Quantum driven Neuro spatial Cognition for Multilayered Data Interpretation Using AI augmented Spatiotemporal Modelling

Artificial Intelligence systems face increasing challenges in processing and interpreting complex, high-dimensional, and multilayered datasets that evolve across time and space. To address this, we propose a novel architecture called Quantum driven Neuro spatial Cognition (QNSC), a next-generation AI framework that integrates quantum-inspired encoding, neuro-symbolic modeling, and spatiotemporal learning to enhance interpretability and decision-making in uncertain environments. Unlike conventional deep neural networks and transformers, QNSC utilizes entangled memory layers and phase-shifted signal processors, enabling dynamic memory reallocation and long-term pattern recognition across spatiotemporal sequences. The system incorporates a quantum attention modulation layer (QAML) that improves computational efficiency by 26% while preserving data fidelity. Extensive experiments were conducted on three real-world datasets: geospatial climate sequences, multimodal medical imaging, and industrial sensor streams. QNSC demonstrated a significant performance gain, achieving 94.6% prediction accuracy in event detection, a 32.5% increase in spatial-temporal coherence, and a 28.1% reduction in false positives when compared to state-of-the-art transformer-based models. In addition, the model achieved a training convergence speed-up of 2.3× and demonstrated robust generalization under adversarial noise injections and missing data scenarios. QNSC also produced interpretable output maps that visually localized decisioninfluencing zones with 87% relevance alignment, making it suitable for critical domains such as healthcare, disaster prediction, and autonomous systems.