Quantum Networks Meets Regenerative Medicine: Quantum Computing Applications in Stem Cell Research

Abstract

The complexity of stem cell biology, multiscale molecular interactions, stochastic differentiation, and microenvironmental dependencies creates computational and experimental bottlenecks that slow translation to clinical therapies. Quantum computing introduces new algorithmic primitives capable of addressing targeted subproblems in regenerative medicine: high-fidelity molecular simulation, quantum machine learning (QML) for high-dimensional noisy data, and quantum optimization for combinatorial experimental design. This manuscript provides a submission-ready, detailed hybrid quantum–classical methods framework for stem cell research, including experimental design, quantum routines (VQE, QML kernels, QAOA), closed-loop automation workflows, pseudocode, validation metrics, ethical and regulatory considerations, and reproducible implementation notes. The paper synthesizes foundational and recent applied literature to propose a practical roadmap for quantum-accelerated regenerative medicine.