Quantum-Enhanced Computational Modeling for Personalized Biomedical Implant Design

Abstract

The design of biomedical implants is increasingly shifting toward personalized, patient-specific solutions that account for anatomical variation, biological response, and long-term biomechanical performance. Traditional computational modeling approaches—while effective for standard geometries—face significant limitations when confronted with high-dimensional optimization, nonlinear tissue interactions, and complex material behaviors. This paper explores the emerging role of quantum-enhanced computational modeling in advancing next-generation personalized biomedical implant design. Leveraging hybrid quantum–classical algorithms, quantum-inspired solvers, and probabilistic optimization techniques, quantum computing offers new capabilities for accelerating finite-element simulations, improving structural optimization, and evaluating multi-objective design constraints. The study assesses how quantum methodologies can support implant customization at scale, reduce computational bottlenecks in digital fabrication workflows, and enable more precise prediction of implant–tissue integration. The findings highlight the potential of quantum technologies to reshape the biomedical device pipeline by providing faster design iteration, enhanced modeling fidelity, and more adaptive patient-centered engineering.