Dissipative quantum generative adversarial networks

TL;DR

This paper introduces a novel unsupervised quantum generative adversarial network (DQGAN) using dissipative quantum neural networks to learn and reproduce quantum data characteristics, expanding quantum machine learning capabilities.

Contribution

It presents the first implementation of a DQGAN, demonstrating unsupervised learning of quantum states with dissipative quantum neural networks, a new approach in quantum generative modeling.

Findings

Successful training of DQGANs to reproduce quantum data

Demonstration of unsupervised learning in quantum neural networks

Proof of concept for extending unlabeled quantum datasets

Abstract

Noisy intermediate-scale quantum (NISQ) devices build the first generation of quantum computers. Quantum neural networks (QNNs) gained high interest as one of the few suitable quantum algorithms to run on these NISQ devices. Most of the QNNs exploit supervised training algorithms with quantum states in form of pairs to learn their underlying relation. However, only little attention has been given to unsupervised training algorithms despite interesting applications where the quantum data does not occur in pairs. Here we propose an approach to unsupervised learning and reproducing characteristics of any given set of quantum states. We build a generative adversarial model using two dissipative quantum neural networks (DQNNs), leading to the dissipative quantum generative adversarial network (DQGAN). The generator DQNN aims to produce quantum states similar to the training data while the discriminator DQNN aims to distinguish the generator's output from the training data. We find that training both parts in a competitive manner results in a well trained generative DQNN. We see our contribution as a proof of concept for using DQGANs to learn and extend unlabeled training sets.