Search Results (42)

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 advent of quantum computing has introduced new opportunities for enhancing classical machine learning architectures. In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum–Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical fully connected neural layers with a parameterized quantum circuit, enabling the processing of textual data within both classical and quantum computational domains. To assess its effectiveness, we conducted experiments on the widely used LIAR dataset utilizing Term Frequency–Inverse Document Frequency (TF-IDF) features, as well as transformer-based DistilBERT embeddings. The experimental results demonstrate that the HQDNN achieves a superior recall performance—92.58% with TF-IDF and 94.40% with DistilBERT—surpassing traditional machine learning models such as Logistic Regression, Linear SVM, and Multilayer Perceptron. Additionally, we compare the HQDNN with SetFit, a recent CPU-efficient few-shot transformer model, and show that while SetFit achieves higher precision, the HQDNN significantly outperforms it in recall. Furthermore, an ablation experiment confirms the critical contribution of the quantum component, revealing a substantial drop in performance when the quantum layer is removed. These findings highlight the potential of hybrid quantum–classical models as effective and compact alternatives for high-sensitivity classification tasks, particularly in domains such as fake news detection. Full article

We propose an amplitude encoding of the traveling salesperson problem along with a method for calculating the cost function using a probability distribution obtained on a quantum computer. Our encoding requires a number of qubits that grows logarithmically with the number of cities. We propose to calculate the cost function using a nonlinear function of expectation values of quantum operators. This is in contrast to the typical method of evaluating the cost function by summing expectation values of quantum operators. We demonstrate our method using a variational quantum eigensolver algorithm to find the shortest route for a given graph. We find that there is a broad range in the hyperparameters of the optimization procedure for which the best route is found. Full article

This article presents a comparative analysis of two types of generators of random sequences: one based on a discrete chaotic system being the logistic map, and the other being a commercial quantum random number generator QUANTIS-USB-4M. The results of the conducted analysis serve as a guide for selecting the type of generator that is more suited for a specific IoT solution, depending on the functional profile of the target application and the amount of random data required in the cryptographic process. This article discusses both the theoretical foundations of chaotic phenomena underlying the pseudorandom number generator based on the logistic map, as well as the theoretical principles of photon detection used in the quantum random number generators. A hardware IP Core implementing the logistic map was developed, suitable for direct implementation either as a standalone ASIC using the SkyWater PDK process or on an FPGA. The generated bitstreams from the implemented IP Core were evaluated for randomness. The analysis of the entropy levels and evaluation of randomness for both the logistic map and the quantum random number generator were performed using the ent tool and NIST test suite. Full article

Disaster logistics presents a highly complex decision-making challenge under conditions of uncertainty, where the timely and efficient allocation of scarce resources is essential to minimize human suffering. In this context, we propose a novel Quantum-Inspired Hyperheuristic Framework (QHHF) designed to solve Dynamic Multi-Objective Combinatorial Optimization Problems (DMOCOPs) arising in disaster relief operations. The proposed framework integrates Quantum-Inspired Evolutionary Algorithms (QIEAs), which facilitate diverse and explorative solution generation, with a Reinforcement Learning (RL)-based hyperheuristic capable of dynamically selecting the most suitable low-level heuristic in response to evolving disaster conditions. A dynamic multi-objective mathematical model is formulated to simultaneously minimize total travel cost and risk exposure, while maximizing priority-weighted demand satisfaction. The model captures real-world complexity through time-dependent variables, stochastic demand variations, and fluctuating transportation risks. Extensive simulations using real-world disaster scenarios demonstrate the effectiveness of the proposed approach in generating high-quality solutions within stringent response time constraints. Comparative evaluations reveal that QHHF consistently outperforms traditional heuristics and metaheuristics in terms of adaptability, scalability, and solution quality across multiple objective trade-offs. Notably, our method achieves a 9.6% reduction in total travel cost, a 6.5% decrease in cumulative risk exposure, and a 4.7% increase in priority-weighted demand satisfaction when benchmarked against existing techniques. This work contributes both to the advancement of hyperheuristic theory and to the development of practical, AI-enabled decision-support tools for emergency logistics management. Full article

