Relevant bibliographies by topics / Quantum Machine Learning (QML)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Quantum Machine Learning (QML)'

Author: Grafiati
Published: 6 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "Quantum Machine Learning (QML)"

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Patel, Ananya (Ph D. Candidate). "ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING." International Journal of Intelligent Data and Machine Learning 2, no. 02 (2025): 1–7. https://doi.org/10.55640/ijidml-v02i02-01.

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The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application
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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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Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (2022): 5774. http://dx.doi.org/10.3390/rs14225774.

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A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for
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Karandashev, Konstantin, and O. Anatole von Lilienfeld. "An orbital-based representation for accurate quantum machine learning." Journal of Chemical Physics 156, no. 11 (2022): 114101. http://dx.doi.org/10.1063/5.0083301.

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We introduce an electronic structure based representation for quantum machine learning (QML) of electronic properties throughout chemical compound space. The representation is constructed using computationally inexpensive ab initio calculations and explicitly accounts for changes in the electronic structure. We demonstrate the accuracy and flexibility of resulting QML models when applied to property labels, such as total potential energy, HOMO and LUMO energies, ionization potential, and electron affinity, using as datasets for training and testing entries from the QM7b, QM7b-T, QM9, and LIBE
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Pramoda Medisetty. "Quantum Machine Learning: A Survey." Journal of Electrical Systems 20, no. 6s (2024): 971–81. http://dx.doi.org/10.52783/jes.2778.

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Quantum Machine Learning (QML) is an emergent discipline that integrates the principles of quantum computing with traditional machine learning techniques, aiming to enhance the capabilities of data analysis and decision-making processes. Leveraging the unique properties, QML promises to revolutionize machine learning by offering superior processing power and computational efficiency. The synergistic approach followed by each Quantum Machine Learning Algorithm allows for the management of large databases and the execution of complex computational tasks more efficiently than classical algorithms
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Chavan, Shradha, and Preeti Mulay. "Define, refined and re-defined concepts of quantum machine learning : A review." Journal of Information and Optimization Sciences 45, no. 5 (2024): 1229–62. http://dx.doi.org/10.47974/jios-1320.

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Quantum Machine Learning (QML) is a new phraseology in the world today. With various upcoming Artificial Intelligence (AI) technologies, everyday data scientists and corporate around the world are trying to harness its power. QML borrows concepts of Quantum Computing and QML algorithms enhance the existing Machine Learning (ML) algorithms by processing datasets more efficiently. Quantum Computers are a powering force driving QML algorithms that boosts computational power and helps analyse data. QML algorithms can be applied in sectors involving number crunching, pattern identification and so o
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Parveen Shaik, Dr Sajeeda. "Exploring the Landscape: A Systematic Review of Quantum Machine Learning and Its Diverse Applications." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 8, no. 1 (2020): 05–09. http://dx.doi.org/10.55083/irjeas.2020.v08i01003.

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Quantum Machine Learning (QML), a confluence of quantum computing and classical machine learning, represents a revolutionary paradigm with transformative potential. This systematic review explores the landscape of QML by investigating its underlying principles, methodologies, diverse applications, challenges, and ethical considerations. Beginning with an examination of fundamental quantum computing principles, the review navigates through various QML methodologies, comparing them with classical counterparts. Real-world applications, ranging from quantum-enhanced optimization to drug discovery,
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Olaitan, Ololade Funke, Samuel Oluwabukunmi Ayeni, Adedapo Olosunde, et al. "Quantum Computing in Artificial Intelligence: a Review of Quantum Machine Learning Algorithms." Path of Science 11, no. 5 (2025): 7001. https://doi.org/10.22178/pos.117-25.

