Machine Learning Applications of Quantum Computing: A Review




Quantum Cryptography, Quantum Computing Security, Applications of Quantum ML, Quantum Algorithms, Quantum Tech in ML, Quantum Computing Trends


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.

Author Biographies

Thien Nguyen, Jamk University of Applied Sciences, Jyväskylä, Finland

Thien Nguyen is a 4th year IT student at Jamk University of Applied Sciences, Finland. He has experience in data analysis and building risk prediction models in banking and finance. Additionally, he is researching artificial intelligence and its applications in practice, exploring its transformative potential across various sectors.

Tuomo Sipola, Jamk University of Applied Sciences, Jyväskylä, Finland

Tuomo Sipola works as a senior researcher at the Institute of Information Technology at Jamk University of Applied Sciences, Jyväskylä, Finland. He completed his PhD in mathematical information technology (University of Jyväskylä) in 2013. He has also worked as a CEO. His interests include machine learning, data analytics and cybersecurity.

Jari Hautamäki, Jamk University of Applied Sciences, Jyväskylä, Finland

Jari Hautamäki, PhL works as a Principal Lecturer of ICT Programmes in a JAMK University of Applied Sciences, Jyväskylä Finland. He has more than 30 years’ experience of university education and its management. His research interesting topics are Data Networks, IT Service Management, Security Information Sharing and Information Security Management.