Efficient option pricing with unary-based photonic computing chip and
  generative adversarial learning
Hui Zhang Lingxiao Wan Sergi Ramos-Calderer Yuancheng Zhan Wai-Keong Mok Hong Cai Feng Gao Xianshu Luo Guo-Qiang Lo Leong Chuan Kwek José Ignacio Latorre Ai Qun Liu
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.