision Tree, K-Nearest Neighbors, Logistic Regression, and Multi-Layer Perceptron, EHQ-FABC achieves a peak accuracy of 99.97% with CatBoost and robust results across tree ensembles, neural, and linear models. SHAP analysis highlights features like domain_token_count and NumberOfDotsinURL as key for spam detection, offering actionable insights for practitioners. EHQ-FABC provides a reliable, transparent, and efficient symmetry-aware solution, advancing both accuracy and explainability in spam detection. Full article

The subset-sum problem, a foundational NP-hard problem in theoretical computer science, serves as a critical building block for cryptographic constructions. This work introduces novel classical and quantum heuristic algorithms for the random subset-sum problem at density $d=1$, where exactly one solution is expected. Classically, we propose the first algorithm based on a ternary tree representation structure, inspired by recent advances in lattice-based cryptanalysis. Through numerical optimization, our method achieves a time complexity of $\tilde{\mathcal{O}}\left(2^{0.2400n}\right)$ and space complexity of $\tilde{\mathcal{O}}\left(2^{0.2221n}\right)$, improving upon the previous best classical heuristic result of $\tilde{\mathcal{O}}\left(2^{0.2830n}\right)$. In the quantum setting, we develop a corresponding algorithm by integrating the classical ternary representation technique with a quantum walk search framework. The optimized quantum algorithm attains a time and space complexity of $\tilde{\mathcal{O}}\left(2^{0.1843n}\right)$, surpassing the prior state-of-the-art quantum heuristic of $\tilde{\mathcal{O}}\left(2^{0.2182n}\right)$. Furthermore, we apply our algorithms to information set decoding in code-based cryptography. For half-distance decoding, our classical algorithm improves the time complexity to $\tilde{\mathcal{O}}\left(2^{0.0453n}\right)$, surpassing the previous best of $\tilde{\mathcal{O}}\left(2^{0.0465n}\right)$. For full-distance decoding, we achieve a quantum complexity of $\tilde{\mathcal{O}}\left(2^{0.058326n}\right)$, advancing beyond the prior best quantum result of $\tilde{\mathcal{O}}\left(2^{0.058696n}\right)$. These findings demonstrate the broad applicability and efficiency of our ternary representation technique across both classical and quantum computational models. Full article

Creative insight is a core phenomenon of human cognition, often characterized by the sudden emergence of novel and contextually appropriate ideas. Classical models based on symbolic search or associative networks struggle to capture the non-linear, context-sensitive, and interference-driven aspects of insight. In this work, we propose a computational model of insight generation grounded in continuous-time quantum walks over weighted semantic graphs, where nodes represent conceptual units and edges encode associative relationships. By exploiting the principles of quantum superposition and interference, the model enables the probabilistic amplification of semantically distant but contextually relevant concepts, providing a plausible account of non-local transitions in thought. The model is implemented using standard Python 3.10 libraries and is available both as an interactive fully reproducible Google Colab notebook and a public repository with code and derived datasets. Comparative experiments on ConceptNet-derived subgraphs, including the Candle Problem, 20 Remote Associates Test triads, and Alternative Uses, show that, relative to classical diffusion, quantum walks concentrate more probability on correct targets (higher AUC and peaks reached earlier) and, in open-ended settings, explore more broadly and deeply (higher entropy and coverage, larger expected radius, and faster access to distant regions). These findings are robust under normalized generators and a common time normalization, align with our formal conditions for transient interference-driven amplification, and support quantum-like dynamics as a principled process model for key features of insight. Full article

