Quantum–Photonic Topological Intelligence for Multiscale Biomedicine: Brain Networks, Biosignals, Spatial Multi-Omics and Autonomous Therapeutics
Year: 2026
Type: article
Abstract: : Biomedical intelligence increasingly depends on integrating data that are heterogeneous not only in format but also in biological scale: dynamic brain networks, high-frequency physiological signals, spatially resolved molecular measurements and treatment-response trajectories. This paper develops a unified conceptual and mathematical architecture—Quantum–Photonic Topological Intelligence for Multiscale Biomedicine (QPTI-MB)—for representing, fusing and governing these data without reducing clinically important structure to a single flat feature space. The proposed framework combines graph and simplicial representations, persistent topological descriptors, multimodal learning, hybrid quantum–photonic feature maps and constrained therapeutic optimization. The methodology is model-based. No patient-level dataset is supplied; therefore, the study uses an explicitly illustrative 0–10 numerical scoring model and does not claim empirical clinical superiority. The framework formalizes four coupled layers: topology-preserving biomedical encoding, quantum–photonic representation, uncertainty-aware cross-modal fusion and benefit–risk–cost constrained therapeutic decision support. A risk-adjusted composite score demonstrates how technical capability can be discounted by uncertainty, bias and translational risk. The analysis indicates that the value of the architecture lies less in any single computational technology than in disciplined multiscale coupling: persistent representations can stabilize structural information across sampling regimes; multimodal fusion can connect electrophysiology with spatial molecular states; and governed optimization can transform predictions into constrained therapeutic recommendations. The paper contributes a named framework, equations, variables, validation logic, scenario analysis and a translational roadmap. Future empirical work should benchmark classical, topological, quantum-inspired and quantum–photonic components under matched datasets, prospective validation, fairness constraints and clinically meaningful endpoints before any claim of deployment advantage is made. Keywords: quantum photonics; topological data analysis; persistent homology; brain connectivity; EEG; ECG; spatial multi-omics; precision oncology; multimodal learning; quantum neural networks; biomedical signal intelligence; uncertainty quantification; autonomous therapeutics; precision medicine; explainable clinical AI
Author: Murali Krishna Pasupuleti
Institution: National Education Services
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7204665787
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