标题： QMoE：用于可扩展量子神经网络的量子专家混合框架 显示英文标题

标题： QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks

摘要： 量子机器学习（QML）在噪声中等规模量子（NISQ）时代已成为一个有前景的方向，通过利用叠加和纠缠提供计算和内存优势。 然而，由于硬件限制，QML模型常常面临可扩展性和表达能力的挑战。 在本文中，我们提出了量子专家混合（QMoE），这是一种将专家混合（MoE）范式整合到QML设置中的新型量子架构。 QMoE包含多个参数化量子电路作为专家模型，以及一个可学习的量子路由机制，该机制根据每个输入选择并聚合专门的量子专家。 提出的QMoE在量子分类任务上的实证结果表明，它始终优于标准量子神经网络，突显了其在学习复杂数据模式方面的有效性。 我们的工作为可扩展且可解释的量子学习框架铺平了道路。 显示英文摘要

摘要： 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.