Relevant bibliographies by topics / Quantum machine learning algorithm

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 algorithm'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Quantum machine learning algorithm"

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Liu, Xiaonan, Haoshan Xie, Zhengyu Liu, and Chenyan Zhao. "Survey on the Improvement and Application of HHL Algorithm." Journal of Physics: Conference Series 2333, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2333/1/012023.

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Abstract Quantum computing is a new computing mode that follows the laws of quantum mechanics to control quantum information units for computation. In terms of computational efficiency, due to the existence of quantum mechanical superposition, some known quantum algorithms can process problems faster than traditional general-purpose computers. HHL algorithm is an algorithm for solving linear system problems. Compared with classical algorithms in solving linear equations, it has an exponential acceleration effect in certain cases and as a sub-module, it is widely used in some machine learning a
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Sarkar, Soumyadip. "Quantum Machine Learning: A Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 352–54. http://dx.doi.org/10.22214/ijraset.2023.49421.

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Abstract: Quantum machine learning is an emerging field that aims to leverage the unique properties of quantum computing to accelerate machine learning tasks. In this paper, we review recent advances in quantum machine learning and discuss the potential applications and challenges associated with this technology. Specifically, we examine the current state of quantum machine learning algorithms, including variational quantum algorithms, quantum neural networks, and quantum generative models. We also discuss the challenges associated with practical quantum computing resources, algorithm design,
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BANG, Jeongho. "Machine Learning and Quantum Algorithm." Physics and High Technology 26, no. 12 (2017): 25–29. http://dx.doi.org/10.3938/phit.26.048.

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Rocha, Bruno Santos, Jose Antonio Moreira Xexeo, and Renato Hidaka Torres. "Post-quantum cryptographic algorithm identification using machine learning." Journal of Information Security and Cryptography (Enigma) 9, no. 1 (2022): 1–8. http://dx.doi.org/10.17648/jisc.v9i1.81.

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This research presents a study on the identification of post-quantum cryptography algorithms through machine learning techniques. Plain text files were encoded by four post-quantum algorithms, participating in NIST's post-quantum cryptography standardization contest, in ECB mode. The resulting cryptograms were submitted to the NIST Statistical Test Suite to enable the creation of metadata files. These files provide information for six data mining algorithms to identify the cryptographic algorithm used for encryption. Identification performance was evaluated in samples of different sizes. The s
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Tychola, Kyriaki A., Theofanis Kalampokas, and George A. Papakostas. "Quantum Machine Learning—An Overview." Electronics 12, no. 11 (2023): 2379. http://dx.doi.org/10.3390/electronics12112379.

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Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we con
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Ma, Yunpu, and Volker Tresp. "Quantum Machine Learning Algorithm for Knowledge Graphs." ACM Transactions on Quantum Computing 2, no. 3 (2021): 1–28. http://dx.doi.org/10.1145/3467982.

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Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computationally resource intensive. This article investigates how to capitalize on quantum resources to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for inference on tensorized data, i.e., on knowledge graphs.
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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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Gao, X., Z. Y. Zhang, and L. M. Duan. "A quantum machine learning algorithm based on generative models." Science Advances 4, no. 12 (2018): eaat9004. http://dx.doi.org/10.1126/sciadv.aat9004.

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Quantum computing and artificial intelligence, combined together, may revolutionize future technologies. A significant school of thought regarding artificial intelligence is based on generative models. Here, we propose a general quantum algorithm for machine learning based on a quantum generative model. We prove that our proposed model is more capable of representing probability distributions compared with classical generative models and has exponential speedup in learning and inference at least for some instances if a quantum computer cannot be efficiently simulated classically. Our result op
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Zhao, Zhikuan, Jack K. Fitzsimons, Patrick Rebentrost, Vedran Dunjko, and Joseph F. Fitzsimons. "Smooth input preparation for quantum and quantum-inspired machine learning." Quantum Machine Intelligence 3 (April 26, 2021): 14. https://doi.org/10.1007/s42484-021-00045-x.

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Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoot
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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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Dissertations / Theses on the topic "Quantum machine learning algorithm"

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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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Pérez, Salinas Adrián. "Algorithmic strategies for seizing quantum computing." Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/673255.

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Quantum computing is an emergent technology with prospects to solve problems nowadays intractable. For this purpose it is a requirement to build computers capable to store and control quantum systems without losing their quantum properties. However, these computers are hard to achieve, and in the near term there will only be Noisy Intermediate-Scale Quantum (NISQ) computers with limited performance. In order to seize quantum computing during the NISQ era, algorithms with low resource demands and capable to return approximate solutions are explored. This thesis presents
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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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Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Nella presente tesi di laurea viene preso in considerazione l’algoritmo di classificazione Support Vector Machine. Piu` in particolare si considera la sua formulazione come problema di ottimizazione Mixed Integer Program per la classificazione binaria super- visionata di un set di dati.
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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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Patel, Darshan D. "Vehicle classification using machine learning algorithm." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1604876.

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<p> Increasing traffic on roadways requires some real-time system that can collect traffic data and helps us to manage existing road infrastructure. For this purpose, we need a state of art system that can detect and classify vehicles into different categories. We developed an in-node microprocessor-based vehicle classification system to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm, which is implemented in machine learning software Weka. J48 is a Quinlan's C4.5 algorithm, an
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Cui, Yan Hong. "Contributions to statistical machine learning algorithm." Doctoral thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/10284.

