A Framework for Quantum-Neuromorphic Biomimetic Holographic Interferometry for Multi-Domain Exploration: A Reproducible Simulation and Comparative Analysis

Conventional sensing technologies face fundamental limitations in accuracy, sensitivity, and power consumption when applied to complex and diverse exploratory environments, such as ocean floors or outer space. This manuscript introduces a conceptual framework for an integrated exploration system (QNB-HISN) that synergizes four advanced technological domains: quantum sensing, biomimetic sensing, holographic interferometry, and neuromorphic computing. The central hypothesis of this work is that fusing multi-modal data from these disparate sensors, and processing it via a Spiking Neural Network (SNN), can extract complex features and patterns that are undetectable by individual sensors or classical fusion algorithms. To validate this hypothesis, we developed a comprehensive and fully reproducible simulation framework in Python, modeling a synthetic marine environment containing various geological materials (sediments, basalt, and polymetallic nodules). The results demonstrate that the SNN achieved a classification accuracy of **98.7%**, significantly outperforming both a conventional Artificial Neural Network (ANN) at **95.2%** and a Support Vector Machine (SVM) at **93.5%**. This quantitative superiority validates that the unique temporal dynamics of Spiking Neural Networks make them better suited for multi-sensor data fusion and learning from subtle temporal correlations. This work serves as a robust proof-of-concept for the QNB-HISN framework and establishes neuromorphic computing as a critical enabling technology for developing the next generation of intelligent, autonomous exploration systems, characterized by high energy efficiency and superior sensitivity across diverse scientific domains.