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 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.
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.
Abstract: Quantum Natural Language Processing (QNLP) represents a pioneering approach to understanding and analyzing natural language by leveraging the principles of quantum computing. This project aims to explore the semantic analysis of autogenerated sentences using QNLP techniques. Traditional Natural Language Processing (NLP) methods have achieved significant milestones in language understanding and generation; however, they often struggle with the intricacies of context, ambiguity, and the vast computational resources required for complex tasks. QNLP offers a novel paradigm by utilizing the superposition and entanglement properties of quantum states, potentially revolutionizing how semantic meaning is extracted and processed. The primary objective of this project is to develop a QNLP framework capable of performing semantic analysis on autogenerated sentences. We will investigate the application of quantum algorithms and quantum machine learning models to decode the semantic structure and context of sentences generated by language models. This involves encoding textual data into quantum states, applying quantum gates and circuits to manipulate these states, and measuring the outcomes to interpret semantic information. By integrating quantum computing with NLP, this project seeks to achieve higher accuracy and efficiency in understanding complex language patterns. The anticipated outcomes include a comprehensive evaluation of QNLP's performance in semantic analysis, a comparison with classical NLP methods, and potential advancements in language processing tasks such as sentiment analysis, language translation, and text summarization. This project will contribute to the growing body of research at the intersection of quantum computing and artificial intelligence, paving the way for innovative solutions in the field of natural language processing.
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.
With the rise of Quantum Natural Language Processing (QNLP) for complex language tasks, ensuring data privacy and security has become a critical challenge. Traditional NLP relies on vast sensitive textual data, posing risks such as data leakage and compliance issues. While quantum computing enhances efficiency and expressiveness, it also introduces new privacy concerns. This paper explores the intersection of QNLP and privacy-preserving techniques, examining vulnerabilities, adapting classical privacy methods to quantum settings, and investigating emerging quantum-based solutions. By addressing key challenges, we aim to bridge the gap between quantum advancements and secure NLP, fostering robust and privacy-compliant QNLP models.
By using the quantum mechanics phenomenon, quantum computers provide a new dimension of computational power that drastically accelerates solving complex and resource-intensive problems. One of the most evolving but, due to the computation power of standard computers, reasonably limited application domain is natural language processing (NLP). NLP seeks to give interactive systems the ability to understand and manipulate human language. Making systems understand and manipulate human languages requires large amounts of data and computational power during learning as well as during the execution of NLP. For handling these amounts of data like text and audio recordings and the complexity of classical NLP algorithms, quantum computation has emerged as a promising solution. This work gives an extensive overview of this new field, known as quantum natural language processing (QNLP). Introducing the basics of quantum computing, we discuss its use in NLP by explaining the different proposed embedding models, quantum algorithms, and other methods of QNLP. As QNLP is still in its infancy, this comprehensive overview is the foundation that points to the upcoming research direction.
Quantum Natural Language Processing (QNLP) is an emerging interdisciplinary field that connects quantum computing and natural language processing (NLP), with the goal of improving language models through quantum algorithms. Although the field has gained increasing attention, research in QNLP remains fragmented, with limited studies mapping its evolution or practical implementations. This article presents a comprehensive scientometric analysis of QNLP research, revealing its conceptual foundations, research dynamics, and emerging directions. Using bibliographic data from the Scopus (2014-2024) and Web of Science (2014-2024) databases and analytical tools such as VOSviewer and Biblioshiny, we examined 116 publications following the PRISMA guidelines. The analysis covers publication growth, author productivity, leading journals, influential countries, and keyword trends. Network visualizations highlight the intellectual structure of the field, co-authorship patterns, and thematic clusters. The results indicate a sharp increase in QNLP studies since 2021, driven by advances in quantum hardware, frameworks such aslambeq, and growing interdisciplinary collaborations. However, during the literature mapping, we identified a significant gap regarding the applications of QNLP in various specific domains such as healthcare field. This study offers a quantitative mapping of the QNLP landscape, providing a structured overview of its development and outlining promising avenues for future exploration.