这个框架能自动设计量子化学电路，比传统ADAPT-VQE快10倍，还在真实量子计算机上跑了实验。搞量子计算化学的可以看看。
ADAPT-GQE是一个生成式AI框架，利用ADAPT-VQE生成的参考电路训练模型，再通过强化学习提升电路生成精度。在抗抑郁药分子imipramine上测试，电路生成时间比ADAPT-VQE减少一个数量级，准确度相当或更优。生成的电路已在Quantinuum Helios-1量子硬件上成功执行，这是AI生成量子化学电路在先进量子硬件上的里程碑。
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 circuit generation. Once trained, the model can efficiently propose and score circuits, enabling reinforcement learning (RL) to drive circuit generation accuracy beyond the accuracy of the ADAPT-VQE training data. This pipeline achieves order-of-magnitude reductions in circuit generation time relative to ADAPT-VQE while maintaining comparable or improved state-preparation accuracy. We demonstrate ADAPT-GQE on imipramine, a well-established tricyclic antidepressant that serves as a representative, challenging target for computational modelling in drug stability protocols. We execute generated circuits on Quantinuum Helios-1, representing a milestone for AI-generated quantum chemistry circuits on state-of-the-art quantum hardware. These results establish a pathway toward automated quantum circuit synthesis for utility-scale quantum computational chemistry.