Convergence Technology 2
Quantum & Bio
The CT2 research group develops quantum and hybrid quantum–classical computational technologies to model, predict, and intervene in core aging processes. By formulating quantitative aging models based on complex system dynamics and phase transition theory, the group advances quantum simulation algorithms and data-encoding methods tailored to biological and pharmacological variables. CT2 further builds classical–quantum hybrid neural networks and molecular generation platforms to enable efficient simulation, prediction, and design of aging-related molecular interventions, establishing next-generation computational tools for aging research and drug discovery.
Interdisciplinary research with other groups
CT2 conducts interdisciplinary convergence research by providing quantum simulation and hybrid AI platforms that support biological discovery and therapeutic development across other research groups. In collaboration with basic aging research teams, CT2 models key aging mechanisms and generates predictive data to guide target identification and intervention strategies. Through close integration with convergence engineering groups, CT2 translates quantum-informed simulations and molecular design tools into practical computational platforms, strengthening cross-disciplinary efforts in AI-driven drug discovery, systems biology, and precision therapeutics for aging-related diseases.
Investigators
