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Journal articles on the topic 'Quantum AI'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Lutz, Rudi. "Quantum AI." Behavioral and Brain Sciences 13, no. 4 (1990): 672–73. http://dx.doi.org/10.1017/s0140525x00080870.

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Ying, Mingsheng. "Quantum computation, quantum theory and AI." Artificial Intelligence 174, no. 2 (2010): 162–76. http://dx.doi.org/10.1016/j.artint.2009.11.009.

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Researcher. "AI and Quantum Computing: The Future of Data Analytics at Scale." International Journal of Computer Science and Information Technology Research (IJCSITR) 6, no. 2 (2025): 35–53. https://doi.org/10.5281/zenodo.15068399.

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<em>The rapid growth of data driven applications has revealed the computational and scalability limitations of traditional computer systems in the delivery of AI and ML solutions. However, with artificial intelligence enhancing various sectors such as banking, healthcare and logistics, the need for improved and more efficient computing has led to the exploration of quantum computing as a possible solution. Quantum Computing (QC), that uses concepts such as superposition and entanglement of quantum bits or qubits is expected to improve AI based data analytics by reducing the time for training m

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Ganapathy, Venkatasubramanian. "Comprehensive Review Of Adversarial Quantum Attacks On AI." Edumania-An International Multidisciplinary Journal 3, no. 2 (2025): 3–32. https://doi.org/10.59231/edumania/9114.

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With the rapid advancement of artificial intelligence (AI) and quantum computing, cybersecurity threats have evolved, giving rise to adversarial quantum attacks. These attacks exploit the vulnerabilities of AI models using quantum algorithms, posing a significant risk to data security, model robustness, and decision-making systems. This paper presents a comprehensive review of adversarial quantum attacks on AI, analyzing their mechanisms, potential impacts, and countermeasures. It explores how quantum computing can enhance adversarial attacks by accelerating the generation of adversarial examp

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Pushkar, Mehendale. "Quantum Machine Learning: The Next Frontier in AI." Journal of Scientific and Engineering Research 10, no. 1 (2023): 104–8. https://doi.org/10.5281/zenodo.13753380.

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Quantum Machine Learning (QML) stands at the intersection of two groundbreaking fields: quantum computing and artificial intelligence. This paper explores the potential of QML to revolutionize AI by leveraging the unique capabilities of quantum mechanics. It delves into the principles of quantum computing, the integration of quantum algorithms with machine learning, and the emerging applications that highlight the transformative power of QML. The paper also discusses the challenges and ethical considerations associated with this nascent field, aiming to provide a comprehensive overview of QML

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Tejesh, Reddy Singasani. "Exploring the Role of Quantum Computing in Accelerating AI Algorithms." Journal of Scientific and Engineering Research 10, no. 2 (2023): 243–46. https://doi.org/10.5281/zenodo.14169187.

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Quantum Computing (QC) and Artificial Intelligence (AI) are two of the fastest growing areas in 2020. It is no surprise then, with the promise of quantum computing to do different types of computation faster than we ever could have imagined possible, that many are looking at how this might help AI algorithms improve. In this article, we explain the potential impact of quantum computing in fastening AI algorithms and current progress or limitation as well.

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Silva, Carla. "What is Quantum AI?" ITNOW 59, no. 4 (2017): 21. http://dx.doi.org/10.1093/itnow/bwx119.

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Shen, Kexuan. "Research on the Application of Quantum Computing in Artificial Intelligence." Journal of Computing and Electronic Information Management 13, no. 1 (2024): 38–42. http://dx.doi.org/10.54097/4cg2am4s.

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This paper aims to explore the application of quantum computing in the field of artificial intelligence (AI). The continuous development of AI technology has brought revolutionary changes to various industries, but traditional computers may encounter bottlenecks when facing complex problems. Quantum computing, as an emerging computing paradigm, has the potential to address complex issues. This paper first introduces the fundamentals of AI and quantum computing, then discusses the application cases of quantum computing in AI, including quantum neural networks, quantum optimization algorithms, a

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Lohia, Aarav. "Quantum Artificial Intelligence: Enhancing Machine Learning with Quantum Computing." Journal of Quantum Science and Technology 1, no. 2 (2024): 6–11. http://dx.doi.org/10.36676/jqst.v1.i2.9.

