Learning to Prepare Molecular Ground States with Transformer Models
Quantum state preparation is a key component of many quantum algorithms. Performing this step efficiently is essential for realizing practical quantum advantage in quantum chemistry applications. Iterative algorithms like ADAPT-VQE can produce shallow ground-state preparation circuits, but become computationally prohibitive for the larger molecules relevant to materials science and pharmaceutical development. Here, we introduce ADAPT-GQE, a generative AI framework that learns to synthesize ground-state preparation circuits for electronic structure calculations. We first use ADAPT-VQE to generate high-quality reference circuits, which are then used as targets for training models for circui...
| Authors | Alex Koziell-Pipe, Jasmine Brewer, Jem Guhit et al. |
|---|---|
| Primary category | quant-ph |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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