The integration of blockchain technology into healthcare systems has emerged as a technical solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, and efficiency in healthcare services, making them a focal point of many studies. However, challenges such as scalability, regulatory compliance, and interoperability continue to limit their widespread adoption. This study conducts a comprehensive literature review to assess blockchain-driven health data management, focusing on the classification of blockchain-based smart contracts in health policy and the health protocols and standards applicable to blockchain-based smart contracts. This review includes 80 core studies published between 2019 and 2025, identified through searches in PubMed, Scopus, and Web of Science using the PRISMA method. Risk of bias and methodological quality were assessed using the Joanna Briggs Institute tool. The findings highlight the potential of blockchain-enabled smart contracts in health policy management, emphasizing their advantages, limitations, and implementation challenges. Additionally, the research underscores their transformative impact on digital health policies in ensuring data integrity, enhancing patient autonomy, and fostering a more resilient healthcare ecosystem. Recent advancements in quantum technologies are also considered as they present both novel opportunities and emerging threats to the future security and design of healthcare blockchain systems. Full article

In this purely analytical analysis, we have investigated the effects of a point-like defect in a continuous medium on a diatomic molecule under the influence of small oscillations arising from the Lennard-Jones potential. In the search for bound-state solutions, we have shown that the allowed values for the lowest energy state of the molecule are influenced by the presence of the defect. Furthermore, another quantum effect was observed: the stability radial point of the diatomic molecule depends on the system’s quantum numbers; it is quantized. Full article

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

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

This paper proposes a quantum algorithm, named Dicke state quantum search (DSQS), to set qubits in the Dicke state $|D_k^n⟩$ of D states in superposition to locate the target inputs or solutions of specific patterns among $2^n$ unstructured input instances, where n is the number of input qubits and $D=\left({\displaystyle \genfrac{}{}{0pt}{}{n}{k}}\right)=\mathrm{O}\left(n^k\right)$ for $min(k,n-k)\ll n/2$. Compared to Grover’s algorithm, a famous quantum search algorithm that calls an oracle and a diffuser O($\sqrt{2^n}$) times, DSQS requires no diffuser and calls an oracle only once. Furthermore, DSQS does not need to know the number of solutions in advance. We prove the correctness of DSQS with unitary transformations, and show that each solution can be found by DSQS with an error probability less than 1/3 through O($n^k$) repetitions, as long as $min(k,n-k)\ll n/2$. Additionally, this paper proposes a classical algorithm, named DSQS-VCP, to generate quantum circuits based on DSQS for solving the k-vertex cover problem (k-VCP), a well-known NP-complete (NPC) problem. Complexity analysis demonstrates that DSQS-VCP operates in polynomial time and that the quantum circuit generated by DSQS-VCP has a polynomial qubit count, gate count, and circuit depth as long as $min(k,n-k)\ll n/2$. We thus conclude that the k-VCP can be solved by the DSQS-VCP quantum circuit in polynomial time with an error probability less than 1/3 under the condition of $min(k,n-k)\ll n/2$. Since the k-VCP is NP-complete, NP and NPC problems can be polynomially reduced to the k-VCP. If the reduced k-VCP instance satisfies $min(k,n-k)\ll n/2$, then both the instance and the original NP/NPC problem instance to which it corresponds can be solved by the DSQS-VCP quantum circuit in polynomial time with an error probability less than 1/3. All statements of polynomial algorithm execution time in this paper apply only to VCP instances and similar instances of other problems, where $min(k,n-k)\ll n/2$. Thus, they imply neither NP ⊆ BQP nor P = NP. In this restricted regime of $min(k,n-k)\ll n/2$, the Dicke state subspace has a polynomial size of $D=\left({\displaystyle \genfrac{}{}{0pt}{}{n}{k}}\right)=\mathrm{O}\left(n^k\right)$, and our DSQS algorithm samples from it without asymptotic superiority over exhaustive enumeration. Nevertheless, DSQS may be combined with other quantum algorithms to better exploit the strengths of quantum computation in practice. Experimental results using IBM Qiskit packages show that the DSQS-VCP quantum circuit can solve the k-VCP successfully. Full article

