This research explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms, aiming to assess the performance enhancements and computational implications across a spectrum of models. We explore various classical-to-quantum mapping methods, ranging from basis encoding, angle encoding to amplitude encoding for encoding classical data, we conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead. This study underscores the potential of quantum data embedding in enhancing classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research directions may involve refining quantum encoding processes to optimize computational efficiency and exploring scalability for real-world applications. Our work contributes to the growing body of knowledge at the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.

翻译：本研究探索将量子数据嵌入技术融入经典机器学习算法，旨在评估不同模型中的性能提升与计算影响。我们考察了从基础编码、角度编码到振幅编码等多种经典到量子映射方法，对包括逻辑回归、K近邻、支持向量机以及随机森林、LightGBM、AdaBoost和CatBoost等集成方法在内的主流机器学习算法进行了广泛实证研究。研究结果表明，量子数据嵌入有助于提高分类精度和F1分数，尤其是在本身受益于增强特征表示的模型中表现显著。我们观察到运行时间存在细微影响：低复杂度模型出现适度增加，而计算密集型模型则呈现明显变化。值得注意的是，集成方法在性能提升与计算开销之间展现了良好平衡。本研究强调了量子数据嵌入在增强经典机器学习模型方面的潜力，并指出需权衡性能改进与计算成本。未来研究方向可能包括优化量子编码过程以提高计算效率，并探索其在实际应用中的可扩展性。我们的工作丰富了量子计算与经典机器学习交叉领域的知识体系，为科研人员与从业者在实际场景中利用量子启发技术的优势提供了重要参考。