Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Elsevier eBooks

Application of quantum metaheuristics techniques in data exploration for Internet of Things environment

Gopal Rathinam, Deepa Mallika Muthiah, Mahmoud Ahmad Al-Khasawneh, Feslin Anish Mon

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1016/b978-0-443-33136-7.00020-7

Related papers

Multilevel quantum metaheuristics: applications in data exploration

Smriti Jaiswal, Neha Tyagi

Elsevier eBooks•Jan 1, 2026•0 citations

Quantum Cryptography for Secure Data Transmission in IoT Networks

Sulakshana Sagar Malwade

The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, especially in safeguarding sensitive data transmitted across vast networks. Traditional cryptographic methods, though widely used, face increasing vulnerabilities in light of advancements in quantum computing. This research explores the application of quantum cryptography, particularly Quantum Key Distribution (QKD), to secure data transmission in IoT networks. Quantum cryptography offers a revolutionary approach by utilizing the principles of quantum mechanics, ensuring communication channels are resistant to eavesdropping and quantum attacks. Despite its promise, integrating quantum cryptography into resource-constrained IoT environments presents several technical challenges. This paper examines current quantum cryptographic techniques, their feasibility for IoT applications, and proposed solutions for overcoming integration hurdles. Case studies of real-world implementations and experimental results are discussed to highlight the effectiveness of these methods. The findings suggest that quantum cryptography, while still in its infancy for IoT, has the potential to provide robust security solutions in future networks.

Computer Fraud & Security•Nov 14, 2024•1 citation

Quantum-inspired metaheuristic algorithms: comprehensive survey and classification

Farhad Soleimanian Gharehchopogh

Artificial Intelligence Review•Nov 2, 2022•257 citations

Quantum Computing-Based Bit Encoding and Decoding for IoT Data Transmission

Ian Poveda, Lukas Jara, Diego Fuentealba +1 more

The Internet of Things (IoT) has proven effective in automating various tasks and finding applications in real-world scenarios such as intelligent industries, smart agriculture, e-healthcare, and environment exploration. Nevertheless, their applications demand secure data transmission, as IoT devices remain susceptible to hacking, potentially leading to data breaches. Such breaches can disrupt device operations, posing risks such as fires, impacts on health, and even jeopardizing lives. These security issues are being addressed using blockchain, which has proven effective in classical computing architectures. However, the technology is gradually transitioning towards quantum computing, which has significant processing power that could easily compromise the algorithms employed by blockchain. Thus, there is a need to use quantum computing approaches. In this work, we propose a small IoT environment for a bit encoding and decoding using quantum superdense coding in the data transmission for IoT devices to validate the feasibility of quantum algorithms in IoT. We tested sending integers through two emulated Raspberry Pi devices in VirtualBox. On each Raspberry Pi, we implemented superdense coding to encode and decode the integers sent by the neighbor device via MQTT. Comparing the source data with the target data, the results indicated zero errors in data transmission, confirming that superdense coding is feasible in the IoT context. The average execution time for data decoding was 0, 39 seconds based on 50 tests.

IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)•Dec 5, 2023•1 citation

Quantum Machine Learning

Revolutionize your IoT infrastructure with this guide to mastering quantum-enhanced machine learning algorithms and theoretical frameworks that are shattering the boundaries of classical computing to deliver unprecedented network performance and security. In a world increasingly reliant on interconnected devices and data-driven insights, the limitations of classical computing become ever more apparent. The convergence of quantum computing, machine learning, and the Internet of Things (IoT) heralds a new era of technological advancement, one where the boundaries of computational possibility are continually redefined. This book offers an in-depth examination of how quantum algorithms are utilized to improve the performance, security, and efficiency of IoT devices and networks. It connects theoretical concepts with practical applications, providing a comprehensive look at fundamental principles and advanced techniques in this rapidly growing field. Using case studies and real-world insights, this book gives readers the latest developments in quantum machine learning, artificial intelligence, and the smart Internet of Things, and their potential to create an accessible pathway to the future. Readers will find the volume: Demonstrates how to seamlessly integrate quantum computing and machine learning for next-gen IoT solutions; Explores the emerging field of quantum machine learning and its various applications for the AI-driven Internet of Things; Provides real-world examples and case studies demonstrating the power of quantum machine learning in smart IoT environments; Comprehensively covers a wide range of topics. Audience Researchers and engineers in machine learning, quantum computing, data science, the Internet of Things.

