JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks

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.

On the Achievable Rate of MIMO Narrowband PLC with Spatio-Temporal Correlated Noise

Narrowband power line communication (NB-PLC) systems are an attractive solution for supporting current and future smart grids. A technology proposed to enhance data rate in NB-PLC is multiple-input multiple-output (MIMO) transmission over multiple power line phases. To achieve reliable communication over MIMO NB-PLC, a key challenge is to take into account and mitigate the effects of temporally and spatially correlated cyclostationary noise. Noise samples in a cycle can be divided into three classes with different distributions, i.e. Gaussian, moderate impulsive, and strong impulsive. However, in this paper we first show that the impulsive classes in their turn can be divided into sub-classes with normal distributions and, after deriving the theoretical capacity, two noise sample sets with such characteristics are used to evaluate achievable information rates: one sample set is the measured noise in laboratory and the other is produced through MIMO frequency-shift (FRESH) filtering. The achievable information rates are attained by means of a spatio-temporal whitening of the portions of the cyclostationary correlated noise samples that belong to the Gaussian sub-classes. The proposed approach can be useful to design the optimal receiver in terms of bit allocation using waterfilling algorithm and to adapt modulation order.