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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 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 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 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 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 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 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 inspired automatic clustering algorithms: A comparative study of genetic algorithm and bat algorithm
This article is intendant to present two automatic clustering techniques of image datasets, based on quantum inspired framework with two different metaheuristic algorithms, viz., Genetic Algorithm (GA) and Bat Algorithm (BA). This work provides two novel techniques to automatically find out the optimum clusters present in images and also provides a comparative study between the Quantum Inspired Genetic Algorithm (QIGA) and Quantum Inspired Bat Algorithm (QIBA). A comparison is also presented between these quantum inspired algorithms with their analogous classical counterparts. During the experiment, it was perceived that the quantum inspired techniques beat their classical techniques. The comparison was prepared based on the mean values of the fitness, standard deviation, standard error of the computed fitness of the cluster validity index and the optimal computational time. Finally, the supremacy of the algorithms was verified in terms of the p-value which was computed by t-test (statistical superiority test) and ranking of the proposed procedures was produced by the Friedman test. During the computation, the betterment of the fitness was judge by a well-known cluster validity index, named, DB index. The experiments were carried out on four Berkeley image and two real life grey scale images. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Quantum Information Processing for Legal Applications through Bloch Sphere of Law
The objective of the research work is to propose a quantum information processing model (QIP) for legal applications including litigation and investigation phases. The quantum information processing and quantum computing concepts can be visualized within a Bloch Sphere of Law (BSL) as legal Bloch vectors (LBV) as quantum computing entities. This quantum approach is needed since the complexity of legalities and the legal objects involved in the final judgement are to be reversible with a lot of uncertainties. The reasoning and prosecution through various trials and investigations are to be considered as mathematical matrix or unitary operations in this muti dimensional legal space. The mapping of legal information into technical and then vectorial representations are deployed through a glossary of legal terms in this quantum paradigm. As a forerunning study and application in the quantum paradigm, mathematical and computational models have been proposed in the work with a case study of a recent civil case. 2022 IEEE. -
Quantum fractional order Darwinian particle swarm optimization for hyperspectral multi-level image thresholding
A Hyperspectral Image (HSI) is a data cube consisting of hundreds of spatial images. Each captured spatial band is an image at a particular wavelength. Thresholding of these images is itself a tedious task. Two procedures, viz., Qubit Fractional Order Particle Swarm Optimization and Qutrit Fractional Order Particle Swarm Optimization are proposed in this paper for HSI thresholding. The Improved Subspace Decomposition Algorithm, Principal Component Analysis, and a Band Selection Convolutional Neural Network are used in the preprocessing stage for band reduction or informative band selection. For optimal segmentation of the HSI, modified Otsu's criterion, Masi entropy and Tsallis entropy are used. A new method for quantum disaster operation is implemented to prevent the algorithm from getting stuck into local optima. The implementations are carried out on three well known datasets viz., the Indian Pines, the Pavia University and the Xuzhou HYSPEX. The proposed methods are compared with state-of-the-art methods viz., Particle Swarm Optimization (PSO), Ant Colony Optimization, Darwinian Particle Swarm Optimization, Fractional Order Particle Swarm Optimization, Exponential Decay Weight PSO and Heterogeneous Comprehensive Learning PSO concerning the optimal thresholds, best fitness value, computational time, mean and standard deviation of fitness values. Furthermore, the performance of each method is validated with Peak signal-to-noise ratio and SensenDice Similarity Index. The KruskalWallis test, a statistical significance test, is conducted to establish the superiority in favor of the proposed methods. The proposed algorithms are also implemented on some benchmark functions and real life images to establish their universality. 2021 Elsevier B.V. -
Quantum cryptography: An in-depth exploration of principles and techniques
Quantum cryptography is evolving in the field of data security and cryptographic research, as it offers a high level of security based on the principles of quantum mechanics. This chapter provides an extensive understanding and in-depth explanation about the basic concepts of the techniques implemented in quantum cryptography. The exploration of the fundamental concepts begins with elaboration on the foundational concepts of quantum mechanics, such as no-cloning, entanglement, superposition, and quantum state measurement, which are crucial for the better understanding of quantum cryptography. Further, the chapter delves more into the quantum key distribution (QKD) protocols such as BB84, BBM92, and B92. All the QKD protocols are analysed and compared based on the underlying principles and techniques. Furthermore, the importance and benefits of the integration of quantum cryptography with the traditional algorithms are also discussed. The chapter also aims to provide thorough study of quantum cryptography principles, challenges, and future directions along with a detailed comprehensive review of quantum cryptographic techniques. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Quantum Convolutional Neural Network for Medical Image Classification: A Hybrid Model
