Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation

Hybrid quantum-classical machine learning offers a path to leverage noisy intermediate-scale quantum (NISQ) devices for drug discovery, but optimal model architectures remain unclear. We systematically optimize the quantum-classical bridge architecture for generative adversarial networks (GANs) in molecular discovery using multi-objective Bayesian optimization. Our optimized model (BO-QGAN) significantly improves performance, achieving a 2.27-fold higher Drug Candidate Score (DCS) than prior quantum-hybrid benchmarks and 2.21-fold higher than the classical baseline, using over 60% fewer parameters. Key findings favor layering multiple (3-4) shallow (4-8 qubit) quantum circuits sequentially, while classical architecture shows less sensitivity above a minimum capacity. This work provides the first empirically grounded architectural guidelines for hybrid models, enabling more effective integration of current quantum computers into pharmaceutical research pipelines.

@article{smith2025_2506.01177,
  title={ Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation },
  author={ Andrew Smith and Erhan Guven },
  journal={arXiv preprint arXiv:2506.01177},
  year={ 2025 }
}