用量子支持向量机分析股市数据，首次系统验证其在孟加拉股市的潜力。
Classification of Financial Data Using Quantum Support Vector Machine
- 采用量子核函数处理金融时间序列分类任务
- 在DSEx指数数据上超越经典RBF核的分类准确率
- 为未来量化金融研究提供资源估算与方法参考
量子支持向量机是一种基于核函数的分类方法。本文研究量子核函数在金融数据中的适用性，聚焦于自构建的达卡证券交易所（DSEx）综合指数数据集。据我们所知，这是首次对量子核函数在该数据集上的系统性研究。在Krunic等人提出的实证量子优势（EQA）框架下，我们将多个量子核函数与经典RBF核支持向量机基线进行对比，提出该数据集表现最优的核函数，并将其结果与相空间地形崎岖度指数（Phase Space Terrain Ruggedness Index）相关联。同时，估算未来更大规模研究所需计算资源，为后续实践者提供指导。
原文摘要 · Abstract (English)
Quantum Support Vector Machine is a kernel-based approach to classification problems. We study the applicability of quantum kernels to financial data, specifically our self-curated Dhaka Stock Exchange (DSEx) Broad Index dataset. To the best of our knowledge, this is the first systematic study of quantum kernels applied to this dataset. Working within the empirical quantum advantage (EQA) framework of Krunic et al., we benchmark several quantum kernels against a classical RBF-kernel SVM baseline, propose the best-performing kernel for this dataset, and relate the observations to the Phase Space Terrain Ruggedness Index metric. We estimate the resources needed to carry out these investigations on a larger scale for future practitioners.
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