[2603.21300] The Average Relative Entropy and Transpilation Depth determines the noise robustness in Variational Quantum Classifiers

Quantum Physics arXiv:2603.21300 (quant-ph) [Submitted on 22 Mar 2026] Title:The Average Relative Entropy and Transpilation Depth determines the noise robustness in Variational Quantum Classifiers Authors:Aakash Ravindra Shinde, Arianne Meijer - van de Griend, Jukka K. Nurminen View a PDF of the paper titled The Average Relative Entropy and Transpilation Depth determines the noise robustness in Variational Quantum Classifiers, by Aakash Ravindra Shinde and 2 other authors View PDF Abstract:Variational Quantum Algorithms (VQAs) have been extensively researched for applications in Quantum Machine Learning (QML), Optimization, and Molecular simulations. Although designed for Noisy Intermediate-Scale Quantum (NISQ) devices, VQAs are predominantly evaluated classically due to uncertain results on noisy devices and limited resource availability. Raising concern over the reproducibility of simulated VQAs on noisy hardware. While prior studies indicate that VQAs may exhibit noise resilience in specific parameterized shallow quantum circuits, there are no definitive measures to establish what defines a shallow circuit or the optimal circuit depth for VQAs on a noisy platform. These challenges extend naturally to Variational Quantum Classification (VQC) algorithms, a subclass of VQAs for supervised learning. In this article, we propose a relative entropy-based metric to verify whether a VQC model would perform similarly on a noisy device as it does on simulations. We establish a str...