[Joint CQSE & NCTS Seminar] Accelerating Virtual Drug Screening via a Hybrid Quantum Annealing and VQE Framework on GPUs

Title: [Joint CQSE & NCTS Seminar] Accelerating Virtual Drug Screening via a Hybrid Quantum Annealing and VQE Framework on GPUs
Speakers: Yi Chou & Kuo-Wei Huang (Compal Electronics, Inc.)
Time: 2026/10/2 (Fri.) 14:20-16:20
Place: Rm. 104, Chin-Pao Yang Lecture Hall, Department of Physics/CCMS, NTU
Online: https://nationaltaiwanuniversity-zbh.my.webex.com/nationaltaiwanuniversity-zbh.my/j.php?MTID=m80ae25729ceb0a2df7d67e9a7320e7a3
 

Abstract
High-throughput virtual screening is critical for early-stage drug discovery, yet classical molecular docking tools frequently struggle to balance conformational search efficiency with the energetic fidelity required for robust ligand prioritization. In this talk, we introduce CGA-QX Docking, a hybrid quantum-classical computing framework integrating GPU-accelerated combinatorial optimization with gate-based quantum algorithms. We further demonstrate the practical application of the Variational Quantum Eigensolver (VQE) for calculating the ground-state energy and dissociation energy of molecular hydrogen (H₂), including the use of hardware-efficient ansatzes and comparisons across GPU-based simulation and physical quantum processors. Results obtained from IQM and IBM quantum hardware are discussed to evaluate the practical performance and limitations of near-term quantum chemistry workflows. This work highlights a pathway toward integrating GPU-accelerated optimization with emerging quantum computing technologies for practical molecular simulation and drug discovery.
 
Biography
Yi Chou is a research engineer at Compal Electronics, Inc., specializing in GPU-accelerated annealing and its applications. She holds degrees in atmospheric science and physics from National Taiwan University, and in computer science (M.S., Ph.D.) from the University of Colorado Boulder. She has published research in quantum control, ultrafast laser science, verification, and Bayesian inference.

Kuo-Wei Huang is a Principal Research Scientist at Compal Electronics. He holds a Ph.D. in Mechanical Engineering from UCLA and an M.S. in Computer Science from the Georgia Institute of Technology. He has extensive cross-disciplinary experience spanning AI/ML applications, bioinformatics, optofluidics, and microfluidic system engineering. He has co-authored over 20 academic publications, holds several U.S. patents, and is a recipient of the SLAS Innovation Award and multiple IEEE Best Paper Awards.