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Quantum computing has emerged as a transformative technology with the potential to revolutionize artificial intelligence (AI) and machine learning (ML). This paper explores the intersection of quantum computing and AI, focusing on how quantum principles can enhance computational capabilities and address challenges in traditional machine learning approaches. Key aspects discussed include quantum algorithms such as quantum support vector machines, quantum neural networks, and quantum variational algorithms, which leverage quantum superposition and entanglement to process vast amounts of data mor

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Dhruvitkumar V Talati. "Quantum minds: Merging quantum computing with next-gen AI." World Journal of Advanced Research and Reviews 19, no. 3 (2023): 1692–99. https://doi.org/10.30574/wjarr.2023.19.3.1819.

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Quantum-enhanced machine learning (QML) is transforming artificial intelligence through the application of quantum computing concepts to solving computationally challenging problems more effectively than conventional methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the capability to speed up deep learning model training, solve combinatorial optimization problems, and improve feature selection in high-dimensional space. It covers basic quantum computer concepts employed within AI, for example, quantum circuits, quantum variational algorithms, and kernel quant

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Talati, Dhruvitkumar V. "Quantum AI and the Future of Super intelligent Computing." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 8, no. 1 (2025): 44–51. https://doi.org/10.60087/jaigs.v8i1.329.

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The convergence of quantum computing and artificial intelligence (AI) is ushering in a transformative era of technological advancement. This comprehensive volume explores the historical evolution of quantum computing and AI, the mathematical principles underpinning quantum mechanics, advanced quantum algorithms, real-world applications, ethical concerns, and futuristic implications of AI-driven quantum systems. With breakthroughs in machine learning, cryptography, automation, and problem-solving, Quantum AI is poised to redefine computation, decision-making, and intelligence. This work serves

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Hullurappa, Muniraju. "Uniting Quantum Computing and Artificial Intelligence: Exploring New Frontiers." FMDB Transactions on Sustainable Computer Letters 2, no. 2 (2024): 120–30. http://dx.doi.org/10.69888/ftscl.2024.000186.

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Two powerful technologies, quantum computing and artificial intelligence (AI), can potentially disrupt sectors and solve some of society’s greatest problems in practically every industry. A study on how quantum computing and AI can work together. We seek to provide a comprehensive literature review encompassing key contributions and problems from both domains. The AI mission’s unique quantum computing strategy and how quantum algorithms can be used to slave for machine learning models and high-speed, sophisticated, accepted handling are explained. Quantum Computing has been shown to improve AI

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Bhatye, Atharva. "Quantum Computing and Its Applications in Artificial Intelligence: A Comprehensive Review." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51022.

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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 pee

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Temitope Oluwatosin Fatunmbi. "Quantum computing and Artificial Intelligence: Toward a new computational paradigm." World Journal of Advanced Research and Reviews 27, no. 1 (2025): 687–95. https://doi.org/10.30574/wjarr.2025.27.1.2498.

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This paper explores the convergence of quantum computing and artificial intelligence (AI), examining how their integration may redefine computational paradigms. Quantum computing, with its unique properties of superposition and entanglement, has the potential to exponentially accelerate AI processes, particularly in optimization, machine learning, and data analysis. We investigate quantum algorithms, such as the quantum Fourier transform and Grover’s algorithm, highlighting their application to AI models and machine learning tasks that require vast computational resources. The paper further de

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Pasupuleti, Murali Krishna. "Supersymmetric Quantum Neural Networks: Bridging Superalgebras and AI Architectures." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 48–59. https://doi.org/10.62311/nesx/rp0425.