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Two of the most disruptive technologies of the 21st century are quantum computing and artificial intelligence. Their intersection has led to the emergence of a new discipline referred to as Quantum Machine Learning (QML), which aims to enhance the capabilities of classical machine learning by leveraging the computational advantages of quantum devices. This paper provides a survey of the most advanced Quantum Machine Learning (QML) algorithms, including Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Netwo
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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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Akrom, Muhamad, Wise Herowati, and De Rosal Ignatius Moses Setiadi. "A Quantum Circuit Learning-based Investigation: A Case Study in Iris Benchmark Dataset Binary Classification." Journal of Computing Theories and Applications 2, no. 3 (2025): 355–67. https://doi.org/10.62411/jcta.11779.

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This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in perfect classification, with all models attaining accuracy, precision, recall, and an F1-score of 1.00
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Dissertations / Theses on the topic "Quantum Machine Learning (QML)"

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Huembeli, Patrick. "Machine learning for quantum physics and quantum physics for machine learning." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672085.

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Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our lives. The way we do research is almost certainly no exception and ML, with its unprecedented ability to find hidden patterns in data, will be assisting future scientific discoveries. Quantum physics on the other side, even though it is sometimes not entirely intuitive, is one of the most successful physical theories and
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De, Bonis Gianluca. "Rassegna su Quantum Machine Learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24652/.

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Il Quantum Computing (QC) e il Machine Learning (ML) sono due dei settori più promettenti dell’informatica al giorno d’oggi. Il primo riguarda l’utilizzo di proprietà fisiche di sistemi quantistici per realizzare computazioni, mentre il secondo algoritmi di apprendimento automatizzati capaci di riconoscere pattern nei dati. In questo elaborato vengono esposti alcuni dei principali algoritmi di Quantum Machine Learning (QML), ovvero versioni quantistiche dei classici algoritmi di ML. Il tutto è strutturato come un’introduzione all’argomento: inizialmente viene introdotto il QC spiegandone le pr
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Du, Yuxuan. "The Power of Quantum Neural Networks in The Noisy Intermediate-Scale Quantum Era." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24976.

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Machine learning (ML) has revolutionized the world in recent years. Despite the success, the huge computational overhead required by ML models makes them approach the limits of Moore’s law. Quantum machine learning (QML) is a promising way to conquer this issue, empowered by Google's demonstration of quantum computational supremacy. Meanwhile, another cornerstone in QML is validating that quantum neural networks (QNNs) implemented on the noisy intermediate-scale quantum (NISQ) chips can accomplish classification and image generation tasks. Despite the experimental progress, little is known abo
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Macaluso, Antonio <1990&gt. "A Novel Framework for Quantum Machine Learning." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9791/2/Antonio_Macaluso_tesi.pdf.

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Quantum computation is an emerging computing paradigm with the potential to revolutionise the world of information technology. It leverages the laws of quantum mechanics to endow quantum machines with tremendous computing power, thus enabling the solution of problems impossible to address with classical devices. For this reason, the field is attracting ever-increasing attention from both academic and private sectors, and its full potential is still to be understood. This dissertation investigates how classical machine learning can benefit from quantum computing and provides several contributio
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Rodriguez, Fernandez Carlos Gustavo. "Machine learning quantum error correction codes : learning the toric code /." São Paulo, 2018. http://hdl.handle.net/11449/180319.

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Orientador: Mario Leandro Aolita<br>Banca:Alexandre Reily Rocha<br>Banca: Juan Felipe Carrasquilla<br>Resumo: Usamos métodos de aprendizagem supervisionada para estudar a decodificação de erros em códigos tóricos de diferentes tamanhos. Estudamos múltiplos modelos de erro, e obtemos figuras da eficácia de decodificação como uma função da taxa de erro de um único qubit. Também comentamos como o tamanho das redes neurais decodificadoras e seu tempo de treinamento aumentam com o tamanho do código tórico.<br>Abstract: We use supervised learning methods to study the error decoding in toric codes ofdiff
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TACCHINO, FRANCESCO. "Digital quantum simulations and machine learning on near-term quantum processors." Doctoral thesis, Università degli studi di Pavia, 2020. http://hdl.handle.net/11571/1317093.