In exotic atomic systems with hadronic constituent particles, it is notoriously difficult to estimate the strong-interaction correction to energy levels. It is well known that, due to the strength of the nuclear interaction, the problem cannot be solved using Wigner–Brillouin perturbation theory alone. Recently, high-angular-momentum Rydberg states of exotic atomic systems with hadronic constituents have been identified as promising candidates in the search for new physics in the low-energy sector of the Standard Model. We thus derive a generalized Deser–Trueman formula for the induced energy shift for a general hydrogenic bound state with principal quantum number n and orbital angular momentum quantum number ℓ, and we find that the energy shift is given by the formula $\delta E=2{\alpha }_{n,\ell }{\beta }_{\ell }{(a_h/a_0)}^{2\ell +1}{E}_h/n^3$, where ${\alpha }_{n,0}=1$, ${\alpha }_{n,\ell }={\prod }_{s=1}^{\ell }(s^{-2}-n^{-2})$, ${\beta }_{\ell }=(2\ell +1)/{[(2\ell +1)!!]}^2$, $E_h$ is the Hartree energy, $a_h$ is the hadronic radius and $a_0$ is the generalized Bohr radius. The square of the double factorial, ${[(2\ell +1)!!]}^2$, in the denominator implies a drastic suppression of the effect for higher angular momenta. Full article

Identifying the most reactive conformation of a molecule is a central challenge in computational chemistry, particularly when reactivity depends on subtle conformational effects. While most conformation search tools aim to find the lowest-energy structure, they often overlook the electronic descriptors that govern chemical reactivity. In this work, we present GAELLE, a cheminformatics tool that combines conformer generation with quantum reactivity descriptors to identify the most reactive structure of a molecule in solution. GAELLE integrates an evolutionary algorithm with fast semiempirical quantum chemical calculations (xTB), enabling the automated ranking of conformers based on HOMO–LUMO gap minimization (Pearson’s principle of maximum hardness) and electrophilicity index (Parr’s electrophilicity scale). Solvent effects are accounted for via implicit solvation models (GBSA/ALPB) to ensure realistic evaluation of reactivity in solution. The method is fully SMILES-driven, open-source, and scalable to medium-sized drug-like molecules. Applications to reactive intermediates, bioactive conformations, and pre-reactive complexes demonstrate the method’s relevance for mechanism elucidation, molecular design, and in silico screening. GAELLE is publicly available and offers a reactivity-focused alternative to traditional energy-minimization tools in conformational analysis. Full article

This manuscript is intended for readers who have a general interest in the subject of quantum computation and provides an overview of the most significant developments in the field. It begins by introducing foundational concepts from quantum mechanics—such as superposition, entanglement, and the no-cloning theorem—that underpin quantum computation. The primary computational models are discussed, including gate-based (circuit) quantum computing, adiabatic quantum computing, measurement-based quantum computing and the quantum Turing machine. A selection of significant quantum algorithms are reviewed, notably Grover’s search algorithm, Shor’s factoring algorithm, and Quantum Singular Value Transformation (QSVT), which enables efficient solutions to linear algebra problems on quantum devices. To assess practical performance, we compare quantum and classical implementations of support vector machines (SVMs) using several synthetic datasets. These experiments offer insight into the capabilities and limitations of near-term quantum classifiers relative to classical counterparts. Finally, we review leading quantum programming platforms—including Qiskit, PennyLane, and Cirq—and discuss their roles in bridging theoretical models with real-world quantum hardware. The paper aims to provide a concise yet comprehensive guide for those looking to understand both the theoretical foundations and applied aspects of quantum computing. Full article