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Abstract: This paper proposes a novel paradigm for quantum artificial intelligence: the design and implementation of Supersymmetric Quantum Neural Networks (S-QNNs) that explicitly integrate superalgebraic structures into quantum circuit-based AI architectures. By embedding the symmetry principles of supersymmetry—captured by quantum superalgebras such as osp(1∣2) and sl(1∣1)—into the computational fabric of quantum neural networks, we aim to create models that exhibit both structural elegance and computational efficiency. The resulting framework allows for interpretable, energy-efficient, and

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Candeloro, Alessandro, Matteo G. A. Paris, and Marco G. Genoni. "On the properties of the asymptotic incompatibility measure in multiparameter quantum estimation." Journal of Physics A: Mathematical and Theoretical 54, no. 48 (2021): 485301. http://dx.doi.org/10.1088/1751-8121/ac331e.

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Abstract We address the use of asymptotic incompatibility (AI) to assess the quantumness of a multiparameter quantum statistical model. AI is a recently introduced measure which quantifies the difference between the Holevo and the symmetric logarithmic derivative (SLD) scalar bounds, and can be evaluated using only the SLD operators of the model. At first, we evaluate analytically the AI of the most general quantum statistical models involving two-level (qubit) and single-mode Gaussian continuous-variable quantum systems, and prove that AI is a simple monotonous function of the state purity. T

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Pasupuleti, Murali Krishna. "Hybrid Intelligence: Leveraging Superalgebraic Quantum States for Neural Network Acceleration." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 36–47. https://doi.org/10.62311/nesx/rp0325.

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Abstract: As artificial intelligence (AI) systems grow in complexity, the demand for efficient and scalable computational architectures has become increasingly urgent. This research introduces a hybrid intelligence paradigm that harnesses quantum superalgebraic states as a new computational substrate for accelerating neural network inference and training. By encoding activation patterns and weight transformations into superalgebraic quantum states, we establish a model that combines symbolic expressiveness with quantum parallelism. This framework explores the interaction between quantum algebr

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Kaul, Deepak. "Dynamic AI-Based Intrusion Detection for Quantum Computing Networks." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 9, no. 10 (2023): 1068–73. https://doi.org/10.5281/zenodo.14708936.

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As quantum computing continues to advance, traditional intrusion detection systems (IDS) may prove inadequate in protecting against quantum-level cybersecurity threats. This paper proposes a novel AI- based intrusion detection system (AI-IDS) specifically designed for quantum computing networks (QCN). By leveraging machine learning (ML) algorithms and quantum data patterns, we develop an adaptive and dynamic framework capable of detecting irregularities unique to quantum communication protocols. Our model integrates classical and quantum features to provide comprehensive protection against bot

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How, Meng-Leong, and Sin-Mei Cheah. "Forging the Future: Strategic Approaches to Quantum AI Integration for Industry Transformation." AI 5, no. 1 (2024): 290–323. http://dx.doi.org/10.3390/ai5010015.

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The fusion of quantum computing and artificial intelligence (AI) heralds a transformative era for Industry 4.0, offering unprecedented capabilities and challenges. This paper delves into the intricacies of quantum AI, its potential impact on Industry 4.0, and the necessary change management and innovation strategies for seamless integration. Drawing from theoretical insights and real-world case studies, we explore the current landscape of quantum AI, its foreseeable influence, and the implications for organizational strategy. We further expound on traditional change management tactics, emphasi

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XU Jiaxin, XU Lechen, LIU Jingyang, DING Huajian, and WANG Qin. "Research Progress on Artificial Intelligence Empowered Quantum Communication and Quantum Sensing Systems." Acta Physica Sinica 74, no. 12 (2025): 0. https://doi.org/10.7498/aps.74.20250322.

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Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. They have attracted significant attention in recent research. However, they both face numerous challenges on the path to practical application. For instance, device imperfections may lead to security vulnerability, and environmental noise may significantly reduce measurement accuracy. Traditional solutions often involve high computational complexity, long processing times, and substa

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Neamah Abbas, Farah, Mohanad Ridha Ghanim, and Rafal Naser Saleh. "Subject Review:AI-Driven Security in Quantum Machine Learning:Vulnerabilities,Threats, and Defenses." International Journal of Engineering Research and Advanced Technology 11, no. 04 (2025): 01–22. https://doi.org/10.31695/ijerat.2025.4.1.