Wiley eBooks•May 22, 2026•0 citations

A review of recent advances in quantum-inspired metaheuristics

Shahin Hakemi, Mahboobeh Houshmand, Esmaeil Kheirkhah +1 more

Evolutionary Intelligence•Oct 23, 2022•70 citations
The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, especially in safeguarding sensitive data transmitted across vast networks. Traditional cryptographic methods, though widely used, face increasing vulnerabilities in light of advancements in quantum computing. This research explores the application of quantum cryptography, particularly Quantum Key Distribution (QKD), to secure data transmission in IoT networks. Quantum cryptography offers a revolutionary approach by utilizing the principles of quantum mechanics, ensuring communication channels are resistant to eavesdropping and quantum attacks. Despite its promise, integrating quantum cryptography into resource-constrained IoT environments presents several technical challenges. This paper examines current quantum cryptographic techniques, their feasibility for IoT applications, and proposed solutions for overcoming integration hurdles. Case studies of real-world implementations and experimental results are discussed to highlight the effectiveness of these methods. The findings suggest that quantum cryptography, while still in its infancy for IoT, has the potential to provide robust security solutions in future networks.
The Internet of Things (IoT) has proven effective in automating various tasks and finding applications in real-world scenarios such as intelligent industries, smart agriculture, e-healthcare, and environment exploration. Nevertheless, their applications demand secure data transmission, as IoT devices remain susceptible to hacking, potentially leading to data breaches. Such breaches can disrupt device operations, posing risks such as fires, impacts on health, and even jeopardizing lives. These security issues are being addressed using blockchain, which has proven effective in classical computing architectures. However, the technology is gradually transitioning towards quantum computing, which has significant processing power that could easily compromise the algorithms employed by blockchain. Thus, there is a need to use quantum computing approaches. In this work, we propose a small IoT environment for a bit encoding and decoding using quantum superdense coding in the data transmission for IoT devices to validate the feasibility of quantum algorithms in IoT. We tested sending integers through two emulated Raspberry Pi devices in VirtualBox. On each Raspberry Pi, we implemented superdense coding to encode and decode the integers sent by the neighbor device via MQTT. Comparing the source data with the target data, the results indicated zero errors in data transmission, confirming that superdense coding is feasible in the IoT context. The average execution time for data decoding was 0, 39 seconds based on 50 tests.
Revolutionize your IoT infrastructure with this guide to mastering quantum-enhanced machine learning algorithms and theoretical frameworks that are shattering the boundaries of classical computing to deliver unprecedented network performance and security. In a world increasingly reliant on interconnected devices and data-driven insights, the limitations of classical computing become ever more apparent. The convergence of quantum computing, machine learning, and the Internet of Things (IoT) heralds a new era of technological advancement, one where the boundaries of computational possibility are continually redefined. This book offers an in-depth examination of how quantum algorithms are utilized to improve the performance, security, and efficiency of IoT devices and networks. It connects theoretical concepts with practical applications, providing a comprehensive look at fundamental principles and advanced techniques in this rapidly growing field. Using case studies and real-world insights, this book gives readers the latest developments in quantum machine learning, artificial intelligence, and the smart Internet of Things, and their potential to create an accessible pathway to the future. Readers will find the volume: Demonstrates how to seamlessly integrate quantum computing and machine learning for next-gen IoT solutions; Explores the emerging field of quantum machine learning and its various applications for the AI-driven Internet of Things; Provides real-world examples and case studies demonstrating the power of quantum machine learning in smart IoT environments; Comprehensively covers a wide range of topics. Audience Researchers and engineers in machine learning, quantum computing, data science, the Internet of Things.