Learning to Prepare Molecular Ground States with Transformer Models

Quantum chemistry simulations require efficient state-preparation circuits, but iterative methods like ADAPT-VQE become computationally prohibitive for large, pharmaceutically relevant molecules. ADAPT-GQE addresses this with a generative AI framework trained on ADAPT-VQE-generated reference circuits, then improved further via reinforcement learning to exceed the training data's accuracy. Demonstrated on imipramine, a tricyclic antidepressant, and executed on Quantinuum's Helios-1 quantum hardware, the approach achieves order-of-magnitude speedups in circuit generation. Applications span drug discovery, materials science, and utility-scale quantum computational chemistry, marking a step toward automated, AI-driven quantum circuit synthesis for real-world molecular modeling. Authors: Alex Koziell-Pipe, Jasmine Brewer, Jem Guhit, Marwa H. Farag, Kripa Panchagnula, Gabriel Laude, Fabian Finger, Carlo Gaggioli, Ludmila Szulakowska, Oliver J. Backhouse, Christos Papalitsas, Jason G. Mustakis, Thomas Soini, David Munoz Ramo, Stephen Clark, Elica Kyoseva, Enrico Rinaldi Paper: https://arxiv.org/abs/2607.22468v1