PhD Student · Computer Science @ Kent State University · Advisor: Dr. Qiang Guan
Visiting Researcher @ Cleveland Clinic · PI: Dr. Feixiong Cheng
Quantum Talents Climbing Program 2023 · SZIQA · Advisor: Dr. Juan Yao
Quantum Computing × Drug Discovery
I am a PhD student in Computer Science, focusing on quantum computing. Previously, I worked as a Research Assistant at the University of Technology Sydney (UTS) and the Shenzhen International Quantum Academy (SZIQA), and also participated in a summer research program at the University of Science and Technology of China (USTC).
My research focuses on applying quantum computing to drug discovery and developing corresponding quantum algorithms and engineering frameworks.
My hardware platform primarily involves IBM's superconducting quantum computers, including Eagle r3 (127 qubits) and Heron r3 (156 qubits).
Current methodologies include Variational Quantum Algorithms (VQA), SQD, Quantum-HPC hybrid frameworks, and Quantum-AI hybrid algorithms. The current research scope spans protein-level structure prediction, energy surface reconstruction, molecular-level ground-state energy estimation, drug usability analysis, and DNA/RNA sequence prediction.
Building practical quantum frameworks on utility-level quantum processors. Starting from amino acid sequences, algorithms such as VQE are employed to identify low-energy conformations corresponding to stable protein structures.
Integrating AI models and classical algorithms to expand the system’s information capacity, enabling quantum algorithms to surpass intrinsic QPU limitations. under current hardware constraints.
Designing quantum algorithms and frameworks that bridge hierarchical levels— from protein structures to molecular interactions—across different physical and chemical scales to advance quantum-enabled drug discovery.
Yuqi Zhang, Yuxin Yang, Weiwen Jiang, Ruth Nussinov, Joseph Loscalzo, Qiang Guan*, Feixiong Cheng*, et al. “A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors.” Advanced Science (IF = 14.3, 2025), Accepted. DOI: 10.1002/advs.202513641. [Google Scholar]
Yuqi Zhang, Yuxin Yang, Cheng-Chang Lu, Weiwen Jiang, Feixiong Cheng, Bo Fang, Qiang Guan*. “QDockBank: A Dataset for Ligand Docking on Protein Fragments Predicted on Utility-Level Quantum Computers.” In SC’25 : The International Conference for High Performance Computing, Networking, Storage and Analysis, St. Louis, MO, USA, Nov 2025. Artifact Available. [Google Scholar]
Yuqi Zhang, Yuxin Yang, Feixiong Cheng, Nima Saeidi, Samuel L. Volchenboum, Junhan Zhao, Siwei Chen, Qiang Guan*, et al. “A Hybrid Quantum-AI Framework for Protein Structure Prediction on NISQ Devices.” Under review, 2025. [Google Scholar]
I would like to express my deepest gratitude to all the mentors and colleagues who have guided and supported me: my advisor Dr. Qiang Guan, and those who have supervised or inspired my research, including Dr. Juan Yao @ SZIQA, Dr. Zhaofeng Su @ USTC, Dr. Sanjiang Li @ UTS, and Dr. Yuan Feng @ THU.
I also sincerely thank my teammates and friends in the 419 Lab — Hang Yu (余航), Yiyan Cheng (程义艳), Rongqi Lu (陆荣琦), Zhuoqing Xiao (肖卓青), Yongzhi Li (李勇志),and Yao Xiao (肖耀) — for their collaboration, encouragement, and shared pursuit of scientific discovery.
I am especially grateful to my friend Haojie Zhang (张豪杰), who first guided me into the world of quantum computing.