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This thesis's research focus is on computational statistics along with DEAR (abbreviation of differential equation associated regression) model direction, and that in mind, the journal papers are written as contributions to statistical machine learning algorithm literature.
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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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Books on the topic "Quantum machine learning algorithm"

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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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Mamduh Mustafa Awd, Mustafa. Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-40237-2.

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Falkenhainer, Brian. The structure-mapping engine: Algorithm and examples. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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Köpf, Christian Rudolf. Meta-learning: Strategies, implementations, and evaluations for algorithm selection. Aka, 2006.

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Book chapters on the topic "Quantum machine learning algorithm"

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Guan, Ji, Wang Fang, and Mingsheng Ying. "Verifying Fairness in Quantum Machine Learning." In Computer Aided Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_20.

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AbstractDue to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition—any two similar individuals must be treated similarly and are thus unbia
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Guan, Ji, Wang Fang, and Mingsheng Ying. "Robustness Verification of Quantum Classifiers." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_7.

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AbstractSeveral important models of machine learning algorithms have been successfully generalized to the quantum world, with potential speedup to training classical classifiers and applications to data analytics in quantum physics that can be implemented on the near future quantum computers. However, quantum noise is a major obstacle to the practical implementation of quantum machine learning. In this work, we define a formal framework for the robustness verification and analysis of quantum machine learning algorithms against noises. A robust bound is derived and an algorithm is developed to
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Pastorello, Davide. "Relevant Quantum Algorithms." In Concise Guide to Quantum Machine Learning. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6897-6_4.

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Pattanayak, Santanu. "Introduction to Quantum Algorithms." In Quantum Machine Learning with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6522-2_3.

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Macaluso, Antonio, Filippo Orazi, Matthias Klusch, Stefano Lodi, and Claudio Sartori. "A Variational Algorithm for Quantum Single Layer Perceptron." In Machine Learning, Optimization, and Data Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25891-6_26.

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Pattanayak, Santanu. "Quantum Fourier Transform and Related Algorithms." In Quantum Machine Learning with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6522-2_4.

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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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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 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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Kasabov, Nikola. "Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework." In Advances in Machine Learning II. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05179-1_19.

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Conference papers on the topic "Quantum machine learning algorithm"

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Li, Zhenghao, Matthew J. H. Kendall, Ruidi Zhu, et al. "High-Rate Photon-Number Resolved Detection with Transition-Edge Sensors Enabled by Machine Learning." In Quantum 2.0. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/quantum.2024.qm2a.4.

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We report a machine-learning-based algorithm that allows transition-edge sensors to distinguish photon number traces at a repetition rate that overcomes their slow recovery time. Detector tomography is performed to benchmark the algorithm’s performance.
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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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Hernandez, Andres Correa, and Claire F. Gmachl. "Machine Learning for Quantum Cascade Laser Design and Optimization." In CLEO: Science and Innovations. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_si.2024.sw3h.3.

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A machine learning framework is used to predict the laser performance of 109 quantum cascade laser designs in 8 hours. The algorithm demonstrates how to optimize the layer structure, yielding a 2-fold increase in performance.
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Pellicer-Guridi, Ruben, Asier Mongelos, Jason Francis, Angel Cifuentes, and Gabriel Molina-Terriza. "Testbed for Automatized Machine Learning Optimization of Nitrogen Vacancy Center Based Magnetometry." In Quantum Sensing and Metrology. Optica Publishing Group, 2024. https://doi.org/10.1364/qsm.2024.qm2c.1.

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We present a versatile, robust and inexpensive setup for Nitrogen Vacancy center based sensing that enables automatized generation of large datasets to train machine learning algorithms towards fieldable advanced quantum magnetic field sensors.
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Kun, Zou, Dong Xinfeng, Zhang Fuzhong, and Cheng Rong. "Quantum Attack Implementation of TWINE Block Cipher." In 2025 4th Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2025. https://doi.org/10.1109/cacml64929.2025.11010933.

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Mankar, Bhagyashri, Mohini Wanjari, and Diksha Gabhane. "Spam SMS Classifier Using Machine Learning Algorithms." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882288.

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Aminpour, Sara, Yaser Banad, and Sarah Sharif. "Quantum Machine Learning Performance Analysis: Accuracy and Efficiency Trade-offs in Linear Classification." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jw5a.72.

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This study introduces the Nelder-Mead minimization method for data reuploading and examines the performance of quantum machine learning algorithms for linear classification using 1-qubit, 2-qubit, and 2-qubit entangled systems. We analyze accuracy and computation time across varying training sample sizes, revealing trade-offs between classification performance and computational efficiency in quantum systems.
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Naik, Salil, Glen Uehara, Kristen Jaskie, Leslie Miller, and Andreas Spanias. "Quantum Positive Unlabeled Learning Algorithms with Applications to Energy." In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2024. http://dx.doi.org/10.1109/mlsp58920.2024.10734794.

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Mishra, Priya, Rodney Lessard, and Indranil Roychoudhury. "Procedures for Evaluating Classical, Quantum, and Hybrid Machine Learning Algorithms." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10427.

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Rawat, Gourav, Sandeep Kumar, and Saptadeepa Kalita. "Automated Tuning of Machine Learning Parameters Using Quantum Evolutionary Algorithms." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10894796.

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Reports on the topic "Quantum machine learning algorithm"

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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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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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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platfor
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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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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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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate th
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Veals, Jeffrey, and Christopher Stone. Chemical Kinetics Database Translation for Machine-Learning-Based Algorithm Development. DEVCOM Army Research Laboratory, 2023. http://dx.doi.org/10.21236/ad1182193.

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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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