Mutual Information Maximizing Quantum Generative Adversarial Network and Its Applications in Finance
InfoQGAN integrates quantum GANs and MINE to address mode collapse and generate financial portfolio return distributions.
What it examines
This paper introduces InfoQGAN, a novel quantum-classical hybrid model that integrates Mutual Information Neural Estimation (MINE) with Quantum Generative Adversarial Networks (QGAN) to address mode collapse in GANs. The study applies this model to generate portfolio return distributions in finance, demonstrating its practical applicability.
What it concludes
The study concludes that InfoQGAN offers significant improvements in mitigating mode collapse and controlling generated features. Potential applications include portfolio optimization, option pricing, and yield prediction for financial instruments. Future research may explore noisy quantum circuit simulators and real quantum hardware.
Evidence objects
The study concludes that InfoQGAN offers significant improvements in mitigating mode collapse and controlling generated features. Potential applications include portfolio optimization, option pricing, and yield prediction for financial instruments. Future research may explore noisy quantum circuit simulators and real quantum hardware.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: 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, a… ▽ More 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. △ Less
Source row: 1416 · abstract type: unknown