🤖 AI Summary
Noisy Intermediate-Scale Quantum (NISQ)-era quantum generative adversarial networks (QGANs) suffer from mode collapse and lack explicit control over the generated distribution. Method: We propose InfoQGAN—the first QGAN framework integrating a Mutual Information Neural Estimator (MINE) to maximize mutual information between latent variables and generated samples, enabling interpretable, controllable quantum generation. It employs parameterized quantum circuits within a classical-quantum hybrid training architecture to synthesize multimodal, high-fidelity portfolio return distributions for dynamic asset allocation. Contribution/Results: Empirical evaluation demonstrates that InfoQGAN substantially mitigates mode collapse, reducing KL divergence by 37% relative to baselines, and significantly improves fidelity in capturing peak–valley structures of the true distribution. This establishes a novel paradigm for robust quantum generative modeling in finance.
📝 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, 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.
