使用Transformer模型学习制备分子基态
Learning to Prepare Molecular Ground States with Transformer Models
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中文总结 AI 辅助
研究如何用Transformer模型制备分子基态，提出ADAPT-GQE框架，先以ADAPT-VQE生成参考电路训练模型，经强化学习提高电路生成精度，相比ADAPT-VQE大幅减少电路生成时间，在量子硬件上演示，为量子计算化学自动合成电路开辟道路。
中文摘要 AI 辅助
量子态制备是许多量子算法的关键组成部分。在量子化学应用中高效执行此步骤对于实现实际量子优势至关重要。像ADAPT-VQE这样的迭代算法能产生浅基态制备电路，但对于与材料科学和药物开发相关的大分子来说计算成本过高。本文引入ADAPT-GQE，一种生成式人工智能框架，用于学习合成电子结构计算的基态制备电路。首先用ADAPT-VQE生成高质量参考电路，作为训练电路生成模型的目标。训练后的模型能高效提出并评估电路，通过强化学习提高电路生成精度。该流程在保持或提高态制备精度的同时，使电路生成时间相对于ADAPT-VQE减少了几个数量级。在三环抗抑郁药丙咪嗪上进行了演示，在Quantinuum Helios-1上执行生成的电路，这代表了人工智能生成的量子化学电路在先进量子硬件上的一个里程碑。这些结果为实用规模的量子计算化学的自动量子电路合成开辟了道路。
英文摘要
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.
发表机构
- Quantinuum
- NVIDIA Corporation（NVIDIA公司）
- Chemical R&D（化学研发部）
- Center for Digital Innovation（数字创新中心）
机构由 AI 辅助整理，请以论文原文为准。
