BO-QGAN+: A Hybrid Quantum Classical GAN for Drug Molecule Generation on ZINC-250K
Generative models for de novo molecular design have become a cornerstone of computational drug discovery, yet existing hybrid quantum-classical approaches remain confined to small-molecule benchmarks such as QM9 ($\boldsymbol{\leq} \mathbf{9}$ heavy atoms). This work introduces BO-QGAN+, a Bayesian-optimised hybrid quantum generative adversarial network that, for the first time, demonstrates competitive molecular generation on the ZINC-250k benchmark, accommodating molecules of up to 38 heavy atoms. Our primary methodological novelty lies in a multicircuit quantum ensemble generator featuring data reuploading variational circuits, which enables scaling to the substantially larger molecular graphs present in ZINC-250k. This generator is coupled with a Wasserstein critic stabilised by gradient penalty (WGAN-GP) and a graph-convolutional discriminator with spectral normalisation. On a 5,000-molecule ZINC-250k subset, BO-QGAN+ achieves the best Drug Candidate Score (DCS) of 1.244 with 94% molecular validity, a QED of 0.41, normalised logP of 0.71, and normalised SA of 0.43 within 700 training epochs These results are obtained with only 16 quantum parameters alongside 2.03 M classical parameters, demonstrating a parameter efficiency ratio exceeding 127,000:1. At epoch 700, the model further achieves Uniqueness of 0.97, Novelty of 0.84, and Internal Diversity of 0.79, confirming that high validity does not come at the expense of generation diversity. Our findings establish that hybrid quantum generators can operate effectively at molecular scales well beyond prior quantum GAN benchmarks, Key limitations include the use of classical quantum simulation rather than physical NISQ hardware, and evaluation on a 5,000-molecule subset. Nevertheless, our findings open a practical pathway toward quantum-assisted drug discovery pipelines on near-term quantum hardware.