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Quantum Machine Learning (QML) has advanced significantly thanks to the combination of Quantum Computing (QC) with Artificial Intelligence (AI), hence releasing computational benefits over conventional methods. This synergy does, however, also bring fresh security flaws like adversarial attacks, quantum noise manipulation, and cryptographic weaknesses. This work offers a thorough investigation of QML security along with an examination of its special vulnerabilities resulting from hardware-induced faults, quantum variational circuits, and quantum data encoding. We methodically investigate adver

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Panyaram, Sudheer. "Utilizing Quantum Computing to Enhance Artificial Intelligence in Healthcare for Predictive Analytics and Personalized Medicine." FMDB Transactions on Sustainable Computing Systems 2, no. 1 (2024): 22–31. http://dx.doi.org/10.69888/ftscs.2024.000194.

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Advancements in quantum computing hold the potential to revolutionize artificial intelligence (AI), particularly in the field of healthcare. This paper explores how quantum computing can be leveraged to improve predictive analytics and facilitate personalized medicine. Through enhanced computational capacity, quantum computing enables faster processing and analysis of large, complex datasets, essential for predictive models in healthcare. This integration can lead to more precise diagnostics, treatment options, and disease prevention strategies by refining AI’s capability to handle vast data.

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Pasupuleti, Murali Krishna. "Quantum Superalgebraic Entanglement Classification with Generative AI." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 60–75. https://doi.org/10.62311/nesx/rp0525.

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Abstract: This paper presents a novel framework for classifying and synthesizing entangled quantum states using the mathematical structure of quantum superalgebras integrated with generative artificial intelligence. While traditional entanglement classification methods struggle with scalability and symmetry-awareness in systems governed by supersymmetric dynamics, this work introduces a hybrid model that leverages Lie superalgebras—such as sl(2∣1) and osp(1∣2)—to construct algebraically constrained entangled state spaces. A generative AI model, specifically a conditional diffusion model, is tr

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Idoko, Peter Idoko, Matthew Ijiga Onuh, Anebi Enyejo Lawrence, Ileanaju Ugbane Solomon, Akoh Omachile, and Olumubo Odeyemi Michael. "Exploring the potential of Elon musk's proposed quantum AI: A comprehensive analysis and implications." Global Journal of Engineering and Technology Advances 18, no. 3 (2024): 048–65. https://doi.org/10.5281/zenodo.10951824.

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Elon Musk has recently introduced the concept of Quantum AI, suggesting a revolutionary integration of quantum computing capabilities with artificial intelligence. This research aims to delve into the theoretical foundations, technological aspects, and potential applications of Musk's proposed Quantum AI. By conducting an in-depth analysis, this study seeks to unravel the unique features and challenges associated with the fusion of quantum computing and artificial intelligence, offering insights into the transformative impact on computational power, machine learning, and problem-solving capabi

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Kartheek Sankranthi. "Quantum Computing's Future Role in AI-Driven Drug Discovery." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1450–58. https://doi.org/10.30574/wjarr.2025.26.2.1692.

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Quantum computing is poised to revolutionize pharmaceutical research through its integration with artificial intelligence for drug discovery applications. This article examines how quantum computational approaches address fundamental limitations in traditional drug development pipelines, particularly in molecular modeling and simulation, where classical computing faces exponential scaling challenges. By leveraging quantum mechanical phenomena like superposition and entanglement, quantum algorithms such as the Variational Quantum Eigen solver and the Quantum Approximate Optimization Algorithm o

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Hatay, Emir Sahin, Muhammed Golec, and Sukhpal Singh Gill. "Transforming Modern Computing With Quantum and AI." International Journal of Information Technology Project Management 16, no. 1 (2025): 1–16. https://doi.org/10.4018/ijitpm.379718.

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Modern computing systems are undergoing rapid transformation through theoretical advances and technical innovations. This article explores the technological and application trends in quantum-driven artificial intelligence (AI) innovations. The authors explore the mathematical frameworks underlying AI and quantum computing systems, with a particular focus on the role of algebraic topology in quantum circuit optimization and error correction. From neural networks to transformers, they investigate how AI architectures are reshaping computational capabilities, such as in healthcare, autonomous sys

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Omoniyi David Olufemi. "Quantum-AI Federated Clouds: A trust-aware framework for cross-domain observability and security." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 4098–140. https://doi.org/10.30574/wjarr.2025.26.2.2074.

