Systematic Literature Review: Quantum Machine Learning and Its Applications Published
Quantum Machine Learning Systematic ReviewExcited 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
Originally shared on LinkedIn on February 5, 2024 - 53 reactions, 4 comments as of 11/12/2025
I research and develop AI-based systems to tackle complex problems in quantum computing and other relevant areas.
My work sits at the intersection of quantum computing, machine learning, and open source. I hold IBM Master Inventor status and have contributed to tools used by millions of developers worldwide.
I have a PhD in Computer Engineering from the University of Salamanca (2018), have authored 80+ publications, and received the 2019 SCIE-BBVA Award for best young researcher in Computer Science in Spain.