In the digital information age, although digital images are widely used, the security issues associated with them have become increasingly severe. Consequently, ensuring secure image transmission has become a critical challenge in contemporary information security research. Chaotic systems are characterized by non-periodic behavior, strong dependence on initial conditions, and other favorable characteristics, and have been widely employed in the scrambling and diffusion processes of image encryption. Compared to classical chaotic maps, a quantum Logistic map exhibits better randomness and stronger sensitivity to initial values, effectively overcoming the attractor problem inherent in classical Logistic maps, thereby significantly enhancing the robustness of encryption methodologies. This article focuses on a innovative integration of a quantum Logistic map, hyper-chaotic Lorenz map, and DNA dynamic encoding technology, to design and implement a highly secure and efficient image encryption scheme. First, high-quality random number sequences are produced utilizing the quantum Logistic map, which is then employed to perform a scrambling operation on the image. Next, by integrating the chaotic sequences yielded from the hyper-chaotic Lorenz map with DNA dynamic encoding and operation rules, we implement a diffusion process, thereby increasing the strength of the image encryption. Experimental simulation results and multiple security analyses demonstrated that our encryption methodology achieved excellent encryption performance, effectively resisting a variety of attack strategies, and it holds significant potential for research on protecting image information through encryption. Full article

Background: Globally, neonatal fungal sepsis (NFS) is a leading cause of neonatal mortality, particularly among vulnerable populations in neonatal intensive care units (NICU). The use of spatial frailty models with a Bayesian approach to identify hotspots and risk factors for neonatal deaths due to fungal sepsis has not been explored before. Methods: A cohort of 80 neonates admitted to the NICU at a Government Hospital in Tamil Nadu, India and diagnosed with fungal sepsis through blood cultures between 2018–2020 was considered for this study. Bayesian spatial frailty models using parametric distributions, such as Log-logistic, Log-normal, and Weibull proportional hazard (PH) models, were employed to identify associated risk factors for NFS deaths and hotspot areas using the R version 4.1.3 software and QGIS version 3.26 (Quantum Geographic Information System). Results: The spatial parametric frailty models were found to be good models for analyzing NFS data. Abnormal levels of activated thromboplastin carried a significantly higher risk of death in neonates across all PH models (Log-logistic, Hazard Ratio (HR), 95% Credible Interval (CI): 22.12, (5.40, 208.08); Log-normal: 20.87, (5.29, 123.23); Weibull: 18.49, (5.60, 93.41). The presence of hemorrhage also carried a risk of death for the Log-normal (1.65, (1.05, 2.75)) and Weibull models (1.75, (1.07, 3.12)). Villivakkam, Tiruvallur, and Poonamallee blocks were identified as high-risk areas. Conclusions: The spatial parametric frailty models proved their effectiveness in identifying these risk factors and quantifying their association with mortality. The findings from this study underline the importance of the early detection and management of risk factors to improve survival outcomes in neonates with fungal sepsis. Full article

This study presents a novel encryption method for RGB (Red–Green–Blue) color images that combines scrambling techniques with the logistic map equation. In this method, image scrambling serves as a reversible transformation, rendering the image unintelligible to unauthorized users and thus enhancing security against potential attacks. The proposed encryption scheme, called Bit-Plane Representation of Quantum Images (BRQI), utilizes quantum operations in conjunction with a one-dimensional chaotic system to increase encryption efficiency. The encryption algorithm operates in two phases: first, the quantum image undergoes scrambling through bit-plane manipulation, and second, the scrambled image is mixed with a key image generated using the logistic map. To assess the performance of the algorithm, simulations and analyses were conducted, evaluating parameters such as entropy (a measure of disorder) and correlation coefficients to confirm the effectiveness and robustness of this algorithm in safeguarding and encoding color images. The results show that the proposed quantum color image encryption algorithm surpasses classical methods in terms of security, robustness, and computational complexity. Full article

