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.

综述型论文详细探讨了量子计算和机器学习在数据处理和分析方面的转变性影响，超越了传统计算方法的限制，重点研究了量子计算对高级数据处理和应用的意义，尤其强调了提升网络安全的潜力。