Quantum Surrogate-Driven Image Classifier: A Gradient-Free Approach to Avoid Barren Plateaus

Yichen Xie

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arXiv · 2505.05249

Quantum Surrogate-Driven Image Classifier: A Gradient-Free Approach to Avoid Barren Plateaus

Yichen Xie

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Abstract

Training deep quantum neural networks (QNNs) for image classification is notoriously difficult due to vanishing gradients (barren plateaus) and limited nonlinearity in purely unitary circuits. We propose a novel gradient-free surrogate-driven framework combined with mid-circuit measurement and reset of ancillary qubits to induce effective nonunitarity. Our approach uses a classical neural surrogate to predict measurement outcomes from circuit parameters to avoid direct gradients. Theoretical results prove that bypassing quantum gradients mitigates plateau issues. Experiments on MNIST, CIFAR-10, and CIFAR-100 with 15-qubit, 6-layer circuits using four resettable ancillas demonstrate superior accuracy compared to direct-gradient QNNs and classical baselines. Our method also serves as a potential for a generalized training framework applicable to various QNN architectures beyond image classification.

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BibTeX RIS

Yichen Xie. 2025-05-08. Quantum Surrogate-Driven Image Classifier: A Gradient-Free Approach to Avoid Barren Plateaus. https://arxiv.org/abs/2505.05249

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