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

This paper introduces the Quantum-Neuromorphic Biomimetic Holographic Interferometry System (QNB-HISN), a novel framework designed to overcome the limitations of conventional sensing technologies in extreme environments such as deep-sea, planetary, and biomedical domains. The system synergistically integrates high-sensitivity quantum sensors (NV-center magnetometers), low-power biomimetic flow arrays, and high-resolution holographic interferometry. The core of the framework is a Spiking Neural Network (SNN), which processes the multi-modal sensor data. The central hypothesis is that the SNN's event-driven, temporal processing capabilities enable the fusion of heterogeneous sensor data to extract complex spatio-temporal correlations that are inaccessible to conventional machine learning algorithms. The framework's efficacy was validated through a reproducible Python-based simulation of a 100x100 pixel seafloor environment with three distinct material classes: sediment, basalt, and polymetallic nodules. In this task, the SNN's performance was benchmarked against an Artificial Neural Network (ANN) and a Support Vector Machine (SVM). The results demonstrate that the SNN achieved a superior classification accuracy of 98.7%, significantly outperforming both the ANN (95.2%) and the SVM (93.5%). Statistical analysis confirmed the significance of these results (p < 0.01), attributing the SNN's success to its proficiency in leveraging temporal dynamics across the multi-modal data streams. The study provides a comprehensive theoretical foundation for the system, details its architecture, and offers open-source simulation code to ensure reproducibility. The findings establish QNB-HISN as a robust proof-of-concept for an intelligent, adaptive, and low-power sensing system with significant potential to advance autonomous exploration and diagnostics in challenging scientific and medical domains.