Synergizing quantum techniques with machine learning for advancing drug discovery challenge - Zhiding Liang; Zichang He; Yue Sun; Dylan Herman; Qingyue Jiao; Yanzhang Zhu; Weiwen Jiang; Xiaowei Xu; Di Wu; Marco Pistoia; Yiyu Shi (Scientific Reports)
Abstract 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.