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Quantum inspired meta-heuristic approaches for automatic clustering of colour images
In this article, quantum inspired incarnations of two swarm based meta-heuristic algorithms, namely, Crow Search Optimization Algorithm and Intelligent Crow Search Optimization Algorithm have been proposed for automatic clustering of colour images. The performance and effectiveness of the proposed algorithms have been judged by experimenting on 15 Berkeley images and five publicly available real life images of different sizes. The validity of the proposed algorithms has been justified with the help of four different cluster validity indices, namely, Pakhira Bandyopadhyay Maulik, I-index, Silhouette and CS-measure. Moreover, Sobol's sensitivity analysis has been performed to tune the parameters of the proposed algorithms. The experimental results prove the superiority of proposed algorithms with respect to optimal fitness, computational time, convergence rate, accuracy, robustness, (Formula presented.) -test and Friedman test. Finally, the efficacy of the proposed algorithms has been proved with the help of quantitative evaluation of segmentation evaluation metrics. 2021 Wiley Periodicals LLC -
Quantum leap in quick commerce: Harnessing quantum computing for sustainable and efficient logistics
This chapter explores how quantum computing can revolutionise the quick commerce industry, focusing on logistics and supply chain management to boost efficiency and sustainability. Quick commerce, an emerging trend in e-commerce, promises incredibly fast delivery speeds to satisfy ever-growing consumer expectations. But this rapid expansion isnt without its hurdles, particularly when it comes to maintaining smooth operations and being eco-friendly. Quantum computing steps in as a potential game-changer, bringing its powerful processing abilities to the table. Integrating quantum computing into quick commerce could transform logistics operations, from planning delivery routes to managing warehouse resources. Its not just about speeding things up; its about rethinking the entire supply chain, including how we handle inventory and the final leg of delivery. Quantum algorithms, which are built on the principles of quantum mechanics, can help companies predict demand more accurately, restock shelves faster, cut down on waste, and enhance overall efficiency. These algorithms are especially good at optimising routes in real time, considering various factors to ensure quicker, more dependable deliveries. This study aims to bridge the gap between the theory and practice of quantum computing in logistics. It examines how quantum computing can be used, its possible benefits, and the challenges it might face in the quick commerce sector. The chapter argues that quantum computing could usher in a new era of logistics management characterised by unprecedented efficiency in routing deliveries, controlling inventory, and allocating resources. Highlighting the use of quantum algorithms for dynamic routing and demand forecasting underscores the potential for creating a more agile and eco-friendly delivery system. Ultimately, this research shines a light on how we can turn the conceptual promise of quantum computing into real-world improvements in quick commerce logistics, advocating for a future where quantum computing leads to a sustainable and efficient quick commerce ecosystem. 2026 selection and editorial matter, Pushan Kumar Dutta, Pronaya Bhattacharya, Jai Prakash Verma, Ashok Chopra, Neel Kanth Kundu and Khursheed Aurangzeb; individual chapters, the contributors. -
Quantum machine learning
Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. New trends in Machine Learning based on Quantum Computing and Quantum Algorithms Examples on real life applications Illustrative diagrams and coding examples. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Quantum Machine Learning Models for Enhancing Big Data Analytics
The blistering growth of information in contemporary business is a great challenge to the traditional analytics in the context of speed, accuracy, and scalability. The Quantum Machine Learning (QML) has the chance to provide a ground-breaking solution, based on quantum superposition, entanglement execution to speed up a computational process and increase predictability. The current work proposed a Hybrid Quantum Classical Framework (HQCF) which is a combination of quantum algorithms and conventional machine learning to solve high-dimensional big data analytics. The proposed system shows huge performance improvements over classical foundations - attaining up to 10-percent higher prediction error, and cutting training costs by a factor of 47 percent in various fields such as finance, healthcare, and internet of things sensor information. The hybrid structure features high scalability as well which means that it can process datasets that are up to six million samples and thus it has a high level of scalability and strength. Such quantitative indicators indicate that quantum-enhanced analytic is technologically advanced or progressive to enhance computational-efficiency, generalization-of-models, and real-time ability to make a decision, with large-scale data settings. 2025 IEEE. -
Quantum mechanical and spectroscopic (FT-IR, FT-Raman,1H,13C NMR, UV-Vis) studies, NBO, NLO, HOMO, LUMO and Fukui function analysis of 5-Methoxy-1H-benzo[d]imidazole-2(3H)-thione by DFT studies
