Quantum Enhanced Language Models for Aviation Safety Intelligence and Predictive Operations

This research introduces the Quantum Information System for Aerospace Safety Intelligence(QISASI), the versatile and a scalable Quantum Intelligence (QI) and Natural Language Processing (NLP) methods the proposed QISASI framework to enhance the aviation safety, streamline with its operational workflows, and it delivers the predictive insights for the quantum enabled maintenance for its mission support systems. The key innovation of QISASI is Quantum Self Attention Based on the Contextual Feature Fusion (QSACF²) module, which encodes the multimodal aviation data such as its textual reports, audio communication transcripts, radar telemetry, and ADS-B signals into its quantum states using the ZZFeatureMap and multilayer quantum gate operations. The QISASI architecture enables the efficiency of its modeling of complex and high dimensional interactions, which is significant and it improves its capabilities in the anomaly detection, risk forecasting, and predictive maintenance. Therefore, the experiments which are conducted on real world ATCOSIM and ADS-B datasets demonstrates that the QISASI substantially method achieving the exceptional accuracy of up to 99.99%, thereby validating its robustness for aviation safety intelligence and effectiveness of the proposed hybrid quantum classical learning approach in real world aviation diagnostics.