用符号化聚类加速量子机器学习的数据编码，提升精度与稳定性。
EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data
- 通过聚类和低深度电路求解均值态，实现快速编码
- 数据映射保真度超90%，显著降低噪声干扰
- 适合在嘈杂中等规模量子设备上部署的高效方案
幅度嵌入（AE）是将经典数据编码到量子线路中的关键步骤。传统方法存在深度大、长度不一的电路问题，导致门操作过多和样本间误差率差异，引发噪声驱动的不一致，降低模型准确率。我们提出EnQode，一种基于符号表示的快速幅度嵌入技术：通过对数据集样本进行聚类，并使用针对特定硬件优化的低深度参数化电路求解簇均值态。该方法减少物理门和交换操作，统一电路深度与结构，使所有样本面临一致且低水平的噪声。在数据映射中实现超过90%的保真度，使量子机器学习在嘈杂中等规模量子（NISQ）设备上表现更鲁棒、性能更优。开源实现提供了可扩展、高效的经典数据与量子模型融合方案。
原文摘要 · Abstract (English)
Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods suffer from deep, variable-length circuits that introduce high output error due to extensive gate usage and variable error rates across samples, resulting in noise-driven inconsistencies that degrade model accuracy. We introduce EnQode, a fast AE technique based on symbolic representation that addresses these limitations by clustering dataset samples and solving for cluster mean states through a low-depth, machine-specific ansatz. Optimized to reduce physical gates and SWAP operations, EnQode ensures all samples face consistent, low noise levels by standardizing circuit depth and composition. With over 90% fidelity in data mapping, EnQode enables robust, high-performance QML on noisy intermediate-scale quantum (NISQ) devices. Our open-source solution provides a scalable and efficient alternative for integrating classical data with quantum models.
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