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The convergence of quantum computing, artificial intelligence (AI), and federated cloud architecture offers transformative potential for secure, scalable, and privacy-preserving data processing. Yet, trust management and cross-domain observability remain major challenges, particularly in decentralized, heterogeneous cloud environments. This paper introduces Quantum-AI Federated Clouds (QAIFC) a novel trust-aware framework that combines quantum-safe encryption, federated machine learning, and explainable AI to enable secure and observable operations across cloud domains. We present QFedSecure,

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Ganguly, Kaushik. "Quantum AI: Deep Learning optimization using Hybrid Quantum Filters." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 1720–33. http://dx.doi.org/10.22214/ijraset.2022.46914.

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Abstract: Deep learning algorithms have shown promising results for different image processing tasks, particularly in remote sensing & image recognition. Till now many studies have been carried out on image processing, which brings a new paradigm of innovative capabilities under the umbrella of intelligent remote sensing and computer vision. Accordingly, quantum processing algorithms have proved to efficiently solve some issues that are undetectable to classical algorithms and processors. Keeping that in mind, a Quantum Convolutional Neural Network (QCNN) architecture along with Hybrid Qua

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Temidayo Olorunsogo, Boma Sonimiteim Jacks, and Olakunle Abayomi Ajala. "LEVERAGING QUANTUM COMPUTING FOR INCLUSIVE AND RESPONSIBLE AI DEVELOPMENT: A CONCEPTUAL AND REVIEW FRAMEWORK." Computer Science & IT Research Journal 5, no. 3 (2024): 671–80. http://dx.doi.org/10.51594/csitrj.v5i3.927.

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This paper proposes a novel conceptual framework that integrates the advanced capabilities of quantum computing to address the urgent need for responsible and inclusive Artificial Intelligence (AI) development. It reviews current challenges in AI, such as bias, lack of inclusivity, and the computational limitations faced by classical computing methods in solving complex societal problems. By harnessing quantum computing, this framework aims to overcome these barriers, enabling faster, more efficient AI solutions that are ethically grounded and universally accessible. By adopting a holistic app

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Ekowati, Maria Atik Sunarti, and Zefanya Permata Nindyatama. "Utilization of Artificial Intelligence in Cyber Security System Prototype to Face Quantum Computing." Journal of Scientific Insights 2, no. 2 (2025): 61–74. https://doi.org/10.69930/jsi.v2i2.338.

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The advent of quantum computing poses significant threats to traditional cybersecurity systems, which rely on cryptographic algorithms vulnerable to quantum attacks. This research explores the utilization of artificial intelligence (AI) in designing a prototype cybersecurity system aimed at countering the emerging threats of quantum computing. The study integrates AI-based intrusion detection systems with quantum-resistant cryptographic techniques to enhance the security of digital systems against quantum-driven cyberattacks. A combination of machine learning algorithms, such as neural network

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Li, Tian, Xiao-Yue Xu, Chen Ding, et al. "AI-Powered Algorithm-Centric Quantum Processor Topology Design." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 442–50. https://doi.org/10.1609/aaai.v39i1.32023.

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Quantum computing promises to revolutionize various fields, yet the execution of quantum programs necessitates an effective compilation process. This involves strategically mapping quantum circuits onto the physical qubits of a quantum processor. The qubits' arrangement, or topology, is pivotal to the circuit's performance, a factor that often defies traditional heuristic or manual optimization methods due to its complexity. In this study, we introduce a novel approach leveraging reinforcement learning to dynamically tailor qubit topologies to the unique specifications of individual quantum ci

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Rajarshi Tarafdar. "Quantum AI: The future of machine learning and optimization." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 2744–51. https://doi.org/10.30574/wjarr.2025.25.2.0639.