Theoretical analysis of the molecular structure, spectroscopic (FT-IR, FT-Raman, 1H, 13C NMR, UV-Vis) studies, and thermodynamic characteristics of 5-Methoxy-1H-benzo[d]imidazole-2(3H)-thione (5MBIT) molecule were done by DFT/B3LYP using 6-311++G(d, p) basis set. Theoretical parameters were compared with experimental data. The dipole moment (?), polarizability (??) and first order hyperpolarizability (?) of the molecule were calculated. Thermodynamic properties, HOMO and LUMO energies were determined. Global reactivity parameters and Fukui function of the 5MBIT molecule were predicted. 2016 Elsevier B.V. -
Quantum Network Attacks in Urban IoT Infrastructures
With urban spaces increasingly networked via the internet of things (IoT), the advent of quantum computing brings with it both potential and major security threats. Quantum network attacks are a serious risk to urban IoT systems by targeting weaknesses in conventional cryptographic protocols. In contrast to classical cyber threats, quantum-powered attacks can decrypt commonly used encryption schemes, leaving vital smart city networks, such as traffic management systems, power grids, and public surveillance, vulnerable. The emergent nature of quantum computing calls for an active response to securing urban IoT systems from prospective vulnerabilities that can impair critical services and undermine public security. The inherent complexity of urban IoT infrastructures renders them extremely susceptible to quantum-based cyberattacks. Quantum decryption, man-in-the-middle attacks, and quantum-boosted malware pose risks that can expose sensitive information and facilitate large-scale cyber intrusions. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Quantum optimization for machine learning
Machine learning is a branch of Artificial Intelligence that seeks to make machines learn from data. It is being applied for solving real world problems with huge amount of data. Though, Machine Learning is receiving wide acceptance, however, execution time is one of the major concerns in practical implementations of Machine Learning techniques. It largely comprises of a set of techniques that trains a model by reducing the error between the desired or actual outcome and an estimated or predicted outcome, which is often called as loss function. Thus, training in machine learning techniques often requires solving a difficult optimization problem, which is the most expensive step in the entire model-building process and its applications. One of the possible solutions in near future for reducing execution time of training process in Machine learning techniques is to implement them on quantum computers instead of classical computers. It is conjectured that quantum computers may be exponentially faster than classical computers for solving problems which involve matrix operations. Some of the machine learning techniques like support vector machines make extensive use of matrices, which can be made faster by implementing them on quantum computers. However, their efficient implementation is non-trivial and requires existence of quantum memories. Thus, another possible solution in near term is to use a hybrid of Classical Quantum approach, where a machine learning model is implemented in classical computer but the optimization of loss function during training is performed on quantum computer instead of classical computer. Several Quantum optimization algorithms have been proposed in recent years, which can be classified as gradient based and gradient free optimization techniques. Gradient based techniques require the nature of optimization problem being solved to be convex, continuous and differentiable otherwise if the problem is non-convex then they can find local optima only whereas gradient free optimization techniques work well even with non-continuous, non-linear and nonconvex optimization problems. This chapter discusses a global optimization technique based on Adiabatic Quantum Computation (AQC) to solve minimization of loss function without any restriction on its structure and the underlying model, which is being learned. Further, it is also shown that in the proposed framework, AQC based approach would be superior to circuit-based approach in solving global optimization problems. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Quantum tasks using six qubit cluster states
The usefulness of the recent experimentally realized six photon cluster state by C. Y. Lu et al. (Nature 3:91, 2007) is investigated for quantum communication protocols like quantum teleportation and quantum information splitting (QIS) and dense coding. We show that the present state can be used for the teleportation of an arbitrary two qubit state deterministically. Later, we devise two distinct protocols for the QIS of an arbitrary two qubit state among two parties. We construct sixteen orthogonal measurement basis on the cluster state, which will lock an arbitrary two qubit state among two parties. The capability of the state for dense coding is investigated and it is shown that one can send five classical bits by sending only three qubits using this state as a shared entangled resource.We finally show that this state can also be utilised in the remote state preparation of an arbitrary two qubit state. Springer Science+Business Media, LLC 2010. -
Quantum technologies outreach and AI
