用量子隧穿启发的激活函数提升混合量子神经网络性能
TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning
- 设计非序列式混合架构，用量子隧穿启发的TDAF替代传统ReLU
- 在类重叠数据上表现更优，分类准确率提升显著
- 适合对量子机器学习表达能力感兴趣的研究者
混合量子-经典神经网络（HQCNN）是机器学习的前沿方向，融合了量子与经典模型的优势。本文提出TunnElQNN，一种由交替的经典与量子层组成的非序列架构。经典部分采用受量子隧穿效应启发的隧穿二极管激活函数（TDAF）。我们在一个交错半圆的合成数据集上评估该模型在多分类任务中的表现，对比了使用传统ReLU激活函数的基线混合架构（ReLUQNN）。结果表明，TunnElQNN在不同类别重叠程度下均优于ReLUQNN。进一步分析显示，TunnElQNN生成的决策边界在高重叠场景下更具鲁棒性，且其性能优于仅在经典网络中使用TDAF的全经典模型。这些发现揭示了将物理启发激活函数与量子组件结合，可有效增强混合量子-经典架构的表达能力与鲁棒性。
原文摘要 · Abstract (English)
Hybrid quantum-classical neural networks (HQCNNs) represent a promising frontier in machine learning, leveraging the complementary strengths of both models. In this work, we propose the development of TunnElQNN, a non-sequential architecture composed of alternating classical and quantum layers. Within the classical component, we employ the Tunnelling Diode Activation Function (TDAF), inspired by the I-V characteristics of quantum tunnelling. We evaluate the performance of this hybrid model on a synthetic dataset of interleaving half-circle for multi-class classification tasks with varying degrees of class overlap. The model is compared against a baseline hybrid architecture that uses the conventional ReLU activation function (ReLUQNN). Our results show that the TunnElQNN model consistently outperforms the ReLUQNN counterpart. Furthermore, we analyse the decision boundaries generated by TunnElQNN under different levels of class overlap and compare them to those produced by a neural network implementing TDAF within a fully classical architecture. These findings highlight the potential of integrating physics-inspired activation functions with quantum components to enhance the expressiveness and robustness of hybrid quantum-classical machine learning architectures.
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