Amplified spontaneous emission of CdTe and CdTe-Ag quantum dot composites were compared for increasing the optical stripe length, whereby optical gain coefficients for various emission wavelengths were obtained. In the case of CdTe-Ag nanoparticle composites, we observed that plasmonic coupling causes both optical enhancement and quenching at different wavelengths, where the amplified spontaneous emission intensity becomes enhanced at short wavelengths but suppressed at long wavelengths (>600 nm). To analyze the logistic stripe length dependence of amplified spontaneous emission intensity, we used a differential method to obtain the gain coefficient beyond the amplification range. This analysis enabled us to find the limit of the commonly used fitting method in terms of a threshold length and a saturation length, where amplification begins and saturation ends, respectively. Full article

The Gram-negative bacterium Helicobacter pylori (H. infection) infects the human stomach and is a major cause of gastritis, peptic ulcers, and gastric cancer. With over 50% of the global population affected, early and accurate diagnosis of H. infection infection is crucial for effective treatment and prevention of severe complications. Traditional diagnostic methods, such as endoscopy with biopsy, serology, urea breath tests, and stool antigen tests, are often invasive, costly, and can lack precision. Recent advancements in machine learning (ML) and quantum machine learning (QML) offer promising non-invasive alternatives capable of analyzing complex datasets to identify patterns not easily discernible by human analysis. This research aims to develop and evaluate HeliEns, a novel quantum hybrid ensemble learning algorithm designed for the early and accurate diagnosis of H. infection infection. HeliEns combines the strengths of multiple quantum machine learning models, specifically Quantum K-Nearest Neighbors (QKNN), Quantum Naive Bayes (QNB), and Quantum Logistic Regression (QLR), to enhance diagnostic accuracy and reliability. The development of HeliEns involved rigorous data preprocessing steps, including data cleaning, encoding of categorical variables, and feature scaling, to ensure the dataset’s suitability for quantum machine learning algorithms. Individual models (QKNN, QNB, and QLR) were trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. The ensemble model was then constructed by integrating these quantum models using a hybrid approach that leverages their diverse strengths. The HeliEns model demonstrated superior performance compared to individual models, achieving an accuracy of 94%, precision of 97%, recall of 92%, and an F1-score of 94% in detecting H. infection infection. The quantum ensemble approach effectively mitigated the limitations of individual models, providing a robust and reliable diagnostic tool. HeliEns significantly improved diagnostic accuracy and reliability for early H. infection detection. The integration of multiple quantum ML algorithms within the HeliEns framework enhanced overall model performance. The non-invasive nature of the HeliEns model offers a cost-effective and user-friendly alternative to traditional diagnostic methods. This research underscores the transformative potential of quantum machine learning in healthcare, particularly in enhancing diagnostic efficiency and patient outcomes. HeliEns represents a significant advancement in the early diagnosis of H. infection infection, leveraging quantum machine learning to provide a non-invasive, accurate, and reliable diagnostic tool. This research highlights the importance of QML-driven solutions in healthcare and sets the stage for future research to further refine and validate the HeliEns model in real-world clinical settings. Full article

Unmanned aerial vehicles (UAVs) play a critical role in various fields, including logistics, agriculture, and rescue operations. Effective identity authentication and key agreement schemes are vital for UAV networks to combat threats. Current schemes often employ algorithms like elliptic curve cryptography (ECC) and Rivest–Shamir–Adleman (RSA), which are vulnerable to quantum attacks. To address this issue, we propose LIGKYX, a novel scheme combining the quantum-resistant Kyber algorithm with the hash-based message authentication code (HMAC) for enhanced security and efficiency. This scheme enables the mutual authentication between UAVs and ground stations and supports secure session key establishment protocols. Additionally, it facilitates robust authentication and key agreement among UAVs through control stations, addressing the critical challenge of quantum-resistant security in UAV networks. The proposed LIGKYX scheme operates based on the Kyber algorithm and elliptic curve Diffie–Hellman (ECDH) key exchange protocol, employing the HMAC and pre-computation techniques. Furthermore, a formal verification tool validated the security of LIGKYX under the Dolev–Yao threat model. Comparative analyses on security properties, communication overhead, and computational overhead indicate that LIGKYX not only matches or exceeds existing schemes but also uniquely counters quantum attacks effectively, ensuring the security of UAV communication networks with a lower time overhead for authentication and communication. Full article

