--- title: 'Systematic Literature Review: Quantum Machine Learning and Its Applications Published' subtitle: 'Comprehensive analysis of QML research from 2017-2023' summary: 'Published systematic literature review on quantum machine learning and its applications in Computer Science Review journal. Analyzed 94 studies from 2017-2023, identifying two primary algorithm categories and highlighting image classification as a key application area, while noting that quantum hardware improvements are necessary for QML full potential.' authors: - juancb tags: - Quantum Computing - Quantum Machine Learning - Machine Learning - Research - Systematic Literature Review - AI for Quantum categories: - Quantum Computing - Artificial Intelligence - Research date: "2024-02-05T00:00:00Z" lastmod: "2024-02-05T00:00:00Z" featured: true draft: false # Featured image # To use, add an image named `featured.jpg/png` to your page's folder. # Placement options: 1 = Full column width, 2 = Out-set, 3 = Screen-width # Focal point options: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight image: placement: 2 caption: 'Quantum Machine Learning Systematic Review' focal_point: "Smart" preview_only: false # Projects (optional). # Associate this post with one or more of your projects. # Simply enter your project's folder or file name without extension. # E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`. # Otherwise, set `projects = []`. projects: [] --- Excited to share our systematic literature review paper "Systematic literature review: Quantum machine learning and its applications" published in Computer Science Review! 🚀 This comprehensive work, in collaboration with David Peral García and Francisco José García-Peñalvo from the University of Salamanca, Spain, analyzes the state of quantum machine learning research from 2017 to 2023. Key findings from our review of 94 studies: - ✅ Identified two primary algorithm categories: quantum versions of classical ML algorithms (support vector machines, k-nearest neighbors) and quantum neural networks - ✅ Image classification emerged as a particularly relevant application area - ✅ While quantum machine learning demonstrates promise, it remains far from achieving its full potential Our analysis highlights that quantum hardware improvements are necessary, as current quantum computers lack sufficient quality, speed, and scalability for QML's full realization. This research provides valuable insights into the current state and future directions of quantum machine learning. Read the full paper: https://www.sciencedirect.com/science/article/pii/S1574013724000030. [DOI](https://doi.org/10.1016/j.cosrev.2024.100619) --- *Originally shared on [LinkedIn](https://www.linkedin.com/posts/juancb_systematic-literature-review-quantum-machine-activity-7160148207727960064-JscM) on February 5, 2024 - 53 reactions, 4 comments as of 11/12/2025*