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.
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.
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.
The models used for facilitating Risk Analysis have undergone notable changes in recent times.This can be attributed to the significant progress made in computing capabilities, as well as the breakthroughs achieved in the field of artificial intelligence.These developments have allowed for the effective extraction of information from complex and unstructured datasets.A particularly useful tool in this regard is Natural Language Processing (NLP), which facilitates the extraction, organization, and classification of information from textual sources.This enables the identification of patterns and trends in an automated fashion.The emergence of Quantum Computing (QC) over the last fifty years has made it one of the most significant fields in computational science.Although fully scalable QC remains elusive, the availability of intermediate noisy scalable QC devices has enabled the realization of nearterm computation on quantum devices using quantum algorithms.QC can (i) solve classically intractable problems and (ii) problems that, while tractable, are classically infeasible.Given the significant computational resources required by state-of-the-art NLP models to extract meaning from text, exploring methods to address such problems using QC would be valuable.The primary aim of this study is to develop a model based on Quantum Natural Language Processing (QNLP) to classify whether human error caused aviation accidents, based on the accident narrative.The research utilizes a database of accident investigation reports conducted by the National Transportation Safety Board (NTSB) spanning from 1982 to 2022.
The integration of quantum computing and neural networks has emerged as a transformative approach for addressing complex industrial challenges, particularly in predictive maintenance.This paper is the result of insights gained during the development of a Proof of Concept (PoC) in collaboration with Embraer, a leading aerospace company in Brazil, and SENAI UpLab, an innovation and technology hub of SENAI, one of Brazil’s foremost institutions for industrial education and applied research. Traditional predictive maintenance methods often struggle with the high-dimensional data and complex failure patterns inherent in aerospace systems. Leveraging the principles of quantum mechanics, QNNs and QSVCs offer enhanced computational power and the ability to process vast datasets exponentially faster than classical machine learning approaches. We propose a novel hybrid framework that combines QNNs and QSVCs, utilizing quantum superposition, entanglement, and kernel-based quantum algorithms to model the degradation patterns of robotic arm components. This enables early detection of potential failures and optimizes maintenance schedules with improved accuracy. The framework is validated using real-world data from aerospace robotic systems provided by EMBRAER, demonstrating superior performance in terms of accuracy, efficiency, and robustness compared to classical machine learning methods. Our results, supported by the collaboration with SENAI UpLab, highlight the potential of quantum-enhanced techniques, including QNNs and QSVCs, to revolutionize predictive maintenance in the aerospace industry, reducing downtime, minimizing costs, and enhancing operational safety. This study contributes to the growing body of research at the intersection of quantum computing and industrial applications, paving the way for future advancements in intelligent maintenance systems.
The integration of quantum computing and neural networks has emerged as a transformative approach for addressing complex industrial challenges, particularly in predictive maintenance.This paper is the result of insights gained during the development of a Proof of Concept (PoC) in collaboration with Embraer, a leading aerospace company in Brazil, and SENAI UpLab, an innovation and technology hub of SENAI, one of Brazil’s foremost institutions for industrial education and applied research. Traditional predictive maintenance methods often struggle with the high-dimensional data and complex failure patterns inherent in aerospace systems. Leveraging the principles of quantum mechanics, QNNs and QSVCs offer enhanced computational power and the ability to process vast datasets exponentially faster than classical machine learning approaches. We propose a novel hybrid framework that combines QNNs and QSVCs, utilizing quantum superposition, entanglement, and kernel-based quantum algorithms to model the degradation patterns of robotic arm components. This enables early detection of potential failures and optimizes maintenance schedules with improved accuracy. The framework is validated using real-world data from aerospace robotic systems provided by EMBRAER, demonstrating superior performance in terms of accuracy, efficiency, and robustness compared to classical machine learning methods. Our results, supported by the collaboration with SENAI UpLab, highlight the potential of quantum-enhanced techniques, including QNNs and QSVCs, to revolutionize predictive maintenance in the aerospace industry, reducing downtime, minimizing costs, and enhancing operational safety. This study contributes to the growing body of research at the intersection of quantum computing and industrial applications, paving the way for future advancements in intelligent maintenance systems.