Quantum computing is one of the buzzing technologies in this modern computational era. Quantum computing is purely based on quantum mechanics as few dormant applications and advanced studies of quantum computers are integrated with quantum mechanics. This paper highlights the seven perspectives of quantum computing which is essential to get deep insights of quantum computing. The seven prerequisites are superposition, decoherence, entanglement, linear algebra, classical mechanics, quantum Fourier analysis and many body systems. The main objective of this paper is to find few stupendous impacts of this computing which will complement and explore various applications of artificial intelligence like weather pattern identification, traffic prediction, e-mail spam filtering, logistics optimisation, etc. This paper discusses visions of the top quantum computing companies and their contributions to quantum technologies. In this paper, a comparative analysis has been presented between quantum computing and classical computing. The major challenges which quantum computing faces have been addressed. Copyright 2025 Inderscience Enterprises Ltd. -
Quantum tunneling rotor as a sensitive atomistic probe of guests in a metal-organic framework
Quantum tunneling rotors in a zeolitic imidazolate framework ZIF-8 can provide insights into local gas adsorption sites and local dynamics of porous structure, which are inaccessible to standard physisorption or x-ray diffraction sensitive primarily to long-range order. Using in situ high-resolution inelastic neutron scattering at 3 K, we follow the evolution of methyl tunneling with respect to the number of dosed gas molecules. While nitrogen adsorption decreases the energy of the tunneling peak, and ultimately hinders it completely (0.33 meV to zero), argon substantially increases the energy to 0.42 meV. Ab initio calculations of the rotational barrier of ZIF-8 show an exception to the reported adsorption sites hierarchy, resulting in anomalous adsorption behavior and linker dynamics at subatmospheric pressure. The findings reveal quantum tunneling rotors in metal-organic frameworks as a sensitive atomistic probe of local physicochemical phenomena. 2023 authors. Published by the American Physical Society. -
Quantum vs. Classical: A Rigorous Comparative Study on Neural Networks for Advanced Satellite Image Classification
Navigating the intersection of quantum computing and classical machine learning in image classification, this study confronts prevailing challenges. Centered on the "Satellite Image dataset (RSI-CB256),"our investigation probes the early phases of quantum architectures, utilizing simulations to transform numerical data into a quantum format, the investigation highlights the existing limitations in traditional classical methodologies for image classification tasks. In light of the groundbreaking possibilities presented by quantum computing, this study underscores the need for creative solutions to push image classification beyond the usual methods. Additionally, the study extends beyond conventional CNNs, incorporating Quantum Machine Learning through the Qiskit framework. This dualparadigm approach not only underscores the limitations of current classical machine learning methods but also sets the stage for a more profound understanding of the challenges that quantum methodologies aim to address. The research offers valuable insights into the ongoing evolution of quantum architectures and their potential impact on the future landscape of image classification and machine learning. 2024 IEEE. -
Quantum-Assisted Metaheuristics for Adaptive Resource Allocation in 6G Networks
With 6G wireless communication systems, the level of demands is now ultra-low-latency, connectivity of devices in large numbers, and flexible spectrum utilization. To resolve these issues, in the current paper, the Quantum-Assisted Metaheuristic (QAM) framework is proposed that combines quantum-inspired operators and traditional metaheuristic methods of adaptive resource allocation in 6G networks. This framework uses quantum-enhanced exploration, dynamically tuned parameters and hybrid quantum-classical computing to trade-off scalability against efficiency, depending on the traffic and channel conditions. Oh! SIM Evaluating a simulated 6G environment, it proves that QAM can be used to improve spectral efficiency by up to 28 percent and the allocation latency by 31 percent over the state-of-the-art metaheuristics, and still treats users of varying densities fairly with a fairness index of 0.94. The results demonstrate the strength and extensiveness of the QAM, and makes it a viable solution to the efficient and intelligent management of resources in next generation wireless networks. 2025 IEEE. -
Quantum-Driven Digital Forensics: Evidence Acquisition, Intrusion Detection, Cybercrime Simulation, andDNA Profiling
Quantum computing introduces a new paradigm in digital forensics by enabling faster cryptographic analysis, enhanced machine learning, and secure data acquisition. This research examines the potential to apply quantum computing to forensics and how it can be used to transform the field through its disruptive capabilities in the evidence collection process, detection of intrusions, modeling of cybercrime, and DNA analysis. It also underrates the dangers that quantum technologies bring to the data security and the urgency of post-quantum encryption technologies. The article presents a blueprint of quantum-driven forensic investigation of the near future by conducting a survey of recent advances and new applications. We combine theory and practice by using datasets such as NSL-KDD, Qiskit simulations, and diagrams of how quantum machine learning models, DNA profiling and intrusion detection systems are used. Pattern matching in the DNA profiling algorithm with quantum computing is determined to have a time complexity of O(n) in the application of the Grover algorithm and O(n) of the corresponding classical algorithm. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Quantum-Driven Finance Transforming Banking Through Next-Generation Technologies
