Visual Question Answering (VQA) systems are artificial intelligence applications that perform multimodal inference by combining visual and linguistic information. This review surveys the current literature on integrating quantum computing, quantum machine learning (QML), and quantum natural language processing (QNLP) techniques in VQA systems. It also discusses the potential impact of quantum computing methods on VQA applications in the context of 6G networks. The paper will demonstrate how this integration can contribute to developing more robust, secure, and efficient VQA solutions.
Visual Question Answering (VQA) systems are artificial intelligence applications that perform multimodal inference by combining visual and linguistic information. This review surveys the current literature on integrating quantum computing, quantum machine learning (QML), and quantum natural language processing (QNLP) techniques in VQA systems. It also discusses the potential impact of quantum computing methods on VQA applications in the context of 6G networks. The paper will demonstrate how this integration can contribute to developing more robust, secure, and efficient VQA solutions.
Abstract This review aimed to explore the integration of Quantum Computing (QC) with Artificial Intelligence (AI) subsets such as Machine Learning (ML) and Deep Learning (DL), addressing the computational demands posed by the exponential growth of visual data. It identifies key challenges such as interdisciplinary complexity, lack of standard benchmarks, scalability, integration barriers, and the theoretical-practical gap in quantum applications. The review systematically examines existing literature on the application of quantum algorithms in areas including image processing, Natural Language Processing (NLP), Transfer Learning (TL), Federated Learning (FL), networking, cybersecurity and the finance sector. It highlights the usage of quantum principles like superposition and entanglement to accelerate computations, optimize models, and enhance data security in ML/DL frameworks. Findings indicate that integrating QC with ML/DL offers faster convergence, improved optimization, secure decentralized learning, and efficient handling of large-scale and complex data. Specific improvements are observed in TL and FL approaches, NLP accuracy, cryptographic robustness, and performance in medical diagnostics and autonomous systems. QC holds transformative potential in enhancing ML/DL capabilities across domains. Despite existing challenges such as error mitigation and integration complexity, its combination with classical learning methods opens new frontiers for research in AI-driven sectors. Future studies should focus on bridging theoretical and application-level gaps while creating standardized evaluation frameworks.
The amalgamation of quantum computing and artificial intelligence (AI) is poised to redefine the capabilities of future 6G networks. This paper presents a structured, system-level analysis of Quantum-enabled AI (QAI) for 6G, organized around a unifying technical framework that progresses from foundations to algorithms, integration, security and deployment. Specifically, we ask: How can quantum technologies be systematically integrated into AI-native 6G networks in a secure, scalable and practically deployable manner? To answer this, we first establish foundational concepts in quantum computing and hybrid quantum-classical architectures as the enabling substrate. Next, we classify quantum machine learning (QML) algorithms by learning paradigm, quantum subroutine, hardware model and ML pipeline stage, directly mapping each category to specific 6G network functions. Building on this algorithmic layer, we examine how QAI integrates into 6G AI pipelines across training, inference, sensing and federated learning. Beyond taxonomy, we provide a comparative tradeoff analysis of representative QML methods, highlighting their suitability, latency implications, hardware readiness and limitations across practical 6G communication scenarios. We then analyze the security implications of this integration, addressing new attack vectors, adversarial threats, hardware vulnerabilities and post-quantum cryptographic requirements that arise specifically at the quantum-classical interface. Finally, we identify the architectural, algorithmic and regulatory barriers that must be addressed before deployment and synthesize lessons learned and strategic recommendations for researchers, developers and policymakers. This layered, interdependent structure, from hardware substrate through algorithmic capability to secure deployment, provides a coherent roadmap for advancing trustworthy QAI ecosystems in 6G.
Quantum Image Representation (QIR) has become a key component of Quantum Image Processing (QIP), enabling the encoding and manipulation of visual data within quantum computing environments.Its integration with Quantum Artificial Intelligence (QAI) and Quantum Machine Learning (QML) has attracted significant research interest due to the potential advantages of quantum parallelism and compact data representation.This survey reviews major QIR techniques, analyzes their encoding strategies and computational characteristics, and examines their opportunities, challenges, and future relevance in quantum AI and machine learning applications.A systematic review of 70 peer-reviewed journal and conference publications published between 1992 and 2025 was conducted.The selected studies were evaluated based on encoding mechanisms, qubit requirements, circuit complexity, retrieval capability, scalability, and suitability for quantum image processing and learning tasks.The review covers prominent QIR models, including FRQI, NEQR, MCQI, QPIE, GQIR, QRMW, and tensor-network-based representations.Comparative analysis reveals important trade-offs between qubit efficiency, encoding complexity, retrieval accuracy, and scalability.Amplitude-based approaches provide compact image encoding but suffer from probabilistic retrieval, whereas basis-state representations offer deterministic recovery at the expense of increased circuit complexity.QIR demonstrates significant potential in image classification, feature extraction, image compression, and hybrid quantum-classical learning frameworks.QIR provides a promising foundation for next-generation quantum-enhanced visual intelligence systems.However, practical implementation remains constrained by state preparation overhead, noise, decoherence, limited qubit availability, and the lack of standardized evaluation frameworks.Future research should focus on scalable encoding methods, fault-tolerant quantum architectures, benchmarking standards, and deeper integration with advanced QML models to accelerate the development of practical quantum image analytics.
Quantum AI is the integration of quantum computing and artificial intelligence, an interaction with the expected impact to be quite positive for the two. For example, QNLP and QCV have the potential to provide radical enhancements to the present methodologies that are used to program machines to comprehend textual as well as image data by harnessing the concept of superposition, entanglement, and quantum parallelism. In this chapter, the application of Quantum AI in NLP and Computer Vision is discussed through quantum language models, quantum image representation, as well as a hybrid approach. However, the tools presented here, together with the theory, have shown the ability to possibly achieve speedup and better efficiency; however, the current NISQ hardware limits the implementation of the tools presented herein. This chapter also discusses difficulties, further study prospects, and prospects for Quantum AI in the domains of NLP and Computer Vision.
Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks
In recent developments, deep learning methodologies applied to Natural Language Processing (NLP) have revealed a paradox: They improve performance but demand considerable data and resources for their training. Alternatively, quantum computing exploits the principles of quantum mechanics to overcome the computational limitations of current methodologies, thereby establishing an emerging field known as quantum natural language processing (QNLP). This domain holds the potential to attain a quantum advantage in the processing of linguistic structures, surpassing classical models in both efficiency and accuracy. In this paper, it is proposed to categorise QNLP models based on quantum computing principles, architecture, and computational approaches. This paper attempts to provide a survey on how quantum meets language by mapping state-of-the-art in this area, embracing quantum encoding techniques for classical data, QNLP models for prevalent NLP tasks, and quantum optimisation techniques for hyper parameter tuning. The landscape of quantum computing approaches applied to various NLP tasks is summarised by showcasing the specific QNLP methods used, and the popularity of these methods is indicated by their count. From the findings, it is observed that QNLP approaches are still limited to small data sets, with only a few models explored extensively, and there is increasing interest in the application of quantum computing to natural language processing tasks.