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Quantum mechanics is the gateway towards novel and potentially disruptive approaches to scientific and technical computing. In this thesis we explore, from a number of different perspectives, the effects of such strong relationship between the physical nature of information and the informational side of physical processes, with a focus on the digital quantum computing paradigm. After an extensive introduction to the theory of universal quantum simulation techniques, we review the main achievements in the field and, in parallel, we outline the state of the art of near-term architectures for qu
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Lukac, Martin. "Quantum Inductive Learning and Quantum Logic Synthesis." PDXScholar, 2009. https://pdxscholar.library.pdx.edu/open_access_etds/2319.

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Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. However, despite its growth, there is no any unified aproach to QLS as Quantum Computing is still being discovered and novel applications are being identified. The intent of this study is to experimentally explore principles of Quantum Logic Synthesis and its applications to Inductive Machine Learning. Based on algorithmic approach, I first design a Genetic Alg
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Cangini, Nicolò. "Quantum Supervised Learning: Algoritmi e implementazione." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17694/.

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Il Quantum Computing non riguarda più soltanto la scienza della Fisica, negli ultimi anni infatti la ricerca in questo campo ha subito una notevole espansione dimostrando l'enorme potenziale di cui dispongono questi nuovi calcolatori che in un futuro prossimo potranno rivoluzionare il concetto di Computer Science così come lo conosciamo. Ad oggi, siamo già in grado di realizzare algoritmi su piccola scala eseguibili in un quantum device grazie ai quali è possibile sperimentare uno speed-up notevole (in alcuni casi esponenziale) su diversi task tipici della computazione classica. In questo elab
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Orazi, Filippo. "Quantum machine learning: development and evaluation of the Multiple Aggregator Quantum Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25062/.

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Human society has always been shaped by its technology, so much that even ages and parts of our history are often named after the discoveries of that time. The growth of modern society is largely derived from the introduction of classical computers that brought us innovations like repeated tasks automatization and long-distance communication. However, this explosive technological advancement could be subjected to a heavy stop when computers reach physical limitations and the empirical law known as Moore Law comes to an end. Foreshadowing these limits and hoping for an even more powerful techno
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Felefly, Tony. "Quantum-classical machine learning for brain tumor imaging analysis." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAJ064.

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La caractérisation des tumeurs cérébrales par des techniques non invasives est nécéssaire. L'objectif est d'utiliser l'apprentissage automatique et la technologie quantique sur des imageries pour caractériser les tumeurs cérébrales. Nous développons un Réseau Neuronal Quantique en utilisant la radiomique des IRM cérébrales pour différencier métastases et gliomes de haut grade. Nous sélectionnons les variables en se basant sur l'information mutuelle et nous utilisons D-Wave pour la solution. Nous entraînons le modèle sur un Simulateur Quantique. Nous utilisons les valeurs de Shapley pour expliq
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Books on the topic "Quantum Machine Learning (QML)"

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Conti, Claudio. Quantum Machine Learning. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-44226-1.

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Karthikeyan, S., M. Akila, D. Sumathi, and T. Poongodi. Quantum Machine Learning. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003429654.

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Pattanayak, Santanu. Quantum Machine Learning with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6522-2.

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Schuld, Maria, and Francesco Petruccione. Machine Learning with Quantum Computers. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83098-4.

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Schütt, Kristof T., Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, and Klaus-Robert Müller, eds. Machine Learning Meets Quantum Physics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40245-7.

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Ganguly, Santanu. Quantum Machine Learning: An Applied Approach. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1.

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Pastorello, Davide. Concise Guide to Quantum Machine Learning. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6897-6.

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Santosh, K. C., Sandeep Kumar Sood, Hari Mohan Pandey, and Charu Virmani, eds. Advances in Artificial-Business Analytics and Quantum Machine Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2508-3.