The swift progress of quantum computing is set to revolutionize the financial sector, especially in the fields of risk management and portfolio optimization. Existing financial models, though effective in some measure, are unable to handle the huge complexities of today's markets, where high-frequency trading, nonlinear interdependencies, and complex risk factors require advanced computational capabilities. Quantum finance, a new multidisciplinary research area, uses quantum computing concepts to improve financial decision-making, investment strategy optimization, and risk reduction more effectively than traditional techniques. This chapter discusses how quantum computing is revolutionizing risk management and portfolio optimization using quantum mechanics-based algorithms like quantum annealing, quantum Monte Carlo simulations, and variational quantum eigensolvers (VQEs). These methods enable financial institutions to resolve high-dimensional optimization problems exponentially quicker, detect more optimal risk-adjusted portfolios, and build predictive models with higher accuracy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Quantum-Enhanced Cryptographic Key Exchange for Secure IoT Networks
The coupling of quantum-metering methods with cryptographic key exchanges spells out a new security paradigm model to safeguard Internet of Things (IoT) networks against rapidly changing cyberspace threats. The given assessment explains a quantum-advanced scenario that is operationally feasible and serves as a platform for quantum key distribution (QKD) along with classical post-quantum algorithms to deliver end-to-end confidentiality, integrity, and authentication features to a wide range of IoT devices. The model, by the virtue of quantum entanglement and photon polarization used in the creation of tamper-evident communication links, is invulnerable to adversarial-aided eavesdropping and computational assault methods and further offers a hybrid encryption protocol that has been demonstrated to alleviate key generation and exchange latency trade-offs - simultaneously maintaining scalability and effectiveness in resource-constrained IoT node environments. The key compromise probability is significantly lowered in the experimental results, along with the keys' entropy levels, which were generated in the pure QKD space as opposed to classicalbased RSA and ECC methods. Also, the framework investigates lightweight quantum-safe authentication techniques that can be used to establish trust at the device level. The output points to the enhanced resistance to quantum and classical attackers that can make the solutions work in real-time application IoT environments such as smart healthcare, autonomous systems, and industrial automation. Overall, the quantum-enriched model of cryptographic key exchange is next-gen IoT ecosystem to be implemented subsequently. 2026 IEEE. -
Quantum-enhanced neuro-fusion framework for intelligent decision-making in smart home IoT surveillance
Smart-home surveillance systems increasingly rely on heterogeneous IoT data streams, requiring efficient fusion, scalability, and robustness under noisy sensing conditions. This paper proposes a Quantum-Inspired Deep Neuro-Fusion Architecture (QDNFA) for anomaly detection in edgecloud IoT environments. The framework integrates modular encoders, temporal alignment, and a quantum-inspired optimisation mechanism to support multi-modal data processing while maintaining real-time performance. Experimental evaluation is conducted on the CASAS Smart Home dataset to validate sensor-centric anomaly detection, scalability across multiple devices, and edgecloud inference efficiency. While the architecture is designed to support audio and video modalities, the present study focuses on low-dimensional sensor data, and large-scale benchmarking on audiovisual surveillance datasets is identified as future work. Results demonstrate improved detection accuracy and reduced latency compared to baseline methods in sensor-driven smart-home scenarios. 2026 The Author(s). -
Quantum-inspired algorithms for cognitive computing: Enhancing cloud-based problem-solving
