How Quantum Computing Could Improve Generative AI: What a New Drug-Discovery Study Reveals Article Highlights A newly published, peer-reviewed study in Scientific Reports offers some of the clearest evidence yet that annealing quantum computers could meaningfully improve the outputs of generative AI models. - Conducted in collaboration between D-Wave and Shionogi & Co., Ltd.: the researchers used a D-Wave annealing quantum computer to improve how an AI model designs new drug candidate molecules. - The quantum-assisted model generated molecules that were more chemically valid and more “drug-like” than those produced by an equivalent classical-only AI model. - The implications of this research for AI are not limited to drug discovery: based on the results, annealing quantum computing could act as a general-purpose stochastic generator inside generative AI, sampling more effectively from the “in-between” spaces that a model hasn’t directly seen in training. These results show promising evidence that annealing quantum computing can act as a practical enhancement to AI today. By Mohsen Rahmani, Matthew Woolway, Vladimir Vargas-Calderón, William Kim, Kevin Chern, and Mohammad Amin A newly published, peer-reviewed study in Scientific Reports offers some of the clearest evidence yet that annealing quantum computers could meaningfully improve the outputs of generative AI models. The research, titled “Molecular design beyond training data with novel extended objective functionals of generative AI models driven by quantum annealing computer,” was conducted in collaboration between D-Wave and Shionogi & Co., Ltd. (“Shionogi”) (formerly the pharmaceutical division of Japan Tobacco). Together, we used a D-Wave™ annealing quantum computer to improve how an AI model designs new drug candidate molecules. The result: molecules that were more chemically valid and more “drug-like” than those produced by an equivalent classical-only AI model. The molecules generated by the quantum-assisted model even exceeded the quality of the very data the model was