2025 marks the UN International Year of Quantum Science and Technology. We expect this emerging technology to enter application in real world industry use cases in the next few years. The current focus in research and industry lies on the nominal function of quantum computing to show its “usefulness” of the technology. Hence, one should also start considering aspects of cybersecurity to prepare for currently known attacks when quantum computing is ready to be used in industry.This work provides an overview on quantum computing (QC) with the underlying used quantum mechanical phenomena. We set special highlight on quantum machine learning (QML) as it is expected to have higher expressiveness and shorter training times. The quantum machine learning lifecycle will be presented in detail with the current state of the art of theory and research of cybersecurity attacks.We aim to use this knowledge and start to construct simple experiments as proof-of-concepts for attacking assets in the CIA triad. These proof-of-concepts will set the base for attacks on more relevant use cases of QML models, leading to thoughts and experiments for countermeasures to be set up and put in place. The conclusion will consist of future work and the currently not in detail investigated sections of the lifecycle.
2025 marks the UN International Year of Quantum Science and Technology. We expect this emerging technology to enter application in real world industry use cases in the next few years. The current focus in research and industry lies on the nominal function of quantum computing to show its “usefulness” of the technology. Hence, one should also start considering aspects of cybersecurity to prepare for currently known attacks when quantum computing is ready to be used in industry.This work provides an overview on quantum computing (QC) with the underlying used quantum mechanical phenomena. We set special highlight on quantum machine learning (QML) as it is expected to have higher expressiveness and shorter training times. The quantum machine learning lifecycle will be presented in detail with the current state of the art of theory and research of cybersecurity attacks.We aim to use this knowledge and start to construct simple experiments as proof-of-concepts for attacking assets in the CIA triad. These proof-of-concepts will set the base for attacks on more relevant use cases of QML models, leading to thoughts and experiments for countermeasures to be set up and put in place. The conclusion will consist of future work and the currently not in detail investigated sections of the lifecycle.
In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.
The book explores the latest quantum computing research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, artificial intelligence-based devices, computer technology, and their solutions. Future quantum machines will exponentially boost computing power, creating new opportunities for improving cybersecurity. Both classical and quantum-based cyberattacks can be proactively identified and stopped by quantum-based cybersecurity before they harm. Complex math-based problems that support several encryption standards could be quickly solved by using quantum machine learning. This comprehensive book examines how quantum machine learning and quantum computing are reshaping cybersecurity, addressing emerging challenges. It includes in-depth illustrations of real-world scenarios and actionable strategies for integrating quantum-based solutions into existing cybersecurity frameworks. A range of topics are examined, including quantum-secure encryption techniques, quantum key distribution, and the impact of quantum computing algorithms. Additionally, it talks about machine learning models and how to use machine learning to solve problems. Through its in-depth analysis and innovative ideas, each chapter provides a compilation of research on cutting-edge quantum computer techniques, like blockchain, quantum machine learning, and cybersecurity. Audience This book serves as a ready reference for researchers and professionals working in the area of quantum computing models in communications, machine learning techniques, IoT-enabled technologies, and various application industries such as finance, healthcare, transportation and utilities.
At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bounds of traditional computational methods. Drawing upon an in-depth analysis of 32 seminal papers, this review delves into the interplay between quantum computing and machine learning, focusing on transcending the limitations of classical computing in advanced data processing and applications. This review emphasizes the potential of quantum-enhanced methods in enhancing cybersecurity, a critical sector that stands to benefit significantly from these advancements. The literature review, primarily leveraging Science Direct as an academic database, delves into the transformative effects of quantum technologies on machine learning, drawing insights from a diverse collection of studies and scholarly articles. While the focus is primarily on the growing significance of quantum computing in cybersecurity, the review also acknowledges the promising implications for other sectors as the field matures. Our systematic approach categorizes sources based on quantum machine learning algorithms, applications, challenges, and potential future developments, uncovering that quantum computing is increasingly being implemented in practical machine learning scenarios. The review highlights advancements in quantum-enhanced machine learning algorithms and their potential applications in sectors such as cybersecurity, emphasizing the need for industry-specific solutions while considering ethical and security concerns. By presenting an overview of the current state and projecting future directions, the paper sets a foundation for ongoing research and strategic advancement in quantum machine learning.
Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing. Following this, we delve into the key aspects of QML in a detailed and comprehensive manner. In this survey, we investigate a variety of QML algorithms, discussing their applicability across different domains. We examine quantum datasets, highlighting their unique characteristics and advantages. The survey also covers the current state of hardware technologies, providing insights into the latest advancements and their implications for QML. Additionally, we review the software tools and simulators available for QML development, discussing their features and usability. Furthermore, we explore practical applications of QML, illustrating how it can be leveraged to solve real-world problems more efficiently than classical ML methods. This survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.
Abstract Artificial intelligence (AI) and quantum computing represent a new frontier in computing, offering the promise of solving problems previously considered unsolvable. This article aims to provide an in-depth analysis of the current state of research at the intersection of quantum computing and artificial intelligence. We will explore the basic concepts, major applications, and challenges of quantum machine learning (QML). We will start by offering a definition of the motivation for the convergence of quantum computing and artificial intelligence. We will then proceed to offer a description of the basic algorithmic frameworks for quantum machine learning, such as quantum differential algorithms (VQA) for near-term quantum (NISQ) devices and the prospects for error-tolerant quantum algorithms such as the Harrow-Hassidim-Lloyd algorithm. We will then proceed to offer a methodical analysis of the current applications of quantum machine learning in finance, cybersecurity, materials science, among other fields. We will then proceed to discuss the basic challenges facing the implementation of quantum machine learning, while offering a glimpse of the future of quantum-enhanced artificial intelligence.
Quantum computing and artificial intelligence (AI) are two influential technologies that are beginning to shape how we solve problems in cybersecurity and education. As cyber threats become progressively complex, there is a growing need for students to understand how these advanced tools work and how to apply them in real-world sites. This paper presents a simple yet effective framework for designing hands-on labs that teach students about quantum machine learning (QML), which leverages the strengths of both quantum computing and Artificial Intelligences. These labs are meant to help students learn by solving practical cybersecurity challenges while developing technical skills in coding, quantum circuits, and machine learning models. We also reviewed key research from experts in the fields of quantum computing, cybersecurity, and education. Their insights helped us determine how to organize the topics and where they should be placed in a learning program. The goal is to make the learning process more interactive, scalable, and relevant to the digital world today. This method not only teaches students new technologies but also provides them with the chance to experience how these tools can be used to defend against real-world cyber threats.