论文标题
深度量子神经网络的应用
Application of deep quantum neural networks to finance
论文作者
论文摘要
量子计算的最新发展使我们有机会探索其在许多领域的潜在应用，而财务领域也不例外。在本文中，我们应用了Beer等人提出的深度量子神经网络。 （2020）并在简单实验的背景下讨论这种潜力，例如学习隐含的波动和期权价格。此外，可以通过神经网络对风险管理的重要措施（例如Delta和Gamma）等希腊人进行分析，而我们的数值实验表明，深度量子神经网络是解决此类数字问题的有前途的技术，可有效地在财务上有效地产生。
The recent development of quantum computing gives us an opportunity to explore its potential applications to many fields, with the field of finance being no exception. In this paper, we apply the deep quantum neural network proposed by Beer et al. (2020) and discuss such potential in the context of simple experiments such as learning implied volatilities and option prices. Furthermore, Greeks such as delta and gamma, which are important measures in risk management, can be computed analytically with the neural network, and our numerical experiments show that the deep quantum neural network is a promising technique for solving such numerical problems arising in finance efficiently.
