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This paper introduces JGRA, a framework that applies Jacobian geometry to assess robustness in Quantum Neural Networks (QNNs) specifically under the noise constraints of the NISQ era. By linking clean-regime model structure to noisy inference behavior, the method provides geometric descriptors capable of predicting resilience to unseen noise, addressing a critical gap where classical redundancy principles have not yet been fully translated to quantum systems. The work offers a practical diagnostic tool for evaluating QNN reliability before deployment in hardware-limited environments.
JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks
arXiv:2606.09964v2 Announce Type: replace-cross Abstract: The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance. In classical deep learning, model robustness and resilience to perturbations are well studied: deep neural networks (DNNs) maintain high performance despite pruning, noise injection, and structural perturbations due to inherent redundancy in their representations. A central challenge in quantum machine learning is to transfer this notion of robustness to quantum neural networks (QNNs) under realistic NISQ noise. While classical deep learning exhibits robustness through structural redundancy, analogous principles for QNNs remain underdeveloped. We propose JGRA: a framework for assessing robustness in noise-aware QNNs via Jacobian geometry, capturing model sensitivity to parameter perturbations induced by noise. Our method includes entropy-matched noise calibration, noise-aware training, and noise-conditioned Jacobian extraction, yielding geometric descriptors that link clean-regime structure to noisy inference behaviour. We also empirically demonstrate that these descriptors encode predictive information about robustness under unseen noise.
Abstract:The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance. In classical deep learning, model robustness and resilience to perturbations are well studied: deep neural networks (DNNs) maintain high performance despite pruning, noise injection, and structural perturbations due to inherent redundancy in their representations. A central challenge in quantum machine learning is to transfer this notion of robustness to quantum neural networks (QNNs) under realistic NISQ noise. While classical deep learning exhibits robustness through structural redundancy, analogous principles for QNNs remain underdeveloped. We propose JGRA: a framework for assessing robustness in noise-aware QNNs via Jacobian geometry, capturing model sensitivity to parameter perturbations induced by noise. Our method includes entropy-matched noise calibration, noise-aware training, and noise-conditioned Jacobian extraction, yielding geometric descriptors that link clean-regime structure to noisy inference behaviour. We also empirically demonstrate that these descriptors encode predictive information about robustness under unseen noise.
Accepted at IEEE qCCL 2026. Author accepted manuscript. 6 pages; cleaned source files, no changes to manuscript content
Quantum Physics (quant-ph); Machine Learning (cs.LG)
arXiv:2606.09964 [quant-ph]
(or arXiv:2606.09964v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2606.09964 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 262 words
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