Quantum-Inspired Graph Neural Digital Twins for the sub-microsecond topological co-design and temporal degradation modeling of cellulose, hemicellulose, and lignin smart gels.

The clinical translation of lignocellulosic smart gels is constrained by empirical formulation screening, complex structure-property relationships, and uncertainty in degradation and therapeutic-release behavior. This study aims to determine whether a unified computational digital-twin framework can (i) optimize the crosslinking topology of cellulose-, hemicellulose-, and lignin-based matrices, (ii) predict their mechanical and temporal degradation behavior, and (iii) translate optimized formulations into real-time control parameters for microfluidic synthesis. To address these objectives, we develop a Quantum-Inspired Graph Neural Digital Twin (Q-GNDT) that represents molecular interactions as dynamic non-Euclidean graphs and formulates topological optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem. A temporal graph-decay model couples pH-dependent hydrolysis, enzymatic cleavage, percolation loss, and drug diffusion to predict time-resolved degradation and release. The computational architecture is integrated with an edge-AI control model designed for sub-microsecond microfluidic actuation. Retrospective cross-validation against published experimental datasets from three independent studies yielded mean absolute errors of 2.1% for pore size, 2.9% for storage modulus, and 1.5% for drug-release kinetics, with an overall RMSE of 0.74%. An in-silico targeted-delivery case study further produced 4.1% initial burst release, 98.0% cumulative delivery, and near-linear release kinetics (R2 = 0.991). These results indicate that Q-GNDT can serve as a computational design and screening framework for narrowing the experimental search space of programmable lignocellulosic biomaterials. Prospective synthesis, biological testing, and in vivo validation remain necessary before clinical translation.