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) in the realm of image classification, particularly focusing on datasets with a highly reduced number of features. We investigate the potential quantum computing holds in processing and classifying image data efficiently, even with limited feature availability. This research investigates QCNNs' application within a highly constrained feature environment, using chest X-ray images to distinguish between normal and pneumonia cases. Our findings demonstrate QCNNs' utility in classifying images from the dataset with drastically reduced feature dimensions, highlighting QCNNs' robustness and their promising future in machine learning and computer vision. Additionally, this study sheds light on the scalability of QCNNs and their adaptability across various training-test splits, emphasizing their potential to enhance computational efficiency in machine learning tasks. This suggests a possibility of paradigm shift in how we approach data-intensive challenges in the era of quantum computing. We are looking into quantum paradigms like Quantum Support Vector Machine (QSVM) going forward so that we can explore trade offs effectiveness of different classical and quantum computing techniques. 2024 IEEE. -
Quantum Computings Path toSupremacy: Progress in the NISQ Epoch
Quantum computing leverages the principles of quantum mechanics for information processing, with qubits serving as the fundamental units of quantum information. Qubits are quantum states where information processing can be engineered. Qubits possess the unique ability to encode, manipulate and extract information, enabling remarkable parallelism in computation. This enhanced computational speed, called quantum supremacy, promises to transcend established complexity boundaries. Significant strides have been made in demonstrating quantum supremacy through various experiments, most notably Googles 2019 experiment utilizing the Sycamore quantum processor to solve a problem that would stymie classical supercomputers for millennia. Other research groups, such as the Chinese team employing Jiuzhang and Zuchongzhi quantum processors, have achieved similar feats, showcasing the profound computational capabilities of quantum computers. It is essential to underscore that quantum supremacy does not signify quantum computers superiority across all tasks; current quantum computers remain constrained in their applicability, excelling primarily in specific problem domains. Nevertheless, recent advancements in quantum computing are noteworthy and ongoing development promises to expand their problem-solving capacities. This paper offers an introductory overview of quantum computing and an assessment of three prominent quantum supremacy experiments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Quantum Computing: Navigating The Technological Landscape for Future Advancements
Quantum Computing represents a transformative paradigm in information processing, leveraging principles of quantum mechanics to enable computations that transcend the limitations of classical computing. This research paper explores the cutting-edge technologies employed in Quantum Computing, examining the key components that facilitate quantum information processing.The purpose of this study is to provide a comprehensive exploration of the state-of-the-art technologies in Quantum Computing, laying the groundwork for future advancements and applications in this rapidly evolving field.The methodology employed in this study integrates three analytical approaches: sentiment analysis, topic modeling, and thematic analysis. Sentiment analysis is utilized to discern and quantify emotional tones within the content. Topic modeling is applied to identify latent themes and patterns within the data, revealing underlying structures. Thematic analysis, on the other hand, involves a systematic identification and exploration of recurrent themes to provide a nuanced understanding of the subject matter. This tripartite methodology ensures a comprehensive examination of the data, facilitating a robust and multifaceted analysis of quantum computing technologies. 2024 IEEE. -
Quantum computational, solvation and in-silico biological studies of a potential anti-cancer thiophene derivative
Heterocyclic molecules display a wide spectrum of properties that span both material and biological domains. Material properties stem from their interactions in the bulk, where a large number of molecules of the same type get together resulting in an enhancement of properties. However, biological properties emanate from the interaction of a single or a few molecules with a biologically functional macromolecule. Computational tools offer a particularly useful way of theoretically studying molecules to arrive at a conclusion regarding such properties, even though they may vary when experimentally evaluated. This study concerns itself with the theoretical investigation comprising density functional theory calculations, topological analyses and in-silico biological evaluation of a thiophene compound, i.e. the title compound. Density functional theory was used to compute properties of the title molecule and their variations in unsolvated and solvated phases using Gaussian 09. The molecule in solvent phases encompassing organic polar protic, organic polar aprotic and inorganic polar protic nature have been subjected to theoretical investigations. The suitability of the molecule for deployment as a modern optical material is examined with positive results. Topological characteristics of the molecule were evaluated using Multiwfn 3.8 to examine electron density distribution and the possible resulting covalent, non-covalent and weak interactions because of such distribution. The potency of the molecule towards brain cancer was evaluated by molecular docking with Auto Dock Tools against two brain cancer protein targets 6ETJ and 6YPE with a good docking score of ?6.63 and ?6.21 kcal mol?1 respectively and the resulting interactions visualized and its pharmacokinetic properties obtained using online tools. 2024 Elsevier B.V. -
Quantum approaches to sustainable resource management in supply chains