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Santosh, KC, Poonam Nandal, Sandeep Kumar Sood, and Hari Mohan Pandey, eds. Advances in Artificial-Business Analytics and Quantum Machine Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4860-0.

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Subramanian, Thiruselvan, Archana Dhyani, Adarsh Kumar, and Sukhpal Singh Gill. Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network. CRC Press, 2022. http://dx.doi.org/10.1201/9781003250357.

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Book chapters on the topic "Quantum Machine Learning (QML)"

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Ponnusamy, Ponnuviji Namakkal, Indra Priyadharshini Sundar, and Nirmala Ganapath. "Boosting in QML." In Quantum Machine Learning. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003429654-12.

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Banafa, Ahmed. "Quantum Machine Learning (QML)." In Transformative AI. River Publishers, 2024. http://dx.doi.org/10.1201/9781032669182-25.

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Pastorello, Davide. "QML Toolkit." In Concise Guide to Quantum Machine Learning. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6897-6_5.

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Ganguly, Santanu. "QML Techniques." In Quantum Machine Learning: An Applied Approach. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_7.

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Ganguly, Santanu. "QML Algorithms II." In Quantum Machine Learning: An Applied Approach. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_6.

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Ganguly, Santanu. "QML: Way Forward." In Quantum Machine Learning: An Applied Approach. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_9.

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Ganguly, Santanu. "QML Algorithms I." In Quantum Machine Learning: An Applied Approach. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_5.

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Lin, Yanling, Ji Guan, Wang Fang, Mingsheng Ying, and Zhaofeng Su. "A obustness fication Tool for uantum Machine Learning Models." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71162-6_21.

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AbstractAdversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noisy Intermediate-Scale Quantum era, where noise is unavoidable. Therefore, it is essential to ensure the robustness of QML models before their deployment. To address this challenge, we introduce VeriQR, the first tool designed specifically for formally verifying and improving the robustness of QML models, to the best of our knowledge. This tool mimics real-world quantum hardware’s noisy impacts by incorporatin
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Ul Hassan Swati, Inzimam, and Aakansha Khanna. "Quantum Machine Learning (QML) Algorithms for Smart Biomedical Applications." In Artificial Intelligence and Optimization Techniques for Smart Information System Generations. CRC Press, 2025. https://doi.org/10.1201/9781003592969-8.

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Hilfiker, Mathias, Leonardo Medrano Sandonas, Marco Klähn, Ola Engkvist, and Alexandre Tkatchenko. "Leveraging Quantum Mechanical Properties to Predict Solvent Effects on Large Drug-Like Molecules." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72381-0_5.

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AbstractUnderstanding how solvation affects structure-property and property-property relationships of drug-like molecules is crucial for de novo design, as most relevant reactions occur in aqueous environments. We have thus performed an exhaustive analysis of the recently proposed Aquamarine dataset to gain insights into the effect of solvent-molecule interaction on the quantum-mechanical (QM) properties of large drug-like molecules. Our results show that the inclusion of an implicit solvent model of water changes the values of (extensive and intensive) QM properties but it does not alter the
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Conference papers on the topic "Quantum Machine Learning (QML)"

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Kamble, Sahil Somaji, Suyog Sudhir Pawar, Tejas Babasaheb Veer, and Digambar M. Padulkar. "Fraud Detection using Quantum Machine Learning(QML)." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862508.

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Abreu, Diego, Christian Esteve Rothenberg, and Antonio Abelém. "QML-IDS: Quantum Machine Learning Intrusion Detection System." In 2024 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2024. http://dx.doi.org/10.1109/iscc61673.2024.10733655.

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Braniff, Austin, Fengqi You, and Yuhe Tian. "Enhanced Reinforcement Learning-driven Process Design via Quantum Machine Learning." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.149501.

