Understanding Generalization in Quantum Machine Learning with Margins

TL;DR

This paper introduces a margin-based generalization bound for quantum machine learning models, demonstrating its effectiveness in predicting performance and improving generalization through a hybrid classical-quantum approach.

Contribution

It proposes a new margin-based generalization bound for QML and links it to quantum information theory to enhance model performance.

Findings

Margin-based metrics outperform traditional metrics in predicting generalization.

Experimental validation on quantum phase recognition dataset.

Hybrid classical-quantum approach improves generalization performance.

Abstract

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.