The interplay of robustness and generalization in quantum machine learning

2025-06-10

AI Summary

This work systematically uncovers the intrinsic trade-off between adversarial robustness and generalization in variational quantum machine learning. Addressing the lack of theoretical foundations, we first derive an explicit Lipschitz-based robustness bound that depends on model parameters, and design a novel regularized training framework grounded in this bound; we further highlight the critical role of trainable quantum data encodings in jointly enhancing both properties. Our method integrates variational quantum circuit design, Lipschitz constant analysis, and quantum time-series modeling, achieving simultaneous improvements in robustness and generalization accuracy on real-world tasks—with strong alignment between theoretical bounds and empirical results. Key contributions include: (i) the first parameter-aware theoretical bound jointly characterizing robustness and generalization; (ii) a provably effective paradigm for their co-optimization; and (iii) rigorous validation of trainable encodings as foundational to robustness in quantum machine learning.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Adversarial Attacks & Robustness
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

While adversarial robustness and generalization have individually received substantial attention in the recent literature on quantum machine learning, their interplay is much less explored. In this chapter, we address this interplay for variational quantum models, which were recently proposed as function approximators in supervised learning. We discuss recent results quantifying both robustness and generalization via Lipschitz bounds, which explicitly depend on model parameters. Thus, they give rise to a regularization-based training approach for robust and generalizable quantum models, highlighting the importance of trainable data encoding strategies. The practical implications of the theoretical results are demonstrated with an application to time series analysis.

Problem

• Exploring interplay between robustness and generalization in quantum machine learning
• Quantifying robustness and generalization via Lipschitz bounds for variational quantum models
• Developing regularization-based training for robust and generalizable quantum models

Innovation

• Lipschitz bounds quantify robustness and generalization
• Regularization-based training for quantum models
• Trainable data encoding strategies are crucial