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Quantum Artificial Intelligence (Quantum AI) represents a rapidly developing interdisciplinary field at the intersection of quantum computing and machine learning (ML). It holds the promise of unlocking unprecedented computational capabilities for complex optimization tasks, large-scale data processing, and advanced pattern recognition. In this research, we provide a comprehensive examination of two principal quantum algorithms—the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE)—applied to classical ML challenges. Using a hybrid simulation framew

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Bibhu, Dash, and Ullah Sameeh. "Quantum-safe: Cybersecurity in the age of Quantum-Powered AI." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 1555–63. https://doi.org/10.5281/zenodo.13323635.

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There is a lot of talk about how the cyber world will change and perceive itself as quantum computing takes center stage in the computing industry in a decade. While some are enthusiastic about the advancement of quantum technology, others remain skeptical about its application in cyberspace. But there is no doubt the interaction of artificial intelligence (AI) and quantum computing offers cybersecurity both tremendous benefits and difficulties as we navigate the digital terrain. This article explores the complex interrelationship between these two state-of-the-art technologies and how it affe

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Mullangi, Kishore, Niravkumar Dhameliya, Sunil Kumar Reddy Anumandla, et al. "AI-Augmented Decision-Making in Management Using Quantum Networks." Asian Business Review 13, no. 2 (2023): 73–86. http://dx.doi.org/10.18034/abr.v13i2.718.

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Combining artificial intelligence (AI) and quantum networks can revolutionize management decision-making. This study delves into the implications of AI-augmented decision-making using quantum networks, focusing on its primary objectives, methodology, significant findings, and policy implications. By thoroughly examining the latest research, analyzing case studies, and exploring future possibilities, this study investigates the potential of combining AI and quantum computing to improve strategic decision-making, streamline operations, and foster innovation in management. The methodology entails

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Baioletti, Marco, Fabrizio Fagiolo, Corrado Loglisci, et al. "Quantum Artificial Intelligence: Some Strategies and Perspectives." AI 6, no. 8 (2025): 175. https://doi.org/10.3390/ai6080175.

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In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes so

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Gustafson, Erik. "The next media epoch: AI, quantum computation and the future form(s) of media." Explorations in Media Ecology 24, no. 1 (2025): 75–82. https://doi.org/10.1386/eme_00241_7.

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At present, artificial intelligence (AI) has been promoted as the next media epoch on the horizon. The probe to follow explored whether AI or the lesser-known development, quantum computation, will be the defining communication medium of the next era. Upon investigation of the structural characteristics of AI and quantum computation, the probe argued that while AI may be the ‘next big thing’ in communication technology, AI represents a development in pre-existing software not a fundamentally new form of media. Instead, it was argued that quantum computation represents a fundamentally new form

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Taherdoost, Hamed, and Mitra Madanchian. "AI Advancements: Comparison of Innovative Techniques." AI 5, no. 1 (2023): 38–54. http://dx.doi.org/10.3390/ai5010003.

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In recent years, artificial intelligence (AI) has seen remarkable advancements, stretching the limits of what is possible and opening up new frontiers. This comparative review investigates the evolving landscape of AI advancements, providing a thorough exploration of innovative techniques that have shaped the field. Beginning with the fundamentals of AI, including traditional machine learning and the transition to data-driven approaches, the narrative progresses through core AI techniques such as reinforcement learning, generative adversarial networks, transfer learning, and neuroevolution. Th

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Pasupuleti, Murali Krishna. "Quantum Supersymmetry Meets AI: An Algebraic Framework for Generalized Intelligence." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 118–32. https://doi.org/10.62311/nesx/rp0925.

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This paper presents a novel interdisciplinary framework that integrates quantum supersymmetry with artificial intelligence to develop an algebraically structured foundation for generalized intelligence. Drawing from the mathematical rigor of supersymmetric quantum mechanics and Lie superalgebras such as osp(1∣2) and sl(2∣1), we propose AI architectures where learning dynamics are governed by graded algebraic transformations and symmetry-preserving constraints. By modeling intelligent agents as evolving quantum systems under the action of supercharges, the framework introduces dual representati

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Shashank Chaudhary. "Quantum Machine Learning: Bridging Quantum Computing and AI for Exponential Gains." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 126–35. https://doi.org/10.32628/cseit251112393.

