Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning

Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning

Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate-scale quantum computers. A fundamental challenge in quantum machine learning is generalization, as the designer targets performance under testing condition...

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Bibliographic Details
Main Authors: Sangwoo Park, Osvaldo Simeone
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Quantum Engineering
Subjects:
Conformal prediction (CP)
generalization analysis
quantum machine learning
uncertainty quantification
Online Access:https://ieeexplore.ieee.org/document/10321713/
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