Liang Zhiding, He Zichang, Sun Yue, Herman Dylan, Jiao Qingyue, Zhu Yanzhang, Jiang Weiwen, Xu Xiaowei, Wu Di, Pistoia Marco, Shi Yiyu
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
JPMorgan Chase, Global Technology Applied Research, New York, NY, 10017, USA.
Sci Rep. 2024 Dec 28;14(1):31216. doi: 10.1038/s41598-024-82576-4.
The Quantum Computing for Drug Discovery Challenge, held at the 42nd International Conference on Computer-Aided Design (ICCAD) in 2023, was a multi-month, research-intensive competition. Over 70 teams from more than 65 organizations from 12 different countries registered, focusing on the use of quantum computing for drug discovery. The challenge centered on designing algorithms to accurately estimate the ground state energy of molecules, specifically OH+, using quantum computing techniques. Participants utilized the IBM Qiskit platform within the constraints of the Noisy Intermediate Scale Quantum (NISQ) era, characterized by noise and limited quantum computing resources. The contest emphasized the importance of accurate estimation, efficient use of quantum resources, and the integration of machine learning techniques. This competition highlighted the potential of hybrid classical-quantum frameworks and machine learning in advancing quantum computing for practical applications, particularly in drug discovery.
2023年在第42届计算机辅助设计国际会议（ICCAD）上举办的药物发现量子计算挑战赛是一项为期数月、研究密集型的竞赛。来自12个不同国家65多个组织的70多个团队报名参赛，专注于将量子计算用于药物发现。挑战赛的核心是设计算法，利用量子计算技术准确估计分子（特别是OH+）的基态能量。参赛者在有噪声的中等规模量子（NISQ）时代的限制下使用IBM Qiskit平台，该时代的特点是存在噪声且量子计算资源有限。竞赛强调了准确估计、高效利用量子资源以及整合机器学习技术的重要性。这场比赛突出了混合经典 - 量子框架和机器学习在推动量子计算实际应用（特别是在药物发现方面）的潜力。
