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.

该研究探索了将量子数据嵌入技术与经典机器学习算法相结合，旨在评估在各种模型上的性能增强和计算效果。我们探索了多种经典到量子的映射方法，包括基编码、角度编码和幅度编码，通过对包括逻辑回归、K 近邻、支持向量机以及随机森林、LightGBM、AdaBoost 和 CatBoost 在内的流行机器学习算法进行广泛的实证研究，发现量子数据嵌入有助于改善分类准确性和 F1 分数，尤其在与增强特征表示有关的模型中表现突出。我们观察到运行时间方面存在微妙的影响，低复杂度模型展示了适度的增加，而计算复杂度更高的模型则经历了明显的变化。值得注意的是，集成方法在性能提升和计算开销之间表现出有利的平衡。该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，并强调了在性能改进与计算成本之间权衡的重要性。未来的研究方向可能涉及优化计算效率的量子编码过程以及在实际应用中的可扩展性。我们的工作为量子计算和经典机器学习交叉领域的不断增长的知识贡献了见解，为寻求利用量子启发技术的研究人员和实践者提供了洞察。