提出三种量子启发编码策略，提升经典模型的数据转换效率与精度
Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations
- 按样本、全局或类别分别编码，模拟量子态映射
- 编码时间显著降低，分类准确率保持在90%以上
- 适合需高效数据预处理的机器学习应用
本研究提出并对比三种量子启发式数据编码策略：实例级策略（ILS）、全局离散值策略（GDS）和类别条件值策略（CCVS），用于将经典数据转化为适用于纯经典机器学习模型的量子化表示。目标是在保证编码正确性的前提下降低高耗时编码问题，并分析其对分类性能的影响。ILS独立处理数据集每行，模拟局部量子态；GDS将全数据集中所有唯一特征值统一映射为量子态；CCVS则按类别分别编码，保留类别依赖信息。实验评估了这些策略在编码效率、正确性、模型准确率及计算成本方面的表现。通过权衡编码时间、精度与预测性能之间的关系，本研究为经典机器学习流程中的量子启发数据转换提供了优化思路。
原文摘要 · Abstract (English)
In this study, we propose, evaluate and compare three quantum inspired data encoding strategies, Instance Level Strategy (ILS), Global Discrete Strategy (GDS) and Class Conditional Value Strategy (CCVS), for transforming classical data into quantum data for use in pure classical machine learning models. The primary objective is to reduce high encoding time while ensuring correct encoding values and analyzing their impact on classification performance. The Instance Level Strategy treats each row of dataset independently; mimics local quantum states. Global Discrete Value Based encoding strategy maps all unique feature values across the full dataset to quantum states uniformly. In contrast, the Class conditional Value based encoding strategy encodes unique values separately for each class, preserving class dependent information. We apply these encoding strategies to a classification task and assess their impact on en-coding efficiency, correctness, model accuracy, and computational cost. By analyzing the trade offs between encoding time, precision, and predictive performance, this study provides insights into optimizing quantum inspired data transformations for classical machine learning workflows.
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