In order to scientifically and reasonably evaluate the digital transformation and upgrading level of “emerging industry” innovation ecosystems, this paper firstly uses the grounded theory to extract the factors influencing the digital transformation and upgrading of the emerging industry innovation ecosystems. Secondly, a cloud model is introduced to evaluate the importance of the influencing factors, select the important factors, and construct an evaluation index system. Thirdly, the projection pursuit model based on the quantum genetic algorithm is used to search for the optimal projection direction and determine the weight and comprehensive evaluation value of each index. Finally, the digital transformation and upgrading levels of 506 innovation subjects are divided into a budding level (I), growth level (II), and mature level (III) based on K-means and the SVM—most of which are at a medium–low level. Therefore, countermeasures and suggestions for promoting the digital transformation and upgrading of the emerging industry innovation ecosystems are put forward. This paper provides a systematic and complete method for the evaluation of digital transformation and upgrading of the emerging industry innovation ecosystems. Further, this paper promotes the combination of qualitative and quantitative analysis and realizes the effective integration of the overall logic chain of theoretical demonstrations, method design, and data analysis. Full article

We propose a new strategy for constructing Runge–Kutta (RK) methods using evolutionary computation techniques, with the goal of directly minimising global error rather than relying on traditional local properties. This approach is general and applicable to a wide range of differential equations. To highlight its effectiveness, we apply it to two benchmark problems with oscillatory behaviour: the (2+1)-dimensional nonlinear Schrödinger equation and the N-Body problem (the latter over a long interval), which are central in quantum physics and astronomy, respectively. The method optimises four free coefficients of a sixth-order, eight-stage parametric RK scheme using a novel objective function that compares global error against a benchmark method over a range of step lengths. It overcomes challenges such as local minima in the free coefficient search space and the absence of derivative information of the objective function. Notably, the optimisation relaxes standard RK node bounds ($c_i\in [0,1]$), leading to improved local stability, lower truncation error, and superior global accuracy. The results also reveal structural patterns in coefficient values when targeting high eccentricity and non-sinusoidal problems, offering insight for future RK method design. Full article

This paper presents a hybrid computational architecture for efficient and robust digital transmission inspired by helical genetic structures. The proposed system integrates advanced modulation schemes, such as multi-pulse-position modulation (MPPM), high-order quadrature amplitude modulation (QAM), and chirp spread spectrum (CSS), along with Reed–Solomon error correction and quantum-assisted search, to optimize performance in noisy and non-line-of-sight (NLOS) optical environments, including VLC channels modeled with log-normal fading. Through mathematical modeling and simulation, we demonstrate that the number of helical transmissions required for genome-scale data can be drastically reduced—up to 95% when using parallel strands and high-order modulation. The trade-off between redundancy, spectral efficiency, and error resilience is quantified across several configurations. Furthermore, we compare classical genetic algorithms and Grover’s quantum search algorithm, highlighting the potential of quantum computing in accelerating decision-making and data encoding. These results contribute to the field of operations research and supply chain communication by offering a scalable, energy-efficient framework for data transmission in distributed systems, such as logistics networks, smart sensing platforms, and industrial monitoring systems. The proposed architecture aligns with the goals of advanced computational modeling and optimization in engineering and operations management. Full article

The creation of larger prebiotic molecules in astronomical regions may require aminoketene (NH₂CHCO) as an intermediate, and the two conformers of this molecule exhibit infrared vibrational frequencies with intensities larger even than the antisymmetric stretch in CO₂. While the present quantum chemically computed frequencies of these fundamentals of ∼4.7 μm are in the same spectroscopic region as features from functionalized polycyclic aromatic hydrocarbons, they provide clear markers for what James Webb Space Telescope IR observations may be able to distinguish. Additionally, the IR and radioastronomical spectral characterization of two additional 2-iminoacetaldehyde, HN=CHC(=O)H, conformers are also computed as are the same data for a new carbene isomer (NH₂CC(=O)H). All conformers of aminoketene and 2-iminoacetaldehyde exhibit dipole moments of more than 2.0 D, if not greater than 4.0 D, implying that they would be notable targets for radioastronomical searches. Additionally, the 2-iminoacetaldehyde conformers have a notable mid-IR C=O stretch around 1735 cm⁻¹ slightly below the same fundamental in formaldehyde. This quantum chemical study is providing a more complete set of reference data for the potential observation of these tautomers and conformers of NH₂CHCO in the laboratory or even in space. Full article