AI自动发现高效量子算法，显著降低分子基态计算资源消耗
Automated near-term quantum algorithm discovery for molecular ground states
- 用AI演化算法搜索框架，自动设计量子化学计算新算法
- 对LiH、H2O、F2分子实现更低资源消耗，达到化学精度
- 可解释性强，适合量子算法研究者与硬件开发者参考
设计量子算法是一项复杂且反直觉的任务，非常适合由人工智能驱动的算法发现。为此，我们采用Hive这一基于大语言模型的程序合成AI平台，通过高度分布式进化过程发现新算法。聚焦量子化学中的基态问题，我们发现了针对分子LiH、H2O和F2的高效量子启发式算法，在量子资源消耗上显著低于当前最先进的近中期量子算法。进一步地，我们对发现的算法进行了可解释性分析，识别出提升效率的关键功能。最后，我们在Quantinuum System Model H2量子计算机上对Hive发现的电路进行了基准测试，并确定了实现化学精度的最低系统要求。我们设想，这种新型量子算法发现方法可拓展至化学以外的领域，也适用于容错量子计算机算法设计。
原文摘要 · Abstract (English)
Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform for program synthesis, which utilises large language models to drive a highly distributed evolutionary process for discovering new algorithms. We focus on the ground state problem in quantum chemistry, and discover efficient quantum heuristic algorithms that solve it for molecules LiH, H2O, and F2 while exhibiting significant reductions in quantum resources relative to state-of-the-art near-term quantum algorithms. Further, we perform an interpretability study on the discovered algorithms and identify the key functions responsible for the efficiency gains. Finally, we benchmark the Hive-discovered circuits on the Quantinuum System Model H2 quantum computer and identify minimum system requirements for chemical precision. We envision that this novel approach to quantum algorithm discovery applies to other domains beyond chemistry, as well as to designing quantum algorithms for fault-tolerant quantum computers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。