The Average Relative Entropy and Transpilation Depth determines the noise robustness in Variational Quantum Classifiers
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 ...
| Authors | Aakash Ravindra Shinde, Arianne Meijer - van de Griend, Jukka K. Nurminen |
|---|---|
| Primary category | quant-ph |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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