Hybrid Quantum Generative Adversarial Networks for Molecular Simulation and Drug Discovery

TL;DR

This paper presents three novel quantum generative adversarial network architectures for molecular simulation, demonstrating that one variant outperforms others in drug molecule property prediction using quantum simulation on the QM9 dataset.

Contribution

Introduction of three new quantum GAN architectures with different configurations and quantum circuit layers for molecular modeling and drug discovery.

Findings

QWGAN-HG-GP outperforms other models in property metrics

Quantum GANs show promise over classical methods in molecular simulation

Quantum simulation used effectively for training on QM9 dataset

Abstract

In molecular research, the modelling and analysis of molecules through simulation is an important part that has a direct influence on medical development, material science and drug discovery. The processing power required to design protein chains with hundreds of peptides is huge. Classical computing techniques, including state-of-the-art machine learning models being deployed on classical computing machines, have proven to be inefficient in this task, though they have been successful in a limited way. Moreover, current practical implementations, as opposed to purely theoretical modelling, are often infeasible in terms of both time and cost. One of the major areas where quantum machine learning is expected to have a profound advantage over classical algorithms is drug discovery. Quantum generative models have given some promising benefits in recent studies. This paper introduces three novel quantum generative adversarial network (QGAN) architecture variants resulting from different configurations, various quantum circuit layers and patched ansatz. A quantum simulator from Xanadu's PennyLane was utilized for executing the QGAN models trained on the QM9 dataset. Upon evaluation, one of the models, namely the QWGAN-HG-GP (Wasserstein distance with gradient penalty) model, outperformed the other QGAN models in different drug molecule property metrics.