Bibliografía

Bibliografía#

1: Reglas de born. URL: https://publish.obsidian.md/myquantumwell/Quantum+Mechanics/Quantum+Measurement/Born+rule.
2: Catalina Albornoz. How to start learning quantum machine learning. URL: https://pennylane.ai/blog/2021/10/how-to-start-learning-quantum-machine-learning/.
3: aprendemachinelearning. Comprende principal. URL: https://www.aprendemachinelearning.com/comprende-principal-component-analysis/.
4: V. Robles B. Descenso por gradiente: una breve introducción - parte 1. URL: vlarobbyk/descenso-por-gradiente.
5: Nils Barth. The gramian and k-volume in n-space: some classical results in linear algebra. Journal of Young Investigators, 1999.
6: Universitat Oberta Catalunya. Concavidad y convexidad de una función. URL: http://cimanet.uoc.edu/cursMates0/IniciacionMatematicas/s11/2_6_8.html.
7: M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, and Patrick J. Coles. Variational quantum algorithms. Nature Reviews Physics, 3(9):625–644, August 2021. URL: http://dx.doi.org/10.1038/s42254-021-00348-9, doi:10.1038/s42254-021-00348-9.
8: C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:273–297, 1995. doi:https://doi.org/10.1007/BF00994018.
9: Coursera. Gradient descent with momentum. URL: https://www.coursera.org/lecture/deep-neural-network/gradient-descent-with-momentum-y0m1f.
10: Juankboards en Planeta Chatbot. Conceptos fundamentales en machine learning: función de pérdida y optimización. URL: https://planetachatbot.com/conceptos-fundamentales-en-machine-learning-funcon-de-perdida-y-optimizacion/.
11: R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(7):179–188, 1936.
12: Institute for Quantum Computing. Quantum kernel methods. URL: PaddlePaddle/Quantum.
13: geeksforgeeks. The ultimate guide to quantum machine learning – the next big thing. URL: https://www.geeksforgeeks.org/the-ultimate-guide-to-quantum-machine-learning-the-next-big-thing/.
14: Marc G Genton. Classes of kernels for machine learning: a statistics perspective. Journal of machine learning research, 2(Dec):299–312, 2001.
15: Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference and prediction. Springer, 2 edition, 2009. URL: http://www-stat.stanford.edu/~tibs/ElemStatLearn/.
16: Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta. Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747):209–212, March 2019. URL: http://dx.doi.org/10.1038/s41586-019-0980-2, doi:10.1038/s41586-019-0980-2.
17: Thomas Hofmann, Bernhard Schölkopf, and Alexander J. Smola. Kernel methods in machine learning. The Annals of Statistics, 36(3):1171 – 1220, 2008. URL: https://doi.org/10.1214/009053607000000677, doi:10.1214/009053607000000677.
18: Hsin-Yuan Huang, Mick Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R. McClean. Power of data in quantum machine learning. Nature Communications, 2020. URL: https://api.semanticscholar.org/CorpusID:226246283.
19: IBM. Quantum machine learning course. URL: https://learn.qiskit.org/course/machine-learning/introduction.
20: IBM. ¿qué es el descenso de gradiente? URL: https://www.ibm.com/mx-es/topics/gradient-descent.
21: IBM. ¿qué es machine learning? URL: https://www.ibm.com/es-es/topics/machine-learning.
22: Keras. Adadelta. URL: https://keras.io/api/optimizers/adadelta/.
23: Diederik P Kingma and Jimmy Ba. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
24: Seth Lloyd, Maria Schuld, Aroosa Ijaz, Josh Izaac, and Nathan Killoran. Quantum embeddings for machine learning. 2020. arXiv:2001.03622.
25: Antonio Macaluso, Luca Clissa, Stefano Lodi, and Claudio Sartori. A variational algorithm for quantum neural networks. In Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, and João Teixeira, editors, Computational Science – ICCS 2020, 591–604. Cham, 2020. Springer International Publishing.
26: Jose Martinez. Gradiente descendiente. URL: https://www.iartificial.net/gradiente-descendiente-para-aprendizaje-automatico/.
27: Mikko Mottonen, Juha J. Vartiainen, Ville Bergholm, and Martti M. Salomaa. Transformation of quantum states using uniformly controlled rotations. 2004. arXiv:quant-ph/0407010.
28: Pennylane. Momentum optimizer. URL: https://docs.pennylane.ai/en/stable/code/api/pennylane.MomentumOptimizer.html.
29: Pennylane. Nesterov momentum optimizer. URL: https://docs.pennylane.ai/en/stable/code/api/pennylane.NesterovMomentumOptimizer.html.
30: Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre. Data re-uploading for a universal quantum classifier. Quantum, 4:226, February 2020. URL: http://dx.doi.org/10.22331/q-2020-02-06-226, doi:10.22331/q-2020-02-06-226.
31: Qibo. Qibo. URL: https://qibo.science/.
32: Qiskit. Quantum machine learning. URL: Qiskit/textbook.
33: qm-ware. Qmware. URL: https://www.qm-ware.com/.
34: Maria Schuld. Kernel-based training of quantum models with scikit-learn. URL: https://pennylane.ai/qml/demos/tutorial_kernel_based_training.html.
35: Maria Schuld. Supervised quantum machine learning models are kernel methods. arXiv preprint arXiv:2101.11020, 2021.
36: Maria Schuld and Nathan Killoran. Quantum machine learning in feature hilbert spaces. Physical Review Letters, February 2019. URL: http://dx.doi.org/10.1103/PhysRevLett.122.040504, doi:10.1103/physrevlett.122.040504.
37: Maria Schuld and Nathan Killoran. Quantum machine learning in feature hilbert spaces. Phys. Rev. Lett., 122:040504, Feb 2019. URL: https://link.aps.org/doi/10.1103/PhysRevLett.122.040504, doi:10.1103/PhysRevLett.122.040504.
38: scikit-learn. Support vector machines. URL: https://scikit-learn.org/stable/modules/svm.html#.
39: Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. On the importance of initialization and momentum in deep learning. In International conference on machine learning, 1139–1147. PMLR, 2013.
40: Wikipedia. Broyden–fletcher–goldfarb–shanno algorithm. URL: https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm.
41: Srinivasan Arunachalam Yunchao Liu and Kristan Temme. A rigorous and robust quantum speed-up in supervised machine learning. URL: https://people.eecs.berkeley.edu/~yunchaoliu/slides/quantumkernel_IBM.pdf.
42: Matthew D Zeiler. Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701, 2012.