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Quantum Machine Learning (QML) represents a convergent frontier where quantum computing meets artificial intelligence, offering transformative possibilities for computational challenges. This article explores the fundamental concepts, current applications, and future prospects of QML, examining how it addresses classical computational bottlenecks through quantum mechanical principles like superposition and entanglement. It analyzes core quantum computing architectures including Quantum Neural Networks, Variational Quantum Circuits, and Quantum Kernel Methods, highlighting their potential advan

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Akoh Atadoga, Chinedu Ugochukwu Ike, Onyeka Franca Asuzu, Benjamin Samson Ayinla, Ndubuisi Leonard Ndubuisi, and Rhoda Adura Adeleye. "THE INTERSECTION OF AI AND QUANTUM COMPUTING IN FINANCIAL MARKETS: A CRITICAL REVIEW." Computer Science & IT Research Journal 5, no. 2 (2024): 461–72. http://dx.doi.org/10.51594/csitrj.v5i2.816.

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This review explores the intricate and evolving relationship between Artificial Intelligence (AI) and Quantum Computing within the realm of financial markets. As technology continues to advance, the integration of AI and quantum computing has emerged as a paradigm-shifting force, promising unprecedented capabilities to analyze and navigate the complexities of financial systems. This critical review delves into the synergies, challenges, and potential disruptions arising from the intersection of these two transformative technologies. The utilization of AI in financial markets has witnessed rema

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Ouellette, Jennifer. "AI learns to crack quantum mechanics." New Scientist 233, no. 3113 (2017): 12. http://dx.doi.org/10.1016/s0262-4079(17)30303-2.

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Surya Kiran, Arjun Kumar, and Swathi Chukkala. "Decentralized AI at the Edge: Federated Learning, Quantum Optimization and IoT Scalability." International Journal of Science and Research Archive 14, no. 3 (2025): 256–63. https://doi.org/10.30574/ijsra.2025.14.3.0633.

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Decentralized artificial intelligence (AI) at the edge marks a revolutionary evolution in computing, enabling efficient, privacy-preserving, and scalable solutions tailored for the Internet of Things (IoT). This paper integrates cutting-edge advancements in federated learning (FL), quantum optimization, and scalable IoT architectures to propose a cohesive framework for next-generation edge AI systems. We conducted an extensive literature review covering privacy-focused decentralized AI, quantum-enhanced optimization methods, and IoT system scalability. Our research highlights significant enhan

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Gaurav, Kashyap. "Quantum Machine Learning: Exploring the Intersection of Quantum Computing and AI." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 13, no. 1 (2025): 1–7. https://doi.org/10.5281/zenodo.14615549.

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At the nexus of artificial intelligence (AI) and quantum computing lies the emerging field of quantum machine learning (QML). By speeding up the computation of intricate algorithms, quantum computers have the potential to transform a number of fields, including machine learning, by outperforming classical computers by an exponential amount in specific tasks. This essay examines the fundamental ideas of quantum computing, how it applies to machine learning, and the potential advantages and difficulties of QML. We examine several quantum algorithms, including quantum versions of support vector m

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Mahender Singh. "Self-Healing Cloud Infrastructures via Al-Driven Quantum Optimization." Cuestiones de Fisioterapia 53, no. 03 (2024): 2387–406. https://doi.org/10.48047/nrh7me39.

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The integration of Artificial Intelligence (AI) with quantum optimization techniques, particularly quantum annealing, offers a promising pathway to enhance the resilience and efficiency of cloud infrastructures. This paper explores the application of quantum annealing to optimize AI algorithms for real-time fault detection and automated recovery in multi-cloud systems.

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Hassan Ali. "Quantum computing and AI in healthcare: Accelerating complex biological simulations, genomic data processing, and drug discovery innovations." World Journal of Advanced Research and Reviews 20, no. 2 (2023): 1466–84. https://doi.org/10.30574/wjarr.2023.20.2.2325.

