{
  "aviso": "speedup_declarado es lo que declara la fuente citada, NO una medición de Rosetta. Lo que Rosetta midió va en evidencia_rosetta, y para la mayoría del catálogo está vacío.",
  "procedencia": {
    "fuente": "Quantum Algorithm Zoo",
    "fuente_url": "https://quantumalgorithmzoo.org/",
    "instantanea_sha256": "dee7e76b5f19096ed329c88714744b93babf7b7d0296eb97e357b2582d16b75e",
    "generado_at": "2026-08-09"
  },
  "id": "machine-learning",
  "nombre": "Machine Learning",
  "categoria": "Optimization, Numerics, and Machine Learning",
  "categoria_id": "ONML",
  "problema": "Entrada paraguas: agrupa las tecnicas cuanticas propuestas para aprendizaje automatico. Es tambien el area donde mas claims cayeron por dequantizacion, es decir, por algoritmos clasicos que despues igualaron la supuesta ventaja.",
  "speedup_declarado": "Varies",
  "declarado_por": "Quantum Algorithm Zoo",
  "fuente_url": "https://quantumalgorithmzoo.org/#ML",
  "implementaciones": [
    {
      "nombre": "Classiq (QSVM)",
      "url": "https://short.classiq.io/qsvm"
    },
    {
      "nombre": "Classiq (Autoencoder)",
      "url": "https://short.classiq.io/autoencoder"
    },
    {
      "nombre": "PennyLane",
      "url": "https://pennylane.ai/qml/demos/tutorial_variational_classifier"
    }
  ],
  "referencias": [
    {
      "n": 11,
      "cita": "Ethan Bernstein and Umesh Vazirani Quantum complexity theory. In Proceedings of the 25th ACM Symposium on the Theory of Computing , pages 11-20, 1993.",
      "url": null
    },
    {
      "n": 23,
      "cita": "A. M. Childs, L. J. Schulman, and U. V. Vazirani Quantum algorithms for hidden nonlinear structures. In Proceedings of the 48th IEEE Symposium on Foundations of Computer Science , pages 395-404, 2007. [ arXiv:0705.2784 ]",
      "url": "http://arxiv.org/abs/0705.2784"
    },
    {
      "n": 31,
      "cita": "Thomas Decker, Jan Draisma, and Pawel Wocjan Quantum algorithm for identifying hidden polynomials. Quantum Information and Computation , 9(3):215-230, 2009. [ arXiv:0706.1219 ]",
      "url": "http://arxiv.org/abs/0706.1219"
    },
    {
      "n": 104,
      "cita": "Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd Quantum algorithm for solving linear systems of equations. Physical Review Letters 15(103):150502, 2009. [ arXiv:0811.3171 ]",
      "url": "http://arxiv.org/abs/0811.3171"
    },
    {
      "n": 146,
      "cita": "Ashley Montanaro The quantum query complexity of learning multilinear polynomials. Information Processing Letters , 112(11):438-442, 2012. [ arXiv:1105.3310 ]",
      "url": "http://arxiv.org/abs/1105.3310"
    },
    {
      "n": 192,
      "cita": "Kristen L. Pudenz and Daniel A. Lidar Quantum adiabatic machine learning. Quantum Information Processing , 12:2027, 2013. [ arXiv:1109.0325 ]",
      "url": "http://arxiv.org/abs/1109.0325"
    },
    {
      "n": 195,
      "cita": "Hartmut Neven, Vasil S. Denchev, Geordie Rose, and William G. Macready Training a binary classifier with the quantum adiabatic algorithm. arXiv:0811.0416 , 2008.",
      "url": "http://arxiv.org/abs/0811.0416"
    },
    {
      "n": 212,
      "cita": "Thomas Decker, Peter Høyer, Gabor Ivanyos, and Miklos Santha Polynomial time quantum algorithms for certain bivariate hidden polynomial problems arXiv:1305.1543",
      "url": "http://arxiv.org/abs/1305.1543"
    },
    {
      "n": 214,
      "cita": "Seth Lloyd, Masoud Mohseni, and Patrick Robentrost Quantum algorithms for supervised and unsupervised machine learning arXiv:1307.0411",
      "url": "http://arxiv.org/abs/1307.0411"
    },
    {
      "n": 221,
      "cita": "Nathan Wiebe, Ashish Kapoor, and Krysta Svore Quantum deep learning arXiv:1412.3489",
      "url": "http://arxiv.org/abs/1412.3489"
    },
    {
      "n": 222,
