Efficient option pricing with a unary-based photonic computing chip and generative adversarial learning

Abstract

In the modern financial industry system, the structure of products has become more and more complex, and the bottleneck constraint of classical computing power has already restricted the development of the financial industry. Here, we present a photonic chip that implements the unary approach to European option pricing, in combination with the quantum amplitude estimation algorithm, to achieve quadratic speedup compared to classical Monte Carlo methods. The circuit consists of three modules: one loading the distribution of asset prices, one computing the expected payoff, and a third performing the quantum amplitude estimation algorithm to introduce speedups. In the distribution module, a generative adversarial network is embedded for efficient learning and loading of asset distributions, which precisely captures market trends. This work is a step forward in the development of specialized photonic processors for applications in finance, with the potential to improve the efficiency and quality of financial services.