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed predictive analytics across domains such as healthcare, finance, and environmental sciences. However, traditional ML models face limitations when dealing with large-scale, high-dimensional datasets, particularly due to computational inefficiencies and the "curse of dimensionality." Quantum computing offers promising solutions by leveraging superposition and entanglement to perform complex computations at exponential speeds. Objective: This study aims to explore how quantum computing can enhance AI and ML models, particularly in the domain of predictive analytics. It proposes a comprehensive Quantum-AI framework that integrates quantum technologies into existing predictive systems to overcome the challenges posed by classical approaches. Methods: The study employed hybrid quantum-classical models using frameworks like Qiskit, TensorFlow Quantum, and Pennylane. Datasets were sourced from real-world applications in finance, healthcare, and climate science. Experiments were designed to compare classical and quantum- enhanced models on metrics such as accuracy, scalability, and computational time. Specific algorithms used include Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN). Result: The proposed quantum-enhanced models showed significant improvements: • Accuracy increased from 85% (classical) to 92% (quantum) in finance-related predictions. • Diagnostic models in healthcare achieved a 94% accuracy rate and reduced training time by 50%. • Climate modeling tasks achieved 90% improved accuracy and 20% faster simulation times. The results validate the effectiveness of quantum models in processing large, complex datasets more efficiently. Conclusion: Quantum-enhanced AI presents a transformative approach to predictive analytics by addressing the core limitations of classical ML systems. This study demonstrates the practical feasibility and industrial relevance of integrating quantum computing into AI workflows. It opens avenues for further research in developing scalable, domain-specific quantum algorithms and hybrid AI models for real-world applications.
The contemporary manufacturing landscape is more complicated, data-heavy and more automated than ever before, producing enormous volumes of multi-modal sensor data, production logs and maintenance records. Predictive analytics within these environments becomes essential to achieve operational efficiency, reduce downtime, and optimise resource allocation; however, many classical machine learning methods struggle with high-dimensional, nonlinear, and dynamic data, resulting in suboptimal predictions and slow decision-making. To overcome these issues, the proposed research suggests a Quantum-Enhanced AI (QAI) system combining various quantum algorithms: Quantum Neural Networks (Quantum Neural Network) to recognize patterns, Quantum Supporter Vector Machines (Quantum Supporter Vector Machines) to classify, Quantum Approximate Optimization Algorithm (Quantum Approximate Optimization Algorithm) to schedule production and allocate resources, and Variational Quantum Eigensolver (Variational Quantum Eigensolver) to optimize parameters under noisy settings or conditions into a hybrid quantum-class The framework is a quantum computer based framework that encodes classical manufacturing data in the form of quantum states, using amplitude and qubit-efficient embeddings, allowing representation of high-dimensional features with the parallelism and entanglement of quantum computing. Realworld experimental validation of both turbofan engine degradation and semiconductor manufacturing sensor logs was conducted using IBM Qiskit and PennyLane to ensure reproducibility, with noise-aware training and error mitigation policies applied in simulations on NISQ-era hardware. The proposed QAI framework demonstrated higher predictive accuracy (12 to 15) than state-of-the-art hybrid quantum-classical and classical models, with superior accuracy, memorability, and F1 scores, and showed exceptional capabilities in anomaly detection, failure prediction, and optimisation. The results validate that quantum algorithms can be integrated into a predictive analytics pipeline to overcome classical computation bottlenecks, enhance robustness, and provide actionable insights for making real-time manufacturing decisions. Overall, this paper has demonstrated that quantum-assisted AI can be beneficial in intelligent manufacturing environments, laying the groundwork for expanding practical applications that bridge the gap between the promises of quantum computational power and predictive analytics in the industry.