Adaptive Quantum Neural Network Architecture Sensitivity to Noise Modeling

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

The sensitivity of quantum neural networks (QNNs) to noise is a critical factor limiting their practical applications. This research investigates the relationship between QNN performance, noise levels, and network architecture, proposing an adaptive modeling framework to enhance noise resilience. The core claim is that QNN performance is intricately linked to the number of qubits and the network's topological structure, with noise sensitivity being a key determinant. The proposed mechanism involves a framework combining quantum state analysis and machine learning, dynamically adjusting network parameters and topology based on real-time noise characterization. This approach moves beyond static noise assumptions, offering a dynamic modeling capability essential for optimizing QNN robustness. The model presented here provides a foundational approach for designing and controlling QNNs in noisy quantum environments, contributing to the field's advancement towards reliable quantum computation.

DOI: 10.5281/zenodo.22269379

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