      "cita": "Seth Lloyd, Silvano Garnerone, and Paolo Zanardi Quantum algorithms for topological and geometric analysis of big data arXiv:1408.3106",
      "url": "http://arxiv.org/abs/1408.3106"
    },
    {
      "n": 224,
      "cita": "Markus Hunziker, David A. Meyer, Jihun Park, James Pommersheim, and Mitch Rothstein The geometry of quantum learning Quantum Information Processing 9:321-341, 2010. [ arXiv:quant-ph/0309059 ]",
      "url": "http://arxiv.org/abs/quant-ph/0309059"
    },
    {
      "n": 236,
      "cita": "Andrew W. Cross, Graeme Smith, and John A. Smolin Quantum learning robust to noise arXiv:1407.5088",
      "url": "http://arxiv.org/abs/1407.5088"
    },
    {
      "n": 237,
      "cita": "Aram W. Harrow and David J. Rosenbaum Uselessness for an oracle model with internal randomness Quantum Information and Computation 14(7/8):608-624, 2014 [ arXiv:1111.1462 ]",
      "url": "http://arxiv.org/abs/1111.1462"
    },
    {
      "n": 246,
      "cita": "Scott Aaronson Read the fine print Nature Physics 11:291-293, 2015. [ fulltext ]",
      "url": "http://www.scottaaronson.com/papers/qml.pdf"
    },
    {
      "n": 250,
      "cita": "S. Lloyd, M. Mohseni, and P. Rebentrost Quantum principal component analysis Nature Physics. 10(9):631, 2014. [ arXiv:1307.0401 ]",
      "url": "http://arxiv.org/abs/1307.0401"
    },
    {
      "n": 251,
      "cita": "Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd Quantum support vector machine for big data classification Phys. Rev. Lett. 113, 130503, 2014. [ arXiv:1307.0471 ]",
      "url": "http://arxiv.org/abs/1307.0471"
    },
    {
      "n": 299,
      "cita": "J. Adcock, E. Allen, M. Day, S. Frick, J. Hinchliff, M. Johnson, S. Morley-Short, S. Pallister, A. Price, and S. Stanisic Advances in quantum machine learning arXiv:1512.02900 , 2015.",
      "url": "http://arxiv.org/abs/1512.02900"
    },
    {
      "n": 309,
      "cita": "Iordanis Kerenidis and Anupam Prakash Quantum recommendation systems Innovations in Theoretical Computer Science (ITCS 2017) , LIPIcs, vol. 67 , pg. 1868-8969 . [ arXiv:1603.08675 ]",
      "url": "http://drops.dagstuhl.de/opus/portals/lipics/index.php?semnr=16054"
    },
    {
      "n": 331,
      "cita": "Peter Wittek Quantum Machine Learning: what quantum computing means to data mining Academic Press, 2014.",
      "url": null
    },
    {
      "n": 332,
      "cita": "Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione An introduction to quantum machine learning Contemporary Physics , 56(2):172, 2014. [ arXiv:1409.3097 ]",
      "url": "https://arxiv.org/abs/1409.3097"
    },
    {
      "n": 333,
      "cita": "J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd Quantum machine learning arXiv:1611.09347",
      "url": "https://arxiv.org/abs/1611.09347"
    },
    {
      "n": 334,
      "cita": "Esma Aïmeur, Gilles Brassard, and Sébastien Gambs Machine learning in a quantum world In Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence pg. 431-442, Springer, 2006.",
      "url": null
    },
    {
      "n": 335,
      "cita": "Vedran Dunjko, Jacob Taylor, and Hans Briegel Quantum-enhanced machine learning Phys. Rev. Lett 117:130501, 2016.",
      "url": null
    },
    {
      "n": 336,
      "cita": "Nathan Wiebe, Ashish Kapoor, and Krysta Svore Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning Quantum Information and Computation 15(3/4): 0318-0358, 2015. [ arXiv:1401.2142 ]",
      "url": "https://arxiv.org/abs/1401.2142"
    },
    {
      "n": 337,
      "cita": "Seokwon Yoo, Jeongho Bang, Changhyoup Lee, and Junhyoug Lee A quantum speedup in machine learning: finding a N-bit Boolean function for a classification New Journal of Physics 6(10):103014, 2014. [ arXiv:1303.6055 ]",
      "url": "https://arxiv.org/abs/1303.6055"
