Generative Adversarial Variational Quantum Kolmogorov-Arnold Network

TL;DR

This paper introduces a quantum-enhanced generative adversarial network using a variational quantum Kolmogorov-Arnold network as the generator, achieving higher accuracy with fewer parameters on image datasets.

Contribution

It presents a novel quantum-based generator for GANs, improving learning efficiency and accuracy over classical and existing quantum methods.

Findings

Achieved higher accuracy on MNIST and CIFAR10 datasets.

Used fewer parameters than traditional neural networks.

Demonstrated quantum advantage in generative modeling.

Abstract

Kolmogorov Arnold Networks is a novel multilayer neuromorphic network that can exhibit higher accuracy than a neural network. It can learn and predict more accurately than neural networks with a smaller number of parameters, and many research groups worldwide have adopted it. As a result, many types of applications have been proposed. This network can be used as a generator solely or with a Generative Adversarial Network; however, KAN has a slower speed of learning than neural networks for the number of parameters. Hence,it has not been researched as a generator. Therefore, we propose a novel Generative Adversarial Network called Generative Adversarial Variational Quantum KAN that uses Variational Quantum KAN as a generator. This method enables efficient learning with significantly fewer parameters by leveraging the computational advantages of quantum circuits and their output distributions. We performed the training and generation task on MNIST and CIFAR10, and revealed that our method can achieve higher accuracy than neural networks and Quantum Generative Adversarial Network with less data.