M. L. Sworna Kokila, V. Bibin Christopher, G. Ramya
Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual‐Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC‐PCSANN‐CHIO‐FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC‐ANN‐FD), electrical power system fault diagnostics using hybrid quantum‐classical deep learning (QC‐CRBM‐FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC‐RF‐FD).
{"title":"Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems","authors":"M. L. Sworna Kokila, V. Bibin Christopher, G. Ramya","doi":"10.1049/qtc2.12106","DOIUrl":"https://doi.org/10.1049/qtc2.12106","url":null,"abstract":"Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual‐Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC‐PCSANN‐CHIO‐FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC‐ANN‐FD), electrical power system fault diagnostics using hybrid quantum‐classical deep learning (QC‐CRBM‐FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC‐RF‐FD).","PeriodicalId":507937,"journal":{"name":"IET Quantum Communication","volume":"29 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sujit Biswas, R. Goswami, K. Hemant Kumar Reddy, S. Mohanty, M. A. Ahmed
The integration of lattice‐based cryptography principles with Quantum Key Distribution (QKD) protocols is explored to enhance security in the context of Internet of Things (IoT) ecosystems. With the advent of quantum computing, traditional cryptographic methods are increasingly susceptible to attacks, necessitating the development of quantum‐resistant approaches. By leveraging the inherent resilience of lattice‐based cryptography, a synergistic fusion with QKD is proposed to establish secure and robust communication channels among IoT devices. Through comprehensive Qiskit simulations and theoretical analysis, the feasibility, security guarantees, and performance implications of this novel hybrid approach are thoroughly investigated. The findings not only demonstrate the efficacy of lattice‐based QKD in mitigating quantum threats, but also highlight its potential to fortify IoT communications against emerging security challenges. Moreover, the authors provide valuable insights into the practical implementation considerations and scalability aspects of this fusion approach. This research contributes to advancing the understanding of quantum‐resistant cryptography for IoT applications and paves the way for further exploration and development in this critical domain.
{"title":"Exploring the fusion of lattice‐based quantum key distribution for secure Internet of Things communications","authors":"Sujit Biswas, R. Goswami, K. Hemant Kumar Reddy, S. Mohanty, M. A. Ahmed","doi":"10.1049/qtc2.12105","DOIUrl":"https://doi.org/10.1049/qtc2.12105","url":null,"abstract":"The integration of lattice‐based cryptography principles with Quantum Key Distribution (QKD) protocols is explored to enhance security in the context of Internet of Things (IoT) ecosystems. With the advent of quantum computing, traditional cryptographic methods are increasingly susceptible to attacks, necessitating the development of quantum‐resistant approaches. By leveraging the inherent resilience of lattice‐based cryptography, a synergistic fusion with QKD is proposed to establish secure and robust communication channels among IoT devices. Through comprehensive Qiskit simulations and theoretical analysis, the feasibility, security guarantees, and performance implications of this novel hybrid approach are thoroughly investigated. The findings not only demonstrate the efficacy of lattice‐based QKD in mitigating quantum threats, but also highlight its potential to fortify IoT communications against emerging security challenges. Moreover, the authors provide valuable insights into the practical implementation considerations and scalability aspects of this fusion approach. This research contributes to advancing the understanding of quantum‐resistant cryptography for IoT applications and paves the way for further exploration and development in this critical domain.","PeriodicalId":507937,"journal":{"name":"IET Quantum Communication","volume":"137 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adarsh Kumar, Mustapha Hedabou, Diego Augusto de Jesus Pacheco
Quantum calculi and formalisms are useful tools for ensuring security and computational capabilities in blockchain and cryptography. They aid in designing and analysing new cryptographic protocols for blockchain, determining the behaviour of quantum operations in blockchain‐based smart contracts, assessing the feasibility and security of quantum algorithms in blockchain applications, and building a quantum‐safe blockchain system. A comprehensive review of the applications of quantum calculi and formalisms in computer security and network security, along with a bibliographic analysis is presented. It is unique in that it combines bibliometric analyses with a technical review of the domain of quantum calculi and formalism. Bibliometric and biographic analysis in the field helps identify research trends, assess the influence of research, determine collaboration patterns, evaluate journals, and examine publication behaviours, among other things. It performs bibliographic and bibliometric analysis using a dataset collected from Scopus and Web of Science through different queries. The obtained results help identify important institutions, authors, organisations, collaboration networks, keywords, and more. The provided open challenges and future vision pave the way for further research in the direction of quantum calculi and formalism applications in computer security and network security.