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In this work, we introduce a quantum-enhanced reinforcement learning (RL) framework for process design synthesis. RL-driven methods for generating process designs have gained momentum due to their ability to intelligently identify optimal configurations without requiring pre-defined superstructures or flowsheet configurations. This eliminates reliance on prior expert knowledge, offering a comprehensive and robust design strategy. However, navigating the vast combinatorial design space poses computational challenges. To address this, a novel approach integrating RL with quantum machine learning
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Wahidin, Khoirul Anwar, and Gelar Budiman. "Quantum Machine Learning (QML) for Irregular Complex Modulation of 8-QAM." In 2024 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE). IEEE, 2024. https://doi.org/10.1109/apace62360.2024.10876890.

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Tiwari, Rajinder, Sunil Semwal, Neetu Sharma, Bhawana, and Rajan Prasad. "An Innovative Approach of Design & Implementation of Quantum Machine Learning (QML) using AINN Algorithms." In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2024. https://doi.org/10.1109/icaccm61117.2024.11059197.

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Suprano, Alessia, Danilo Zia, Luca Innocenti, et al. "Photonic quantum extreme learning machine." In Quantum 2.0. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/quantum.2024.qw4a.2.

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We experimentally implemented a quantum extreme learning machine to re-construct the polarization state of single photons. Our approach offers a resource-efficient method that does not require a detailed apparatus calibration.
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Hai, Vu Tuan, Vo Minh Kiet, Le Vu Trung Duong, Pham Hoai Luan, Le Bin Ho, and Yasuhiko Nakashima. "Quantum Battery Optimization through Quantum Machine Learning Techniques." In 2024 21st International SoC Design Conference (ISOCC). IEEE, 2024. http://dx.doi.org/10.1109/isocc62682.2024.10762673.

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Rashid, Sara Mahmoudi. "Quantum Machine Learning Acceleration with Quantum Control Techniques." In 2024 6th Iranian International Conference on Microelectronics (IICM). IEEE, 2024. https://doi.org/10.1109/iicm65053.2024.10824322.

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Gince, Jérémie, Jean-Michel Pagé, Marco Armenta, Ayana Sarkar, and Stefanos Kourtis. "Fermionic Machine Learning." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.00195.

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Milas, P., S. S. Mahtab, T. Sujeta, M. G. Spencer, and B. Ozturk. "Hardware and Machine Learning Optimization of Diamond Quantum Sensors." In Quantum 2.0. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/quantum.2024.qtu3a.36.

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Quantum sensing with nitrogen vacancy (NV) color center defects in diamond was optimized with hardware and machine learning approaches, which led to the development of small footprint quantum sensor devices.
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Reports on the topic "Quantum Machine Learning (QML)"

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Zahorodk, Pavlo V., Yevhenii O. Modlo, Olga O. Kalinichenko, Tetiana V. Selivanova, and Serhiy O. Semerikov. Quantum enhanced machine learning: An overview. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4357.

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Machine learning is now widely used almost everywhere, primarily for forecasting. The main idea of the work is to identify the possibility of achieving a quantum advantage when solving machine learning problems on a quantum computer.
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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum
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Tretiak, Sergei, Benjamin Tyler Nebgen, Justin Steven Smith, Nicholas Edward Lubbers, and Andrey Lokhov. Machine Learning for Quantum Mechanical Materials Properties. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1498000.

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Liu, Minzhao, Ge Dong, Kyle Felker, et al. Exploration of Quantum Machine Learning and AI Accelerators for Fusion Science. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1840522.

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Billari, Francesco C., Johannes Fürnkranz, and Alexia Prskawetz. Timing, sequencing and quantum of life course events: a machine learning approach. Max Planck Institute for Demographic Research, 2000. http://dx.doi.org/10.4054/mpidr-wp-2000-010.

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Wu, Sau Lan. Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1971973.

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Pasupuleti, Murali Krishna. Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv525.

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Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences.
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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi225.

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Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts
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