We introduce the concept of quantum minimal learning machine (QMLM), a supervised similarity-based learning algorithm. The algorithm is conceptually based on a classical machine learning model and adopted to work with quantum data. We will motivate the theory and run the model as an error mitigation method for various parameters.
@article{arxiv.2603.07532,
title = {Quantum Minimal Learning Machine: A Fidelity-Based Approach to Error Mitigation},
author = {Clemens Lindner and Joonas Hämäläinen and Matti Raasakka},
journal= {arXiv preprint arXiv:2603.07532},
year = {2026}
}
Comments
8 pages, 4 figures. Published in Communications in Computer and Information Science (Springer), QUEST-IS 2025. Author version