Quantum Minimal Learning Machine: A Fidelity-Based Approach to Error Mitigation

Abstract

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.

8 pages, 4 figures. Published in Communications in Computer and Information Science (Springer), QUEST-IS 2025. Author version

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• Quantum Machine Learning
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