Relevant bibliographies by topics / Quantum Machine Learning

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'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Quantum Machine Learning"

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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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Peleshenko, Vitaly A. "QUANTUM MACHINE LEARNING." SOFT MEASUREMENTS AND COMPUTING 11, no. 60 (2022): 82–107. http://dx.doi.org/10.36871/2618-9976.2022.11.008.

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Johnson, Sarah L. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 1, no. 02 (2025): 1–11. https://doi.org/10.63665/ijicsitr.v1i02.04.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discu
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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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Biamonte, Jacob, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. "Quantum machine learning." Nature 549, no. 7671 (2017): 195–202. http://dx.doi.org/10.1038/nature23474.

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Allcock, Jonathan, and Shengyu Zhang. "Quantum machine learning." National Science Review 6, no. 1 (2018): 26–28. http://dx.doi.org/10.1093/nsr/nwy149.

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Chen, Samuel Yen-Chi, and Shinjae Yoo. "Federated Quantum Machine Learning." Entropy 23, no. 4 (2021): 460. http://dx.doi.org/10.3390/e23040460.

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Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federa
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Sarah, L. Johnson. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 01, no. 02 (2025): 31–41. https://doi.org/10.5281/zenodo.15147384.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behi
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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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Pudenz, Kristen L., and Daniel A. Lidar. "Quantum adiabatic machine learning." Quantum Information Processing 12, no. 5 (2012): 2027–70. http://dx.doi.org/10.1007/s11128-012-0506-4.

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Dissertations / Theses on the topic "Quantum Machine Learning"

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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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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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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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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"

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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"

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Palani, Durgadevi, and Akila Krishnamoorthy. "Quantum Classification." In Quantum Machine Learning. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003429654-11.

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Schuld, Maria, and Francesco Petruccione. "Machine Learning." In Quantum Science and Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96424-9_2.

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Schuld, Maria, and Francesco Petruccione. "Machine Learning." In Quantum Science and Technology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83098-4_2.

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Schuld, Maria, and Francesco Petruccione. "Quantum Machine Learning." In Encyclopedia of Machine Learning and Data Science. Springer US, 2023. http://dx.doi.org/10.1007/978-1-4899-7502-7_913-2.

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Pattanayak, Santanu. "Quantum Machine Learning." In Quantum Machine Learning with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6522-2_5.

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Schuld, Maria, and Francesco Petruccione. "Quantum Machine Learning." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_913-1.

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Schuld, Maria, and Francesco Petruccione. "Quantum Machine Learning." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_913.

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Anandan, Indhuja, and Lalith Prem Ravi. "Deep Quantum Learning." In Quantum Machine Learning. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003429654-14.

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Ramasamy, Ramani, Thiruselvan Palusamy, and Ramathilagam Arunagiri. "Quantum Information Science." In Quantum Machine Learning. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003429654-9.

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Poobala, Mangalraj, Ganesh Kumar Natarajan, Iniyan Shanmugam, and Justin Vargese. "Quantum Machine Learning Approaches." In Quantum Machine Learning. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003429654-10.

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Conference papers on the topic "Quantum Machine Learning"

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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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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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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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Hickmann, M. Lautaro, Markus Lange, and Hans-Martin Rieser. "Enhancing Machine Learning with Quantum Methods." In ESANN 2025. Ciaco - i6doc.com, 2025. https://doi.org/10.14428/esann/2025.es2025-29.

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Srinidhi, Srivageesh K., K. S. Vishal, U. Someswara Shashank, and Meena Belwal. "Quantum Machine Learning Compiler for Hybrid Quantum-Classical Models." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725839.

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Vimal, Dixit. "PIMA Diabetes Prediction Using Machine Learning and Quantum Machine Learning Techniques." In 2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K). IEEE, 2024. https://doi.org/10.23919/ituk62727.2024.10772814.

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Fyrillas, Andreas, Olivier Faure, Nicolas Maring, Jean Senellart, and Nadia Belabas. "High-Fidelity Quantum Information Processing with Machine Learning-Characterized Photonic Circuits." In Quantum 2.0. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/quantum.2024.qw4a.1.

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Photonic integrated circuits (PICs) are attractive platforms for manipulating quantum light. Imperfections limit the fidelity of photonically integrated quantum information protocols. We use machine learning and a clear box approach to characterize large PICs and mitigate imperfections, achieving high-fidelity for scalable implementations.
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Pfister, Olivier, Amanuel Anteneh, and Léandre Brunel. "Machine learning for efficient generation of universal photonic quantum computing resources." In Quantum Communications and Quantum Imaging XXII, edited by Keith S. Deacon and Ronald E. Meyers. SPIE, 2024. http://dx.doi.org/10.1117/12.3030556.

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Reports on the topic "Quantum Machine Learning"

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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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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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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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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. 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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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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10

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:
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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