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In the modern financial industry system, the structure of products has becomemore and more complex, and the bottleneck constraint of classical computingpower has already restricted the development of the financial industry. Here,we present a photonic chip that implements the unary approach to Europeanoption pricing, in combination with the quantum amplitude estimation algorithm,to achieve a quadratic speedup compared to classical Monte Carlo methods. Thecircuit consists of three modules: a module loading the distribution of assetprices, a module computing the expected payoff, and a module performing thequantum amplitude estimation algorithm to introduce speed-ups. In thedistribution module, a generative adversarial network is embedded for efficientlearning and loading of asset distributions, which precisely capture the markettrends. This work is a step forward in the development of specialized photonicprocessors for applications in finance, with the potential to improve theefficiency and quality of financial services.

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