发表机构
Faculty of Computing Universiti Teknologi Malaysia Johor Bahru, Malaysia Remote Student based in Jakarta, Indonesia; Informatics Department Faculty of Science; Faculty of Computing Universiti Teknologi Malaysia Johor Bahru, Malaysia（; ; ）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出大语言模型引导的AdaInit初始化方法，结合GPU加速量子模拟，在乳腺X线摄影分类任务中，相比随机初始化大幅提升梯度方差与收敛速度，保持相同分类准确率，验证了方法的有效性与兼容性。
AI 中文摘要
变分量子算法常面临贫瘠高原问题，即代价梯度随量子电路深度增加快速衰减，削弱参数化量子电路的可训练性。本文评估庄和坎宁安提出的AdaInit（自适应初始化），该方法利用大语言模型为量子神经网络提出初始参数。我们研究一种简化的单查询AdaInit变体，结合NVIDIA CUDA-Q中的GPU加速模拟，将其应用于DMR-IR乳腺X线摄影数据集的二分类任务。AdaInit在初始化时的梯度方差比随机初始化高14.6倍（0.0095对比0.0006），收敛速度快160倍（1.1秒对比176秒），同时保持61.4%的相同分类准确率。我们基于参数化电路景观的几何特性提供理论分析，实证表明大语言模型引导的初始化能将优化器置于参数空间的可训练区域。除性能外，结果显示单次大语言模型查询即可生成有信息量的参数，无需迭代优化，为提升可训练性提供低开销路径。研究结果验证了AdaInit在医学成像场景中的有效性，并证明其与GPU加速量子后端兼容，可实现实际加速。
英文摘要
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.
CommentsPresented at IAICT 2026, Bali, Indonesia
DOI：10.1109/IAICT71158.2026.11620917
