The Cross-Kernel Margin: A Robustness Measure for Quantum Kernel Methods

arXiv:2601.23084 · quant-ph · Submitted 2026-01-30 · Read on arXiv

quant-ph

Submitted: 2026-01-30

Updated: 2026-07-09

Code: https://github.com/saarishag/cross_kernel_margin_nisq

Terminology

• Robustness — 52× in this paper · explained in 5 episodes
• Accuracy — 28× in this paper · explained in 1 episode
• Generalization — 13× in this paper · explained in 13 episodes
• Calibration — 9× in this paper · explained in 7 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Quantum Circuits — 6× in this paper · explained in 2 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Adversarial Robustness — 3× in this paper · explained in 3 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Fisher Information — 2× in this paper · explained in 3 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Noisy Intermediate-Scale Quantum — 2× in this paper
• Clustering — 1× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Entanglement — 1× in this paper · explained in 4 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Instantaneous Quantum Polynomial-time — 1× in this paper
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reproducing Kernel Hilbert Space — 1× in this paper
• Reproducing Kernel Hilbert Space (RKHS) — 1× in this paper · explained in 3 episodes

Sources

• A Systematic Study of Noise Effects in Hybrid Quantum-Classical Machine Learning
• Rademacher complexity of noisy quantum circuits
• Rethinking Robustness in Machine Learning: A Posterior Agreement Approach
• An inequality for the trace of matrix products, using absolute values
• PennyLane: Automatic differentiation of hybrid quantum-classical computations

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