Quantum-Inspired Computational Modeling of Spatial Immune Aging: A Tensor Product Approach

Background: Immunosenescence, the age-related decline in immune function, represents a critical challenge in geriatric medicine. Traditional modeling approaches fail to capture the spatial heterogeneity and compositional complexity of the aging immune system.Methods: We developed a quantum-inspired tensor product Hilbert space framework integrating 11 immune cell types across 8 tissue compartments (dimension: 88). Two cohorts—young adults (<50 years) and elderly individuals (>65 years)—were simulated using empirically derived distributions from recent immune cell census studies. State evolution was governed by a spatial Hamiltonian incorporating intratissue dynamics, cellular trafficking, and cytokine-mediated coupling.Results: The elderly cohort exhibited hallmark immunosenescence signatures: 30% reduction in naive T lymphocytes (p<0.001), 25% expansion of NK cells (p<0.001), and 33% impairment in migration capacity. Tissue-specific Shannon entropy decreased by 8-12% across major compartments (lymph nodes: -11%, bone marrow: -8%, peripheral blood: -9%), providing a quantitative metric for immune aging. Enhanced intertissue coupling (1.5-fold increase) captured inflammaging signatures. Multivariate analysis yielded a composite aging index with 89% discrimination accuracy (AUC=0.89, 95% CI: 0.83-0.94).Conclusions: Information-theoretic diversity metrics derived from spatially resolved models provide quantifiable biomarkers of immunological aging with strong clinical correlations. This framework enables personalized immune age assessment, vaccine responsiveness prediction, and rational design of immune rejuvenation strategies.