A research collaboration between Oak Ridge National Laboratory (ORNL), the Cleveland Clinic, and IBM Quantum has completed the first heterogeneous quantum-classical simulation of tritium binding within a liquid inorganic molten salt. Released as a preprint on arXiv by a team including ORNL Section Head Tom Beck, Corporate Research Fellow Al Geist, and Cleveland Clinic staff scientist Dr. Kenneth Merz Jr., the project uses quantum-centric supercomputing to resolve electronic ground-state energies for clusters of fluorine, lithium, and beryllium (FLiBe; 2LiF–BeF2). The joint venture serves as a baseline proof-of-concept for the U.S. Department of Energy’s (DOE) Genesis Mission, which aims to eliminate the tritium extraction and fuel-breeding bottlenecks that hinder commercial nuclear fusion power plants. [ ORNL - LBNL - IBM Simulation Stack ] Compute Architecture─► Heterogeneous CPUs, GPUs, and cloud-accessible IBM Quantum QPUs. Physical Mechanism ─► Active tritium extraction & conformational energy modeling in liquid FLiBe blankets. Algorithmic Engine ─► Embedded-Wavefunction (EWF) partitioning & Extended Sample-Based Quantum Diagonalization.To achieve self-sustaining nuclear fusion within magnetic-confinement tokamaks, reactors must breed their own tritium (3H) fuel on-site by wrapping the high-temperature plasma wall in a thick blanket of liquid salt. When high-energy neutrons bombard lithium-6 atoms inside the fluid, they split to yield fresh tritium gas. However, optimizing the chemical recipe of a liquid salt that shifts dynamically under radiation, magnetic fields, and intense thermal loads is an intractable challenge for classical supercomputers. Classical techniques like Density Functional Theory (DFT) introduce free-energy error margins as high as 10%, failing to predict whether the liberated tritium will drift out safely as a harvestable gas or bind with fluorine to create corrosive tritium fluoride (TF). To overcome the exponential scaling overhead of tracking subatomic electron correlations in a highly polarized, charged ionic mixture, the team adapted an automated wave function-based embedding framework originally deployed to model