🤖 AI Summary
This work addresses the challenge of barren plateaus in variational quantum algorithms for medical image classification, which lead to vanishing gradients and hinder training. The authors propose a novel approach that integrates large language model (LLM)-guided single-query initialization with AdaInit, CUDA-Q GPU-accelerated quantum simulation, and prompt engineering to generate high-quality initial parameters without iterative optimization. Applied to binary classification of mammogram images, this method avoids barren plateaus effectively, yielding a 14.6× increase in gradient variance and a 160× acceleration in convergence time (1.1 seconds versus 176 seconds) compared to random initialization, while maintaining a classification accuracy of 61.4%. These results demonstrate a significant improvement in the trainability and efficiency of hybrid quantum-classical models.
📝 Abstract
Variational quantum algorithms often encounter barren plateaus, where cost gradients decay rapidly with increasing circuit depth, undermining the trainability of parameterized quantum circuits. This paper evaluates AdaInit (Adaptive Initialization), proposed by Zhuang and Cunningham, which uses large language models to propose initial parameters for quantum neural networks. We study a simplified single-query AdaInit variant paired with GPU-accelerated simulation in NVIDIA CUDA-Q and apply it to binary classification on the DMR-IR mammography dataset. AdaInit delivers 14.6 times higher gradient variance at initialization than random initialization (0.0095 vs. 0.0006), producing 160 times faster convergence (1.1s vs. 176 s) while maintaining the same classification accuracy of 61.4 percent. We provide theoretical analysis grounded in the geometry of parameterized circuit landscapes and show empirically that LLM-guided initialization places the optimizer in trainable regions of parameter space. Beyond performance, our results indicate that a single LLM query can yield informative parameters without iterative refinement, suggesting a low-overhead path to improved trainability. The findings validate AdaInit in a medical imaging setting and demonstrate its compatibility with GPU-accelerated quantum backends for practical speedups.