混合变分量子-经典框架：自适应加权与效率评估
Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
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中文总结 AI 辅助
本文提出Sim-HVQC混合深度量子神经网络，结合自适应SimAM加权与经典特征提取，在多类数据集上实现高效分类，并验证了可复现性与参数效率。
中文摘要 AI 辅助
混合量子-经典神经网络已成为在机器学习中利用量子计算同时缓解当前硬件限制的一种有前景的方法。本文提出了Sim-HVQC，一种混合深度量子神经网络，它将自适应的、无参数的SimAM加权模块与经典特征提取相结合，以在将类判别信息编码到变分量子电路（VQC）之前保留这些信息。以往的研究仅限于二分类问题[1][2][3][4][5]。相比之下，所提出的框架在多种多类数据集（MNIST、KMNIST、Fashion-MNIST和EMNIST）上进行了训练和评估。该框架进一步通过多种子评估、参数分析和潜在/量子特征检查展示了可复现性、参数效率和可解释性。源代码可在以下网址公开获取：SimAM-HVQC。
英文摘要
Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating current hardware limitations. This paper presents Sim-HVQC, a hybrid Deep Quantum Neural Network that couples an adaptive, parameter-free SimAM weighting module with classical feature extraction to preserve class-discriminative information prior to encoding into a Variational Quantum Circuit (VQC). Previous studies are restricted to binary classification [1] [2] [3] [4] [5]. In contrast, the proposed framework is trained and evaluated on various multi-class datasets(MNIST, KMNIST, Fashion-MNIST, and EMNIST). The framework further demonstrates reproducibility, parameter efficiency, and interpretability through multi-seed evaluation, parameter analysis, and latent/quantum feature inspection. The source code is publicly available at https://github.com/Dilli822/ SimAM-HVQC
发表机构
- Institute of Science and Technology Tribhuvan University（特里布文大学科学技术学院）
机构由 AI 辅助整理，请以论文原文为准。