The convergence of quantum-inspired algorithms and cloud-based frameworks represents a transformative shift in computational capabilities tailored to the human-centric goals of Industry 5.0. Unlike Industry 4.0, which focused on automation and digitization, Industry 5.0 emphasizes intelligent systems that complement human decision-making. Quantum-inspired algorithms, derived from the principles of superposition and entanglement, offer superior capabilities in optimization and pattern recognition without requiring quantum hardware. When integrated with scalable and distributed cloud computing infrastructures, these algorithms enable high-performance cognitive computing, tackling previously intractable problems across domains. This chapter explores the theoretical foundation and practical implementation of such systems, including quantum-inspired neural networks (QiNNs), quantum-inspired immune algorithms (QiIAs), and quantum-inspired particle swarm optimization (QPSO). These models exhibit enhanced accuracy and efficiency in applications like pattern recognition, anomaly detection, and multiobjective optimization. Real-world case studies in finance, cybersecurity, healthcare, and smart grid management highlight their impact on risk modeling, threat mitigation, and decision support systems. The chapter further proposes a cloud integration framework, addressing challenges in scalability, performance, and security. Implementation strategies and architectural designs are discussed with a focus on dynamic resource management, real-time analytics, and secure deployment. The synthesis of these technologies marks a significant advancement toward achieving adaptive, intelligent, and secure computational ecosystems, aligned with the values and vision of Industry 5.0. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Quantum-Inspired Genetic Algorithms for Secure and Scalable Cloud-Based Decision Support Systems
Cloud-based DSS are critical for data-intensive decision-making but tremendously challenged by issues of scalability, security, and the optimization of resources. In general, optimization approaches such as GA, PSO, and ACO treat the problems of allocation and security enhancement of cloud resources very inadequately. Hence, the present work addresses developing a QIGA-based optimization framework for the performance optimization of cloud DSS. At its core, it utilizes quantum-like principles, such as superposition and probabilistic search, for resource optimization with respect to stability, security, and rapid convergence. Therefore, this underpinning framework comprises resource optimization, anomalous detection, and quantum-independent encryption through QIGA, which enhances the data security along with computational efficiency. The experimental results depict the performance efficiency of QIGA since its execution time, CPU, memory utilization requirements, and energy consumption are less while the task completion rate is higher and the security vulnerabilities are reduced in comparison to traditional optimization techniques. QIGA-based anomaly detection improves accuracy at the expense of response time, while its quantum-inspired encryption provides the best cryptographic security. Therefore, these results verify that QIGA is an efficient secured methodology for scalable cloud-controlled DSS, hence being a potential candidate for decision-making optimization in highly dynamic cloud environments. 2025 IEEE. -
Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem
Portfolio optimization has long been a challenging proposition and a widely studied topic in finance and management. It involves selecting and allocating the right assets according to the desired objectives. It has been found that this nonlinear constraint problem cannot be effectively solved using a traditional approach. This paper covers and compares quantum-inspired versions of four popular evolutionary techniques with three benchmark datasets. Genetic algorithm, differential evolution, particle swarm optimization, ant colony optimization, and their quantum-inspired incarnations are implemented, and the results are compared. Experiments have been carried out with more than 10 years of stock price data from NASDAQ, BSE, and Dow Jones. This work proposes several enhancements to allocate funds efficiently, such as improved crossover techniques and dynamic and adaptive selection of parameters. Furthermore, it is observed that the quantum-inspired techniques outperform the classical counterparts. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Quantum?Inspired Strategies and Business Resilience in Industry 5.0: A Study of Small Businesses
The discussion of the study revolves around the assessment of the various quantum strategies influencing the resilience of a business in Industry 5.0. Quantum strategy implementation (QSI), technological readiness (TR), superposition capability (SPC), innovation capacity (IC), leadership style (LS) and organisational culture (OC) were identified as independent variables which might have a significant influence on business resilience (BR), the dependent variable for the study. Moreover, industry volatility (IV) is taken as a mediating variable. Questionnaire was framed involving the 5-point Likert scale, and data collection was carried out. Among 250 responses, 32 was either incomplete or invalid. Final determination of sample size was 218. Stratified random sampling technique was adopted to ensure different industries and geographical locations were covered. Pilot testing with the 30 initial responses was carried out for better clarity and validity before full deployment. SPSS 25 and AMOS 23 were the statistical tools involved to analyse the data. Reliability and validity check, correlation, regression, confirmatory factor analysis, mediation analysis and structural equation modelling (SEM) are the statistical methods used in the study. Highest correlation is seen among IC and BR. Regression results show that IV and LS have an insignificant impact on the BR. Results of SEM show the index values are well within the recommended value range indicating a good model fit. With the results, small businesses may adapt to rapid technological changes and increase resilience. 2026 Saladi Jaswanth Seshasai, R. Vijay Raja and G. Kumar