用多种嵌入策略提升量子机器学习拟合能力
Multiple Embeddings for Quantum Machine Learning
- 融合多种量子数据嵌入方法，增强模型表达力
- 实验显示性能显著优于现有先进方法
- 适合需高效处理多类型数据的量子计算场景
本文针对当前量子机器学习方法因过度依赖单一数据嵌入策略而导致拟合能力不足的问题，提出一种新型量子机器学习框架，集成多种量子数据嵌入策略，使模型能充分挖掘量子计算在处理不同数据集时的多样性优势。实验结果验证了该框架的有效性，在实际应用中表现优异，显著优于现有最先进方法。
原文摘要 · Abstract (English)
This work focuses on the limitations about the insufficient fitting capability of current quantum machine learning methods, which results from the over-reliance on a single data embedding strategy. We propose a novel quantum machine learning framework that integrates multiple quantum data embedding strategies, allowing the model to fully exploit the diversity of quantum computing when processing various datasets. Experimental results validate the effectiveness of the proposed framework, demonstrating significant improvements over existing state-of-the-art methods and achieving superior performance in practical applications.
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