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Understanding Generalization in Quantum Machine Learning with Margins by Hur, Tak; Park, Daniel K. is a scholarly article available to read on EtoBox.

What is Understanding Generalization in Quantum Machine Learning with Margins about?

Understanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current generalization theories, particularly those relying on uniform bounds, across both classical and quantum settings. In this work, we present a margin-based generalization bound for QML models, providing a more reliable framework for evaluating generalization. Our experimental studies on the quantum phase recognition (QPR) dataset demonstrate that margin-based metrics are strong predictors of generalization performance, outperforming traditional metrics like parameter count. By connecting this margin-based metric to quantum information theory, we demonstrate how to enhance the generalization performance of QML through a classical-quantum hybrid approach when applied to classical data.

Author

Hur, Tak; Park, Daniel K.

Published

2024

Language

EN