    },
    {
      "n": 338,
      "cita": "Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione Prediction by linear regression on a quantum computer Physical Review A 94:022342, 2016. [ arXiv:1601.07823 ]",
      "url": "https://arxiv.org/abs/1601.07823"
    },
    {
      "n": 339,
      "cita": "Zhikuan Zhao, Jack K. Fitzsimons, and Joseph F. Fitzsimons Quantum assisted Gaussian process regression arXiv:1512.03929",
      "url": "https://arxiv.org/abs/1512.03929"
    },
    {
      "n": 340,
      "cita": "Esma Aïmeur, Gilles Brassard, and Sébastien Gambs Quantum speed-up for unsupervised learning Machine Learning , 90(2):261-287, 2013.",
      "url": null
    },
    {
      "n": 341,
      "cita": "Nathan Wiebe, Ashish Kapoor, and Krysta Svore Quantum perceptron models Advances in Neural Information Processing Systems 29 (NIPS 2016), pg. 3999–4007, 2016. [ arXiv:1602.04799 ]",
      "url": "https://arxiv.org/abs/1602.04799"
    },
    {
      "n": 342,
      "cita": "G. Paparo, V. Dunjko, A. Makmal, M. Martin-Delgado, and H. Briegel Quantum speedup for active learning agents Physical Review X 4(3):031002, 2014. [ arXiv:1401.4997 ]",
      "url": "https://arxiv.org/abs/1401.4997"
    },
    {
      "n": 343,
      "cita": "Daoyi Dong, Chunlin Chen, Hanxiong Li, and Tzyh-Jong Tarn Quantum reinforcement learning IEEE Transactions on Systems, Man, and Cybernetics- Part B (Cybernetics) 38(5):1207, 2008.",
      "url": null
    },
    {
      "n": 344,
      "cita": "Daniel Crawford, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya Ronagh Reinforcement learning using quantum Boltzmann machines arXiv:1612.05695 , 2016.",
      "url": "https://arxiv.org/abs/1612.05695"
    },
    {
      "n": 345,
      "cita": "Steven H. Adachi and Maxwell P. Henderson Application of Quantum Annealing to Training of Deep Neural Networks arXiv:1510.06356 , 2015.",
      "url": "https://arxiv.org/abs/1510.06356"
    },
    {
      "n": 346,
      "cita": "M. Benedetti, J. Realpe-Gómez, R. Biswas, and A. Perdomo-Ortiz Quantum-assisted learning of graphical models with arbitrary pairwise connectivity arXiv:1609.02542 , 2016.",
      "url": "https://arxiv.org/abs/1609.02542"
    },
    {
      "n": 348,
      "cita": "M. H. Amin, E. Andriyash, J. Rolfe, B. Kulchytskyy, and R. Melko Quantum Boltzmann machine arXiv:1601.02036 , 2016.",
      "url": "https://arxiv.org/abs/1601.02036"
    },
    {
      "n": 349,
      "cita": "Peter Wittek and Christian Gogolin Quantum enhanced inference in Markov logic networks Scientific Reports 7:45672, 2017. [ arXiv:1611.08104 ]",
      "url": "https://arxiv.org/abs/1611.08104"
    },
    {
      "n": 350,
      "cita": "N. H. Bshouty and J. C. Jackson Learning DNF over the uniform distribution using a quantum example oracle SIAM Journal on Computing 28(3):1136-1153, 1999.",
      "url": null
    },
    {
      "n": 351,
      "cita": "Srinivasan Arunachalam and Ronald de Wolf A survey of quantum learning theory arXiv:1701.06806 , 2017.",
      "url": "https://arxiv.org/abs/1701.06806"
    },
    {
      "n": 352,
      "cita": "Rocco A. Servedio and Steven J. Gortler Equivalences and separations between quantum and classical learnability SIAM Journal on Computing , 33(5):1067-1092, 2017.",
      "url": null
    },
    {
      "n": 353,
      "cita": "Srinivasan Arunachalam and Ronald de Wolf Optimal quantum sample complexity of learning algorithms arXiv:1607.00932 , 2016.",
      "url": "https://arxiv.org/abs/1607.00932"
    },
    {
      "n": 354,
      "cita": "Alex Monràs, Gael Sentís, and Peter Wittek Inductive quantum learning: why you are doing it almost right arXiv:1605.07541 , 2016.",
      "url": "https://arxiv.org/abs/1605.07541"
    },
    {
      "n": 355,
