A Quantum Generative Adversarial Network for distributions
This work addresses the challenge of leveraging quantum advantages for generative modeling in finance, but it appears incremental as it adapts existing GAN concepts to a quantum framework without clear breakthroughs.
The authors tackled the problem of applying quantum computing to generative adversarial networks (GANs) by developing a fully connected quantum GAN and demonstrating its application in mathematical finance for volatility modeling, though no concrete performance numbers are provided.
Generative Adversarial Networks are becoming a fundamental tool in Machine Learning, in particular in the context of improving the stability of deep neural networks. At the same time, recent advances in Quantum Computing have shown that, despite the absence of a fault-tolerant quantum computer so far, quantum techniques are providing exponential advantage over their classical counterparts. We develop a fully connected Quantum Generative Adversarial network and show how it can be applied in Mathematical Finance, with a particular focus on volatility modelling.