Photonic Neuromorphic Quantum Intelligence for Real-Time Multimodal Fusion across 6G, Robotics, Biomedicine, Earth Observation and Industry 5.0

Abstract: Real-time intelligence increasingly depends on the simultaneous interpretation of heterogeneous signals generated by wireless networks, autonomous machines, biomedical instruments, Earth-observation platforms and cyber-physical industrial assets. Yet contemporary architectures typically optimise communication, sensing, inference and decision-making in separate computational stacks, creating latency, energy and interoperability bottlenecks. This paper proposes the Photonic–Neuromorphic Quantum Real-Time Fusion (PNQ-RTF) framework, a model-based architecture that integrates ultrabroadband photonic signal transformation, event-driven neuromorphic dynamics, multimodal representation learning and quantum-assisted optimisation for cross-domain signal, sensor and decision fusion. The methodology combines matrix fusion, spiking differential dynamics, photonic linear transforms, variational quantum representations, Bayesian uncertainty aggregation and constrained multi-objective optimisation. Five application layers—6G communications, robotics, biomedicine, Earth observation and Industry 5.0—are represented within a common latent state and decision space. An illustrative 0–10 numerical model produces a weighted conventional fusion score of 6.80 and a PNQ-RTF score of 8.30, corresponding to a demonstrative improvement of 22.1%. A second composite index incorporating latency, energy efficiency, uncertainty control and reliability yields a Real-Time Fusion Readiness score of 8.17. These values are illustrative rather than empirical. The study’s principal contribution is an interdisciplinary mathematical framework showing how photonic speed, neuromorphic sparsity and quantum-assisted search can be organised as complementary computational layers while preserving domain-specific safety, explainability and validation requirements. The framework provides a testable basis for future hardware–software co-design and cross-sector benchmarking of next-generation real-time intelligent systems. Keywords: photonic computing; neuromorphic intelligence; quantum artificial intelligence; real-time signal fusion; multimodal sensor fusion; 6G; autonomous robotics; biomedical signal processing; hyperspectral Earth observation; Industry 5.0; spiking neural networks; quantum optimisation; edge intelligence; digital twins; uncertainty-aware decision fusion