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Libros sobre el tema "Quantum Machine Learning (QML)"

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Autor: Grafiati
Publicado: 6 de septiembre de 2023

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1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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7

Ran, Shi-Ju. Tensor Network Contractions: Methods and Applications to Quantum Many-Body Systems. Cham: Springer Nature, 2020.

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8

Bhattacharyya, Siddhartha, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman y Susanta Chakraborti, eds. Quantum Machine Learning. De Gruyter, 2020. http://dx.doi.org/10.1515/9783110670707.

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9

Quantum Machine Learning. Elsevier, 2014. http://dx.doi.org/10.1016/c2013-0-19170-2.

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10

Bhattacharyya, Siddhartha, Indrajit Pan, Ashish Mani, Elizabeth Behrman y Susanta Chakraborti. Quantum Machine Learning. de Gruyter GmbH, Walter, 2020.

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11

Bhattacharyya, Siddhartha, Indrajit Pan, Ashish Mani, Elizabeth Behrman y Susanta Chakraborti. Quantum Machine Learning. de Gruyter GmbH, Walter, 2020.

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12

Bhattacharyya, Siddhartha, Indrajit Pan, Ashish Mani, Elizabeth Behrman y Susanta Chakraborti. Quantum Machine Learning. de Gruyter GmbH, Walter, 2020.

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13

Petruccione, Francesco y Maria Schuld. Machine Learning with Quantum Computers. Springer International Publishing AG, 2021.

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14

Machine Learning with Quantum Computers. Springer International Publishing AG, 2022.

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15

Chapline, George. Quantum Mechanics and Machine Learning. World Scientific Publishing Co Pte Ltd, 2020.

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16

Müller, Klaus-Robert, Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko y Koji Tsuda. Machine Learning Meets Quantum Physics. Springer, 2020.

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17

HAMILTON. Quantum Machine Learning: Concepts Poshb. Institute of Physics Publishing, 2023.

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18

Pastorello, Davide. Concise Guide to Quantum Machine Learning. Springer, 2022.

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19

Simeone, Osvaldo. Introduction to Quantum Machine Learning for Engineers. Now Publishers, 2022.

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20

Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science & Technology Books, 2014.

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21

Wittek, Peter. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science & Technology Books, 2016.

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22

Wittek, Peter. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science & Technology Books, 2014.

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23

Ganguly, Santanu. Quantum Machine Learning : an Applied Approach: The Theory and Application of Quantum Machine Learning in Science and Industry. Apress L. P., 2021.

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24

Wichert, Andrzej. Principles of Quantum Artificial Intelligence: Quantum Problem Solving and Machine Learning. World Scientific Publishing Co Pte Ltd, 2020.

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25

Quantum Chemistry in the Age of Machine Learning. Elsevier, 2023. http://dx.doi.org/10.1016/c2020-0-03124-5.

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26

Dral, Pavlo O. Quantum Chemistry in the Age of Machine Learning. Elsevier, 2022.

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27

Dral, Pavlo O. Quantum Chemistry in the Age of Machine Learning. Elsevier, 2022.

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28

Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage. Packt Publishing, Limited, 2022.

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29

Subramanian, Thiruselvan, Archana Dhyani, Adarsh Kumar y Sukhpal Singh Gill. Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network. Taylor & Francis Group, 2022.

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30

Subramanian, Thiruselvan. Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network. CRC Press LLC, 2022.

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31

Subramanian, Thiruselvan, Archana Dhyani, Adarsh Kumar y Sukhpal Singh Gill. Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network. Taylor & Francis Group, 2022.

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32

Subramanian, Thiruselvan. Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network. CRC Press LLC, 2022.

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33

Subramanian, Thiruselvan, Archana Dhyani, Adarsh Kumar y Sukhpal Singh Gill. Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network. Taylor & Francis Group, 2022.

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34

Pattanayak, Santanu. Quantum Machine Learning with Python: Using Cirq from Google Research and IBM Qiskit. Apress, 2021.

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35

Witte, Frank, Melanie Swan y Renato P. Dos Santos. Blockchain Physics: Quantum Computing, Distributed Ledgers, Machine Learning, and Other Smart Network Technologies. World Scientific Publishing UK Limited, 2020.

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36

Kovera, Artem. How to Create Machine Superintelligence: A Quick Journey Through Classical/Quantum Computing, Artificial Intelligence, Machine Learning, and Neural Networks. Independently Published, 2017.

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37

Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

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38

Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

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39

Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

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40

Tirrito, Emanuele, Cheng Peng, Xi Chen, Shi-Ju Ran y Luca Tagliacozzo. Tensor Network Contractions: Methods and Applications to Quantum Many-Body Systems. Springer International Publishing AG, 2020.

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41

Di Ventra, Massimiliano. MemComputing. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192845320.001.0001.

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From the originator of MemComputing comes the very first book on this new computing paradigm that employs time non-locality (memory) to both process and store information. The book discusses the rationale behind MemComputing, its theoretical foundations, and wide-range applicability to combinatorial optimization problems, Machine Learning, and Quantum Mechanics. The book is ideal for graduate students in Physics, Computer Science, Electrical Engineering, and Mathematics as well as researchers in both academia and industry interested in unconventional computing. The author relies on extensive margin notes, important remarks, and several artworks to better explain the main concepts and clarify all the jargon, making the book as self-contained as possible. The reader will be guided from the basic notions to the more advanced ones with a writing style that is always clear and engaging. Along the way, the reader will appreciate the advantages of this computing paradigm and the major differences that set it apart from the prevailing Turing model of computation, and even Quantum Computing.
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42

Heng, Liao y Bill McColl, eds. Mathematics for Future Computing and Communications. Cambridge University Press, 2021. http://dx.doi.org/10.1017/9781009070218.

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For 80 years, mathematics has driven fundamental innovation in computing and communications. This timely book provides a panorama of some recent ideas in mathematics and how they will drive continued innovation in computing, communications and AI in the coming years. It provides a unique insight into how the new techniques that are being developed can be used to provide theoretical foundations for technological progress, just as mathematics was used in earlier times by Turing, von Neumann, Shannon and others. Edited by leading researchers in the field, chapters cover the application of new mathematics in computer architecture, software verification, quantum computing, compressed sensing, networking, Bayesian inference, machine learning, reinforcement learning and many other areas.
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