[2603.22399] Latent Style-based Quantum Wasserstein GAN for Drug Design

Quantum Physics arXiv:2603.22399 (quant-ph) [Submitted on 23 Mar 2026] Title:Latent Style-based Quantum Wasserstein GAN for Drug Design Authors:Julien Baglio, Yacine Haddad, Richard Polifka View a PDF of the paper titled Latent Style-based Quantum Wasserstein GAN for Drug Design, by Julien Baglio and 2 other authors View PDF HTML (experimental) Abstract:The development of new drugs is a tedious, time-consuming, and expensive process, for which the average costs are estimated to be up to around $2.5 billion. The first step in this long process is the design of the new drug, for which de novo drug design, assisted by artificial intelligence, has blossomed in recent years and revolutionized the field. In particular, generative artificial intelligence has delivered promising results in drug discovery and development, reducing costs and the time to solution. However, classical generative models, such as generative adversarial networks (GANs), are difficult to train due to barren plateaus and prone to mode collapse. Quantum computing may be an avenue to overcome these issues and provide models with fewer parameters, thereby enhancing the generalizability of GANs. We propose a new style-based quantum GAN (QGAN) architecture for drug design that implements noise encoding at every rotational gate of the circuit and a gradient penalty in the loss function to mitigate mode collapse. Our pipeline employs a variational autoencoder to represent the molecular structure in a latent space,...