# Machine Learning Applications of Quantum Computing: A Review

arXiv: https://arxiv.org/abs/2406.13262 · HTML: https://mlsanity.com/paper/2406.13262/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Asynchronous training of quantum reinforcement learning](https://mlsanity.com/paper/2301.05096/) (2023)
- [Classical-to-quantum convolutional neural network transfer learning](https://mlsanity.com/paper/2208.14708/) (2022)
- [Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications](https://mlsanity.com/paper/2202.11200/) (2022)
- [The role of entanglement for enhancing the efficiency of quantum kernels towards classification](https://mlsanity.com/paper/2209.05142/) (2022)
- [Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification](https://mlsanity.com/paper/2105.09580/) (2021)

## Built on by (most cited first, 4 of 4)

- [Multi-channel convolutional neural quantum embedding](https://mlsanity.com/paper/2509.22355/) (2025)
- [Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions](https://mlsanity.com/paper/2512.15286/) (2025)
- [Quantum Error Mitigation with Attention Graph Transformers for Burgers Equation Solvers on NISQ Hardware](https://mlsanity.com/paper/2512.23817/) (2025)
- [SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training](https://mlsanity.com/paper/2604.06900/) (2026)

## Abstract (arXiv)

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.