量子计算和形式主义是确保区块链和密码学安全性和计算能力的有用工具。它们有助于为区块链设计和分析新的加密协议，确定基于区块链的智能合约中的量子操作行为，评估区块链应用中量子算法的可行性和安全性，以及构建量子安全的区块链系统。本书全面回顾了量子计算和形式主义在计算机安全和网络安全中的应用，并进行了文献分析。它的独特之处在于将文献计量分析与量子计算和形式主义领域的技术综述相结合。该领域的文献计量和传记分析有助于确定研究趋势、评估研究影响、确定合作模式、评估期刊和检查出版行为等。它利用从 Scopus 和 Web of Science 收集的数据集，通过不同的查询进行书目和文献计量分析。获得的结果有助于识别重要的机构、作者、组织、合作网络、关键词等。提供的公开挑战和未来愿景为计算机安全和网络安全领域量子计算和形式主义应用方向的进一步研究铺平了道路。
{"title":"Quantum calculi and formalisms for system and network security: A bibliographic insights and synoptic review","authors":"Adarsh Kumar, Mustapha Hedabou, Diego Augusto de Jesus Pacheco","doi":"10.1049/qtc2.12102","DOIUrl":"https://doi.org/10.1049/qtc2.12102","url":null,"abstract":"Quantum calculi and formalisms are useful tools for ensuring security and computational capabilities in blockchain and cryptography. They aid in designing and analysing new cryptographic protocols for blockchain, determining the behaviour of quantum operations in blockchain‐based smart contracts, assessing the feasibility and security of quantum algorithms in blockchain applications, and building a quantum‐safe blockchain system. A comprehensive review of the applications of quantum calculi and formalisms in computer security and network security, along with a bibliographic analysis is presented. It is unique in that it combines bibliometric analyses with a technical review of the domain of quantum calculi and formalism. Bibliometric and biographic analysis in the field helps identify research trends, assess the influence of research, determine collaboration patterns, evaluate journals, and examine publication behaviours, among other things. It performs bibliographic and bibliometric analysis using a dataset collected from Scopus and Web of Science through different queries. The obtained results help identify important institutions, authors, organisations, collaboration networks, keywords, and more. The provided open challenges and future vision pave the way for further research in the direction of quantum calculi and formalism applications in computer security and network security.","PeriodicalId":507937,"journal":{"name":"IET Quantum Communication","volume":"10 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arecanut X‐ray images accurately represent their internal structure. A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. The investigation explores various transfer learning models with different sizes to identify the most suitable one for achieving enhanced accuracy. The Shufflenet model with a scale factor of 2.0 attains the highest classification accuracy of 97.72% using the QCNN approach, with a model size of 28.40 MB. Out of the 12 transfer learning models tested, 9 exhibit improved classification accuracy when using QCNN models compared to the traditional CNN‐based transfer learning approach. Consequently, the exploration of CNN and QCNN‐based classification reveals that QCNN outperforms traditional CNN models in accuracy within the transfer learning framework. Further experiments with qubits suggest that utilising 4 qubits is optimal for classification operations in this context.
{"title":"Quantum‐inspired Arecanut X‐ray image classification using transfer learning","authors":"Praveen M Naik, Bhawana Rudra","doi":"10.1049/qtc2.12099","DOIUrl":"https://doi.org/10.1049/qtc2.12099","url":null,"abstract":"Arecanut X‐ray images accurately represent their internal structure. A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. The investigation explores various transfer learning models with different sizes to identify the most suitable one for achieving enhanced accuracy. The Shufflenet model with a scale factor of 2.0 attains the highest classification accuracy of 97.72% using the QCNN approach, with a model size of 28.40 MB. Out of the 12 transfer learning models tested, 9 exhibit improved classification accuracy when using QCNN models compared to the traditional CNN‐based transfer learning approach. Consequently, the exploration of CNN and QCNN‐based classification reveals that QCNN outperforms traditional CNN models in accuracy within the transfer learning framework. Further experiments with qubits suggest that utilising 4 qubits is optimal for classification operations in this context.","PeriodicalId":507937,"journal":{"name":"IET Quantum Communication","volume":"31 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantum computing (QC) hinged upon the bedrock principles of quantum theory and holds promise for reforming a large number of industries. The researcher in this area aims to deliver a comprehensive understanding of the current state of the art and future trajectories of QC. The authors have discovered that most academic studies have concentrated upon dissecting specific aspects of QC. This discernment underscores the exigency of identifying challenges that might impede the seamless integration of QC within the software industry. Moreover, it becomes crucial to ascertain the panoply of solutions/practices required to overcome these barriers. A comprehensive multi‐vocal literature review was performed and culled a total of 49 academic papers for data extraction. A total of 13 challenges encountered by organisations were identified during the adoption of QC. Subsequently, these challenges were examined deeply and determined that five of them are the most critical, these are ‘Lack of quantum specific algorithms, dev and testing methodologies’, ‘Difficult compilation and debugging’, ‘Lack of development tools and technology’, ‘Lack of development guidelines & Quality Assurance Standards’ and ‘Lack of professional expert’, together founding over 30% of occurrences. These challenges from various perspectives were evaluated, including time frame, methodology, geographical region and publication platform. To address these barriers and implement the QC in software industry effectively, a total of 53 practices/solutions. This research aims to share valuable knowledge to simplify and amplify quantum application development.
{"title":"Quantum computing challenges and solutions in software industry—A multivocal literature review","authors":"Masaud Salam, Muhammad Ilyas","doi":"10.1049/qtc2.12096","DOIUrl":"https://doi.org/10.1049/qtc2.12096","url":null,"abstract":"Quantum computing (QC) hinged upon the bedrock principles of quantum theory and holds promise for reforming a large number of industries. The researcher in this area aims to deliver a comprehensive understanding of the current state of the art and future trajectories of QC. The authors have discovered that most academic studies have concentrated upon dissecting specific aspects of QC. This discernment underscores the exigency of identifying challenges that might impede the seamless integration of QC within the software industry. Moreover, it becomes crucial to ascertain the panoply of solutions/practices required to overcome these barriers. A comprehensive multi‐vocal literature review was performed and culled a total of 49 academic papers for data extraction. A total of 13 challenges encountered by organisations were identified during the adoption of QC. Subsequently, these challenges were examined deeply and determined that five of them are the most critical, these are ‘Lack of quantum specific algorithms, dev and testing methodologies’, ‘Difficult compilation and debugging’, ‘Lack of development tools and technology’, ‘Lack of development guidelines & Quality Assurance Standards’ and ‘Lack of professional expert’, together founding over 30% of occurrences. These challenges from various perspectives were evaluated, including time frame, methodology, geographical region and publication platform. To address these barriers and implement the QC in software industry effectively, a total of 53 practices/solutions. This research aims to share valuable knowledge to simplify and amplify quantum application development.","PeriodicalId":507937,"journal":{"name":"IET Quantum Communication","volume":"49 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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