Quantum annealers are suited to solve several logistic optimization problems expressed in the QUBO formulation. However, the solutions proposed by the quantum annealers are generally not optimal, as thermal noise and other disturbing effects arise when the number of qubits involved in the calculation is too large. In order to deal with this issue, we propose the use of the classical branch-and-bound algorithm, that divides the problem into sub-problems which are described by a lower number of qubits. We analyze the performance of this method on two problems, the knapsack problem and the traveling salesman problem. Our results show the advantages of this method, that balances the number of steps that the algorithm has to make with the amount of error in the solution found by the quantum hardware that the user is willing to risk. The results are obtained using the commercially available quantum hardware D-Wave Advantage, and they outline the strategy for a practical application of the quantum annealers. Full article

This paper presents a quantum formulation for classical abstract dynamical systems (ADS), defined by coupled sets of first-order differential equations. They are referred to as “abstract” because their dynamical variables can be of different interrelated natures, not necessarily corresponding to physics, such as populations, socioeconomic variables, behavioral variables, etc. A classical linear Hamiltonian can be derived for ADS by using Dirac’s dynamics for singular Hamiltonian systems. Also, a corresponding first-order Schrödinger equation (which involves the existence of a system Planck constant particular of each system) can be derived from this Hamiltonian. However, Madelung’s reinterpretation of quantum mechanics, followed by the Bohm and Hiley work, produces no further information about the mathematical formulation of ADS. However, a classical quadratic Hamiltonian can also be derived for ADS, as well as a corresponding second-order Schrödinger equation. In this case, the Madelung reinterpretation of quantum mechanics provides a quantum Hamiltonian that does provide the quantum formulation for ADS, which provides new quantum variables interrelated dynamically with the classical variables. An application case is presented: the one-dimensional autonomous system given by the logistic dynamics. The differences between the classical and the quantum trajectories are highlighted in the context of this application case. Full article

Innovative technologies have been incorporated into intelligent transportation systems (ITS) to improve sustainability, safety, and efficiency, hence revolutionising traditional transportation. The combination of three-dimensional (3D) indoor building mapping and navigation is a groundbreaking development in the field of ITS. A novel methodology, the “Three-Dimensional Routing Information Framework “(3D RIF), is designed to improve indoor navigation systems in the field of ITS. By leveraging the Quantum Geographic Information System (QGIS), this framework can produce three-dimensional routing data and incorporate sophisticated routing algorithms to handle the complexities associated with indoor navigation. The paper provides a detailed examination of how the framework can be implemented in transport systems in urban environments, with a specific focus on optimising indoor navigation for various applications, including emergency services, tourism, and logistics. The framework includes real-time updates and point-of-interest information, thereby enhancing the overall indoor navigation experience. The 3D RIF’s framework boosts the efficiency and effectiveness of intelligent transportation services by optimising the utilisation of internal resources. The research outcomes are emphasised, demonstrating a mean enhancement of around 25.51% in travel. The measurable enhancement highlighted in this statement emphasises the beneficial influence of ITS on the efficiency of travel, hence underscoring the significance of the ongoing progress in this field. Full article

Quantum computing is emerging as a groundbreaking force, promising to redefine the boundaries of technology and business. This paper provides an in-depth examination of the quantum realm, beginning with its fundamental principles and extending to its implications for today’s industries. We discuss how quantum algorithms threaten existing cryptographic measures while also uncovering vast opportunities in sectors like finance, healthcare, and logistics. The narrative then shifts to the evolution of new business models, exemplified by Quantum-as-a-Service (QaaS) and enhanced AI capabilities. Alongside the myriad opportunities, we address the challenges and ethical concerns surrounding the swift rise of quantum technologies. By emphasizing the importance of collaborative efforts among businesses, policymakers, and technologists, the article advocates for a balanced and responsible approach to quantum adoption. Through this analytical lens, the article paints a comprehensive picture of the impending quantum era, presenting both its transformative potential and the complexities it brings to our interconnected world. Full article