Mutual Information Maximizing Quantum Generative Adversarial Network and
  Its Applications in Finance
Mingyu Lee Myeongjin Shin Junseo Lee Kabgyun Jeong
One of the most promising applications in the era of NISQ (Noisy
Intermediate-Scale Quantum) computing is quantum machine learning. Quantum
machine learning offers significant quantum advantages over classical machine
learning across various domains. Specifically, generative adversarial networks
have been recognized for their potential utility in diverse fields such as
image generation, finance, and probability distribution modeling. However,
these networks necessitate solutions for inherent challenges like mode
collapse. In this study, we capitalize on the concept that the estimation of
mutual information between high-dimensional continuous random variables can be
achieved through gradient descent using neural networks. We introduce a novel
approach named InfoQGAN, which employs the Mutual Information Neural Estimator
(MINE) within the framework of quantum generative adversarial networks to
tackle the mode collapse issue. Furthermore, we elaborate on how this approach
can be applied to a financial scenario, specifically addressing the problem of
generating portfolio return distributions through dynamic asset allocation.
This illustrates the potential practical applicability of InfoQGAN in
real-world contexts.