Systematic Literature Review: Quantum Machine Learning and Its Applications Published

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

Originally shared on LinkedIn on February 5, 2024 - 53 reactions, 4 comments as of 11/12/2025