      "cita": "A. Bisio, G. Chiribella, G. M. D'Ariano, S. Facchini, and P. Perinotti Optimal quantum learning of a unitary transformation Physical Review A 81:032324, 2010. [ arXiv:0903.0543 ]",
      "url": "https://arxiv.org/abs/0903.0543"
    },
    {
      "n": 356,
      "cita": "M. Sasaki, A. Carlini, and R. Jozsa Quantum template matching Physical Review A 64:022317, 2001. [ arXiv:quant-ph/0102020 ]",
      "url": "https://arxiv.org/abs/quant-ph/0102020"
    },
    {
      "n": 357,
      "cita": "Masahide Sasaki and Alberto Carlini Quantum learning and universal quantum matching machine Physical Review A 66:022303, 2002. [ arXiv:quant-ph/0202173 ]",
      "url": "https://arxiv.org/abs/quant-ph/0202173"
    },
    {
      "n": 358,
      "cita": "Esma Aïmeur, Gilles Brassard, and Sébastien Gambs Quantum clustering algorithms In Proceedings of the 24th International Conference on Machine Learning (ICML) , pg. 1-8, 2007.",
      "url": null
    },
    {
      "n": 359,
      "cita": "Iordanis Kerenidis and Anupam Prakash Quantum gradient descent for linear systems and least squares arXiv:1704.04992 , 2017.",
      "url": "https://arxiv.org/abs/1704.04992"
    },
    {
      "n": 400,
      "cita": "Ewin Tang A quantum-inspired classical algorithm for recommendation systems In Proceedings of STOC 2019 , pg. 217-228. [ arXiv:1807.04271 ]",
      "url": "https://arxiv.org/abs/1807.04271"
    },
    {
      "n": 401,
      "cita": "Ewin Tang Quantum-inspired classical algorithms for principal component analysis and supervised clustering arXiv:1811.00414 , 2018.",
      "url": "https://arxiv.org/abs/1811.00414"
    },
    {
      "n": 403,
      "cita": "Zhikuan Zhao, Alejandro Pozas-Kerstjens, Patrick Rebentrost, and Peter Wittek Bayesian Deep Learning on a Quantum Computer Quantum Machine Intelligence vol. 1, pg. 41-51, 2019. [ arXiv:1806.11463 ]",
      "url": "https://arxiv.org/abs/1806.11463"
    },
    {
      "n": 428,
      "cita": "Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme A rigorous and robust quantum speed-up in supervised machine learning arXiv:2010.02174",
      "url": "https://arxiv.org/abs/2010.02174"
    },
    {
      "n": 433,
      "cita": "András Gilyén, Yuan Su, Guang Hao Low, and Nathan Wiebe Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics Proceedings of STOC 2019 , pg. 193-204 [ arXiv:1806.01838 ]",
      "url": "https://arxiv.org/abs/1806.01838"
    },
    {
      "n": 456,
      "cita": "Xujie Song, Tong Liu, Shengbo Eben Li, Jingliang Duan, Wenxuan Wang, and Keqiang Li Training multi-layer neural networks on Ising machine arXiv:2311.03408 .",
      "url": "https://arxiv.org/abs/2311.03408"
    },
    {
      "n": 487,
      "cita": "Sam McArdle, András Gilyén, and Mario Berta A streamlined quantum algorithm for topological data analysis with exponentially fewer qubits arXiv:2209.12887 , 2022.",
      "url": "https://arxiv.org/abs/2209.12887"
    },
    {
      "n": 488,
      "cita": "Bernardo Ameneyro, Vasileios Maroulas, and George Siopsis Quantum persistent homology Journal of Applied and Computational Topology , 1-20, 2024. [ arXiv:2202.12965 ]",
      "url": "https://arxiv.org/abs/2202.12965"
    },
    {
      "n": 490,
      "cita": "Dominic W. Berry, Yuan Su, Casper Gyurik, Robbie King, Joao Basso, Alexander Del Toro Barba, Abhishek Rajput, Nathan Wiebe, Vedran Dunjko, and Ryan Babbush Analyzing prospects for quantum advantage in topological data analysis PRX Quantum , 5:010319, 2022. [ arXiv:2209.13581 ]",
      "url": "https://arxiv.org/abs/2209.13581"
    }
  ],
  "n_referencias": 56,
  "remisiones": [],
  "evidencia_rosetta": {
    "medido": false,
    "lectura": "Rosetta no tiene ninguna corrida sellada sobre este algoritmo. Que esté catalogado no significa que lo hayamos medido ni que lo ofrezcamos."
  }
}