Quantum computing leverages the principles of quantum mechanics to process information in ways that classical computers cannot. This capability is particularly advantageous for solving complex optimization problems that are common in supply chain management. Quantum algorithms, such as the quantum approximate optimization algorithm (QAOA) and quantum annealing, have shown promise in efficiently solving these problems by exploring numerous potential solutions simultaneously and identifying optimal strategies. The purpose of this chapter is to investigate the rapidly developing topic of quantum computing and its potential applications in managing sustainable resources within supply chains. Traditional resource allocation methods often struggle to maximize efficiency while minimizing environmental impact. However, new developments in quantum computing have opened up potentially fruitful pathways for addressing these issues. This study aims to explore how quantum computing can revolutionize through an examination of quantum algorithms, optimization approaches, and case studies. 2024 by IGI Global. All rights reserved. -
Quantum Algorithm: A Classical Realization in High-Performance Computing Using MPI
Volume3, Special Issue3 ISSN: 23198753 -
Quantitative X-ray and Spectroscopic Analysis of Nanocrystalline and Amorphous Carbon Materials
Carbon and its various allotropic forms is a blooming and extensively investigated field for the past few decades. The revolution which started with the discovery of fullerenes in 1985 continues with the newly discovered wonder material graphene and has never failed to amass the interest of scientific community. After all these years it still stays as a hot topic of research. This is primarily due to their unique physical and chemical properties which makes them suitable for a whole host of applications ranging from thin film technology to nano-medicine. But, the production cost of these novel materials is an issue which shadows its glory and hence it is essential to find out efficient and cost effective sources and production methods for these materials. Graphene oxide has attracted much interest because of its low cost, easy access and unique ability to get converted into graphene. Graphene oxide is basically, a graphene sheet which consists of either carboxyl or hydroxyl groups. Foreseeing the upcoming era of carbon nanomaterials on account of their revolutionary applications and the ever increasing demand for economical and viable sources, we have identified and explored the structural parameters of an efficient and cost effective precursor of the same. In the present investigation, wood charcoal and coconut shell charcoal, which is a superior source of activated carbon, is produced by a slow thermal decomposition method in a limited supply of oxygen. It is an impure form of carbon- is a black residue composed mainly of carbon, ash and char. Wood charcoal is transformed into Graphite oxide (GO) by a modified Hummers method. Spectroscopic analysis of the samples is carried out by various techniques such as X-ray diffraction (XRD), Raman Spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X-ray Photoelectron Spectroscopy (XPS), UV-Vis spectroscopy and Scanning Electron Microscopy (SEM). The various structural parameters are calculated from XRD and Raman data. -
Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow
As there has been a rise in the usage of in silico approaches, for assessing the risks of harmful chemicals upon animals, more researchers focus on the utilization of Quantitative Structure Activity Relationship models. A number of machine learning algorithms link molecular descriptors that can infer chemical structural properties associated with their corresponding biological activity. Efficient and comprehensive computational methods which can process huge set of heterogeneous chemical datasets are in demand. In this context, this study establishes the usage of various machine learning algorithms in predicting the acute aquatic toxicity of diverse chemicals on Fathead Minnow (Pimephales promelas). Sample drive approach is employed on the train set for binning the data so that they can be located in a domain space having more similar chemicals, instead of using the dataset that covers a wide range of chemicals at the entirety. Here, bin wise best learning model and subset of features that are minimally required for the classification are found for further ease. Several regression methods are employed to find the estimation of toxicity LC50 value by adopting several statistical measures and hence bin wise strategies are determined. Through experimentation, it is evident that the proposed model surpasses the other existing models by providing an R2 of 0.8473 with RMSE 0.3035 which is comparable. Hence, the proposed model is competent for estimating the toxicity in new and unseen chemical. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Quantitative assessment of blockchain applications for Industry 4.0 in manufacturing sector
Blockchain is one of the emerging digital technologies that will play a role in the breakthroughs of the fourth industrial revolution. The use of blockchain technology has the potential to greatly benefit businesses of all sizes by increasing their data's integrity, privacy, and openness. The term Industry 4.0? refers to the amalgamation of recent advances in manufacturing technology that have helped businesses cut production times significantly. The industrial and supply chain industries can benefit from these technological advancements in a number of ways. Increased efficiency in production and a more stable supply chain are just two of the many benefits that blockchain promises to bring to the manufacturing industry. The study focuses on Blockchain's huge potential in the context of Industry 4.0. Understanding the role of Blockchain technology in Industry 4.0 is examined, along with its various drivers, enablers, and associated capabilities. The several sub-domains of Industry 4.0 that can benefit from the implementation of Blockchain technology are also covered. The present research is primary and exploratory in nature. The sample size of the study is 256. The responses obtained from workers working in manufacturing sector in Delhi/NCR. The responses from workers obtained through structured questionnaire. The several sub-domains of Industry 4.0 found that can benefit from the implementation of Blockchain technology. At last, the existing study found the most important uses of Blockchain technology in the fourth industrial revolution. 2023