Understanding Generalization in Quantum Machine Learning with Margins
This work addresses the challenge of improving generalization capabilities in quantum machine learning, particularly for researchers and practitioners dealing with quantum data, though it appears incremental as it builds on existing margin-based ideas from classical ML.
The authors tackled the problem of unreliable generalization theories in quantum machine learning by developing a margin-based generalization bound, which they experimentally validated on the quantum phase recognition dataset, showing it outperforms traditional metrics like parameter count as a predictor of generalization performance.
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.