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

TL;DR

This paper introduces a photonic chip utilizing unary encoding and generative adversarial networks to accelerate European option pricing, achieving quadratic speedup over classical methods and enhancing financial computation efficiency.

Contribution

The work presents a novel photonic computing chip combining unary encoding, quantum amplitude estimation, and GANs for efficient asset distribution modeling in option pricing.

Findings

Achieves quadratic speedup over classical Monte Carlo methods

Integrates GANs for efficient asset distribution learning

Demonstrates potential for specialized photonic processors in finance

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 a quadratic speedup compared to classical Monte Carlo methods. The circuit consists of three modules: a module loading the distribution of asset prices, a module computing the expected payoff, and a module performing the quantum amplitude estimation algorithm to introduce speed-ups. In the distribution module, a generative adversarial network is embedded for efficient learning and loading of asset distributions, which precisely capture the 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.