优化量子机器学习的数据编码方式，显著提升模型性能。
Quantum feature encoding optimization
- 通过调整特征的排序、选择和权重来优化数据输入方式。
- 在多种数据集和量子电路规模下，模型性能均有明显提升。
- 已在100量子比特真实硬件上验证可行性，适合未来量子应用。
量子机器学习（QML）有望在模型复杂度和准确性方面实现突破。其关键挑战在于输入数据的编码方式，直接影响模型表现。本文聚焦于QML特有的编码问题——不改变量子变分线路（ansatz），而是优化数据如何传递给ansatz。我们设计了基于经典数据预处理（如特征排序、选择与加权）的QML流程，在多种数据集、ansatz结构及电路规模下评估其对性能的影响，并验证是否可通过优化编码提升表现。实验结果表明，优化特征编码能显著且一致地改善各类QML模型性能，为未来应用提供有力支持。最后，我们在实际量子硬件上运行100量子比特电路，成功实现了性能提升，证明该方法具备实用性。
原文摘要 · Abstract (English)
Quantum Machine Learning (QML) holds the promise of enhancing machine learning modeling in terms of both complexity and accuracy. A key challenge in this domain is the encoding of input data, which plays a pivotal role in determining the performance of QML models. In this work, we tackle a largely unaddressed aspect of encoding that is unique to QML modeling -- rather than adjusting the ansatz used for encoding, we consider adjusting how data is conveyed to the ansatz. We specifically implement QML pipelines that leverage classical data manipulation (i.e., ordering, selecting, and weighting features) as a preprocessing step, and evaluate if these aspects of encoding can have a significant impact on QML model performance, and if they can be effectively optimized to improve performance. Our experimental results, applied across a wide variety of data sets, ansatz, and circuit sizes, with a representative QML approach, demonstrate that by optimizing how features are encoded in an ansatz we can substantially and consistently improve the performance of QML models, making a compelling case for integrating these techniques in future QML applications. Finally we demonstrate the practical feasibility of this approach by running it using real quantum hardware with 100 qubit circuits and successfully achieving improved QML modeling performance in this case as well.
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