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Quantum Optimization
Quantum Minimal Learning Machine: A Fidelity-Based Approach to Error Mitigation
arXiv
Authors: Clemens Lindner, Joonas Hämäläinen, Matti Raasakka
Year
2026
Paper ID
28638
Status
Preprint
Abstract Read
~2 min
Abstract Words
50
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- We introduce the concept of quantum minimal learning machine (QMLM), a supervised similarity-based learning algorithm.
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