提出量子专家混合框架，提升量子神经网络的可扩展性与表达能力
QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
- 引入可学习的量子路由机制，动态选择并组合专家电路
- 在量子分类任务中表现优于标准量子神经网络
- 适合追求高效可解释量子模型的研究者
量子机器学习（QML）在噪声中等规模量子（NISQ）时代展现出巨大潜力，通过利用叠加和纠缠实现计算与内存优势。然而，受硬件限制，现有QML模型常面临可扩展性和表达力不足的问题。本文提出量子专家混合（QMoE）架构，将专家混合（MoE）范式引入QML场景。QMoE包含多个参数化量子电路作为专家模型，并配备可学习的量子路由机制，根据输入动态选择并聚合特定专家。在量子分类任务上的实证结果表明，该方法持续优于标准量子神经网络，验证了其在学习复杂数据模式方面的有效性。本工作为构建可扩展且可解释的量子学习框架奠定了基础。
原文摘要 · Abstract (English)
Quantum machine learning (QML) has emerged as a promising direction in the noisy intermediate-scale quantum (NISQ) era, offering computational and memory advantages by harnessing superposition and entanglement. However, QML models often face challenges in scalability and expressiveness due to hardware constraints. In this paper, we propose quantum mixture of experts (QMoE), a novel quantum architecture that integrates the mixture of experts (MoE) paradigm into the QML setting. QMoE comprises multiple parameterized quantum circuits serving as expert models, along with a learnable quantum routing mechanism that selects and aggregates specialized quantum experts per input. The empirical results from the proposed QMoE on quantum classification tasks demonstrate that it consistently outperforms standard quantum neural networks, highlighting its effectiveness in learning complex data patterns. Our work paves the way for scalable and interpretable quantum learning frameworks.
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