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The convergence of quantum computing and artificial intelligence (AI) presents a paradigm shift in healthcare, revolutionizing complex biological simulations, genomic data processing, and drug discovery innovations. Traditional computational methods, despite their advancements, often struggle with the sheer scale and complexity of biological data, limiting the speed and accuracy of medical breakthroughs. Quantum computing, with its ability to process vast datasets exponentially faster than classical computers, coupled with AI's predictive capabilities, offers a transformative solution for acce

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Vandanapu, Manoj Kumar, Asmath Shaik, Satish Kumar Nagamalla, and Radharani Balbhadruni. "Quantum-Inspired AI for Optimized High-Frequency Trading." International Journal of Finance 9, no. 7 (2024): 1–17. http://dx.doi.org/10.47941/ijf.2301.

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This article explores the transformative role of quantum-inspired AI in optimizing financial practices, particularly within high-frequency trading (HFT) in the financial sector. As HFT operates in an environment of rapid transactions and significant market volatility, the need for advanced optimization techniques becomes paramount. Quantum-inspired algorithms leverage principles from quantum mechanics, such as superposition and tunneling, to enhance various aspects of trading strategies. These algorithms enable rapid optimization of asset allocation, real-time trade execution, and proactive fr

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Bibhu Dash and Sameeh Ullah. "Quantum-safe: Cybersecurity in the age of Quantum-Powered AI." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 1555–63. http://dx.doi.org/10.30574/wjarr.2024.21.1.2640.

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There is a lot of talk about how the cyber world will change and perceive itself as quantum computing takes center stage in the computing industry in a decade. While some are enthusiastic about the advancement of quantum technology, others remain skeptical about its application in cyberspace. But there is no doubt the interaction of artificial intelligence (AI) and quantum computing offers cybersecurity both tremendous benefits and difficulties as we navigate the digital terrain. This article explores the complex interrelationship between these two state-of-the-art technologies and how it affe

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Ali Akbar, Muhammad Ejaz Bashir*, and Muzamil Hussain ALHussaini. "IOT Security in Complex Systems: Big Data, Quantum Computing and HCI Design for AI Ethics." Annual Methodological Archive Research Review 3, no. 1 (2025): 30–39. https://doi.org/10.63075/1meshm93.

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Advanced security frameworks are required to protect networked devices due to the extraordinary data production caused by the IOT's rapid expansion in complex systems. In light of AI ethics, this research investigates the relationship between IOT security, Big Data analytics, Quantum Computing, and Human-Computer Interaction (HCI) design. IOT networks need real-time anomaly detection driven by Big Data algorithms and quantum-resistant cryptography solutions as they grow more susceptible to sophisticated cyber threats. Furthermore, by addressing concerns of transparency, bias reduction, and use

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Rah, Ali. "Artificial Intelligence Expansion, A Transformation or a Mutation." International Journal of Computational Science, Information Technology and Control Engineering 11, no. 3 (2024): 9–17. http://dx.doi.org/10.5121/ijcsitce.2024.11302.

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The field of Artificial Intelligence (AI) is evolving in a fast pace impacting a wide range of other field including general public use of social media platforms all the way to industrial activities and Cybersecurity. When AI is combined with Quantum Computing the abilities of AI are exponentially increased. This paper aims to explore the evolution of AI, discuss if this evolution a transformation or a mutation, and its correlation with Quantum Computing. It is also looking into the impact of AI on Cybersecurity risks and mitigation.

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Shah, Jyoti Kunal. "Ethical Considerations of LLM-Driven Quantum Code Generation for Optimization Tasks." American Journal of Engineering and Technology 05, no. 12 (2023): 52–59. https://doi.org/10.37547/tajet/volume05issue12-13.

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The convergence of large language models (LLMs) and quantum computing has the potential to revolutionize software development for quantum optimization tasks. AI-assisted code generation, powered by models like OpenAI Codex, can accelerate the design of quantum algorithms by automating routine coding tasks and democratizing access to quantum programming. However, this innovation introduces a web of ethical, legal, and technical challenges. This paper investigates the implications of using LLMs to generate quantum code, focusing on intellectual property (IP) concerns, the risk of unintended outc

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