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Books on the topic 'Quantum machine learning algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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

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2

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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3

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

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4

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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5

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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6

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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7

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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8

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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9

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

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10

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

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11

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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12

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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13

Travis, Kerzic, and United States. National Aeronautics and Space Administration., eds. mGA1.0: A common LISP implementation of a messy genetic algorithm. Research Institute for Computing and Information Systems, University of Houston, Clear Lake, 1990.

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14

Travis, Kerzic, and United States. National Aeronautics and Space Administration., eds. mGA1.0: A common LISP implementation of a messy genetic algorithm. Research Institute for Computing and Information Systems, University of Houston, Clear Lake, 1990.

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15

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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16

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

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17

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

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18

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

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19

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

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20

Song, Houbing Herbert, and Narayan Vyas. Quantum Machine Learning: Quantum Algorithms and Neural Networks. de Gruyter GmbH, Walter, 2024.

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21

Song, Houbing Herbert, and Narayan Vyas. Quantum Machine Learning: Quantum Algorithms and Neural Networks. de Gruyter GmbH, Walter, 2024.

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22

Song, Houbing Herbert, and Narayan Vyas. Quantum Machine Learning: Quantum Algorithms and Neural Networks. de Gruyter GmbH, Walter, 2024.

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23

Combarro, Elías F., and Samuel González-Castillo. Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-On Approach to Modern Quantum Algorithms. de Gruyter GmbH, Walter, 2023.

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24

Combarro, Elias F., and Samuel Gonzalez-Castillo. Practical Guide to Quantum Machine Learning and Quantum Optimisation: Hands-On Approach to Modern Quantum Algorithms. Packt Publishing, Limited, 2023.

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25

S, Oswalt Manoj. Quantum Computing and Artificial Intelligence: Training Machine and Deep Learning Algorithms on Quantum Computers. de Gruyter GmbH, Walter, 2023.

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26

S, Oswalt Manoj. Quantum Computing and Artificial Intelligence: Training Machine and Deep Learning Algorithms on Quantum Computers. de Gruyter GmbH, Walter, 2023.

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27

S, Oswalt Manoj. Quantum Computing and Artificial Intelligence: Training Machine and Deep Learning Algorithms on Quantum Computers. de Gruyter GmbH, Walter, 2023.

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28

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

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29

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

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30

Analog algorithm: Landscapes of machine learning. Fotohof Edition, 2020.

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31

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

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32

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

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33

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

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34

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

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35

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

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36

Machine Learning With Quantum Computers. Springer Nature, 2021.

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37

Vijay, Prity. Enhanced Machine Learning Algorithm: Big Data Perspectives. Editorial Academica Espanola, 2020.

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38

Malitsky, Yuri. Instance-Specific Algorithm Configuration. Springer, 2014.

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39

Malitsky, Yuri. Instance-Specific Algorithm Configuration. Springer, 2016.

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40

Malitsky, Yuri. Instance-Specific Algorithm Configuration. Springer, 2014.

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41

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

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42

Concise Guide to Quantum Machine Learning. Springer, 2023.

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43

Fiebrink, Rebecca A., and Baptiste Caramiaux. The Machine Learning Algorithm as Creative Musical Tool. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.23.

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Abstract:

Machine learning is the capacity of a computational system to learn structure from data in order to make predictions on new data. This chapter draws on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. It motivates a new understanding of learning algorithms as human-computer interfaces: like other interfaces, learning algorithms can be characterized by the ways their affordances intersect with goals of human users. The chapter also argues that the nature of interaction between use

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44

Joshi, Varad Vidyadhar. Expert-gate algorithm. 1992.

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45

Joshi, Varad Vidyadhar. Expert-gate algorithm. 1992.

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46

Awd, Mustafa Mamduh Mustafa. Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing. Springer Fachmedien Wiesbaden GmbH, 2023.

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47

Mehta, Shilpa, and Pallavi Sapkale. Quantum Computing and Machine Learning For 6G. Wiley & Sons, Incorporated, John, 2024.

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48

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

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49

Mehta, Shilpa, and Pallavi Sapkale. Quantum Computing and Machine Learning For 6G. Wiley & Sons, Incorporated, John, 2024.

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50

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

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