The paper proposes to treat financial models using techniques of quantum mechanics. The methodology relies on the Dirac matrix formalism and the Feynman path integral approach. This leads us to reexamine in this framework the classical option pricing models of Cox-Ross-Rubinstein and Black-Scholes. Moreover, financial data are classified with respect to the spectrum of a certain observable and then analyzed to identify price jumps using supervised machine learning tools.

中文翻译：

---

量子期权定价和数据分析

本文提出使用量子力学技术来处理金融模型。该方法依赖于狄拉克矩阵形式主义和费曼路径积分法。这使我们在此框架中重新审视了Cox-Ross-Rubinstein和Black-Scholes的经典期权定价模型。此外，根据某种可观察的频谱对金融数据进行分类，然后使用监督的机器学习工具进行分析以识别价格上涨。