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This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a forecast problem in the airline industry.
Ivar Ekeland, “On the variational principle,” Journal of Mathematical Analysis and Applications 47
1974
Earlier work this paper cites.
PM Lerman, “Fitting segmented regression models by grid search,” Journal of the Royal Statistical Society: Series C (Applied Statistics) 29
1980
Earlier work this paper cites.
Francisco J Solis and Roger J-B Wets, “Minimization by random search techniques,” Mathematics of operations research 6
1981
Earlier work this paper cites.
Dong C. Liu and Jorge Nocedal, “On the limited memory BFGS method for large scale optimization,” Mathematical Programming 45
1989
Earlier work this paper cites.
Ronald J Williams and Jing Peng, “An efficient gradient-based algorithm for on-line training of recurrent network trajectories,” Neural computation 2
1990
Earlier work this paper cites.
Raymond E Wright, “Logistic regression.” (1995)
1995
Earlier work this paper cites.
Nicolo Cesa-Bianchi, “Analysis of two gradient-based algorithms for on-line regression,” in Proceedings of the tenth annual conference on Computational learning theory (1997) pp. 163–170
1997
Earlier work this paper cites.
Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf, “Support vector machines,” IEEE Intelligent Systems and their applications 13
1998
Earlier work this paper cites.
Andrew Steane, “Quantum computing,” Reports on Progress in Physics 61
1998
Earlier work this paper cites.
Jozef Gruska et al. , Quantum computing , Vol. 2005 (McGraw-Hill London, 1999)
1999
Earlier work this paper cites.
Yoshua Bengio, “Gradient-based optimization of hyperparameters,” Neural computation 12
2000
Earlier work this paper cites.
Michael W Browne, “Cross-validation methods,” Journal of Mathematical Psychology 44
2000
Earlier work this paper cites.
Spall James C., “Spsa community,” (2001)
2001
Earlier work this paper cites.
David G Kleinbaum, K Dietz, M Gail, Mitchel Klein, and Mitchell Klein, Logistic regression (Springer, 2002)
2002
Earlier work this paper cites.
Michael A. Nielsen, Isaac Chuang, and Lov K. Grover, “Quantum computation and quantum information,” American Journal of Physics 70
2002
Earlier work this paper cites.
Steven M LaValle, Michael S Branicky, and Stephen R Lindemann, “On the relationship between classical grid search and probabilistic roadmaps,” The International Journal of Robotics Research 23
2004
Earlier work this paper cites.
G Arutyunov, S Frolov, and M Staudacher, “Bethe ansatz for quantum strings,” Journal of High Energy Physics 2004
2004
Earlier work this paper cites.
Stefan Lessmann, Robert Stahlbock, and Sven F Crone, “Optimizing hyperparameters of support vector machines by genetic algorithms.” in IC-AI , Vol. 74 (2005) p. 82
2005
Earlier work this paper cites.
Derya Birant and Alp Kut, “St-dbscan: An algorithm for clustering spatial–temporal data,” Data & knowledge engineering 60
2007
Earlier work this paper cites.
Richard M Dudley, “Sample functions of the gaussian process,” in Selected works of RM Dudley (Springer, 2010) pp. 187–224
2010
Earlier work this paper cites.
Xin-She Yang, Nature-inspired metaheuristic algorithms (Luniver press, 2010)
2010
Earlier work this paper cites.
James Bergstra and Yoshua Bengio, “Random search for hyper-parameter optimization.” Journal of machine learning research 13
2012
Earlier work this paper cites.
Hongfang Zhou, Peng Wang, and Hongyan Li, “Research on adaptive parameters determination in dbscan algorithm,” Journal of Information & Computational Science 9
2012
Earlier work this paper cites.
Rémi Bardenet, Mátyás Brendel, Balázs Kégl, and Michele Sebag, “Collaborative hyperparameter tuning,” in International conference on machine learning (PMLR, 2013) pp. 199–207
2013
Cited alongside, same era.
Katharina Eggensperger, Matthias Feurer, Frank Hutter, James Bergstra, Jasper Snoek, Holger Hoos, Kevin Leyton-Brown, et al. , “Towards an empirical foundation for assessing bayesian optimization of hyperparameters,” in NIPS workshop on Bayesian Optimization in Theory and Practice , Vol. 10 (2013)
2013
Cited alongside, same era.
Thomas S Ferguson, Mathematical statistics: A decision theoretic approach , Vol. 1 (Academic press, 2014)
2014
Cited alongside, same era.
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “An introduction to quantum machine learning,” Contemporary Physics 56
2014
Cited alongside, same era.
Diederik P. Kingma and Jimmy Ba, “Adam: A method for stochastic optimization,” (2014)
Sukin Sim, Peter D. Johnson, and Alán Aspuru‐Guzik, “Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum‐classical algorithms,” Advanced Quantum Technologies 2
2019
Later among the works it cites.
Andrej Miklosik and Nina Evans, “Impact of big data and machine learning on digital transformation in marketing: A literature review,” Ieee Access 8
2020
Later among the works it cites.
Li Yang and Abdallah Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing 415
2020
Later among the works it cites.
Valentina Cacchiani and Juan-José Salazar-González, “Heuristic approaches for flight retiming in an integrated airline scheduling problem of a regional carrier,” Omega 91
2020
Later among the works it cites.
Axel Parmentier and Frédéric Meunier, “Aircraft routing and crew pairing: Updated algorithms at air france,” Omega 93
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2014
Cited alongside, same era.
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas, “Taking the human out of the loop: A review of bayesian optimization,” Proceedings of the IEEE 104
2015
Cited alongside, same era.
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “An introduction to quantum machine learning,” Contemporary Physics 56
2015
Cited alongside, same era.
Vedran Dunjko, Jacob M Taylor, and Hans J Briegel, “Quantum-enhanced machine learning,” Physical review letters 117
2016
Cited alongside, same era.
Alison Callahan and Nigam H Shah, “Machine learning in healthcare,” in Key Advances in Clinical Informatics (Elsevier, 2017) pp. 279–291
2017
Cited alongside, same era.
Yangyang Li, Gao Lu, Linhao Zhou, and Licheng Jiao, “Quantum inspired high dimensional hyperparameter optimization of machine learning model,” in 2017 International Smart Cities Conference (ISC2) (2017) pp. 1–6
2017
Cited alongside, same era.
Alexios Koutsoukas, Keith J Monaghan, Xiaoli Li, and Jun Huan, “Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data,” Journal of cheminformatics 9
2017
Cited alongside, same era.
Pablo Ribalta Lorenzo, Jakub Nalepa, Michal Kawulok, Luciano Sanchez Ramos, and José Ranilla Pastor, “Particle swarm optimization for hyper-parameter selection in deep neural networks,” in Proceedings of the genetic and evolutionary computation conference (2017) pp. 481–488
2017
Cited alongside, same era.
2020
Later among the works it cites.
Parfait Atchade Adelomou, Elisabet Golobardes Ribé, and Xavier Vilasís Cardona, “Using the variational-quantum-eigensolver (vqe) to create an intelligent social workers schedule problem solver,” in International Conference on Hybrid Artificial Intelligence Systems (Springer, 2020) pp. 245–260
2020
Later among the works it cites.
Atchade Parfait Adelomou, Elisabet Golobardes Ribe, and Xavier Vilasis Cardona, “Using the parameterized quantum circuit combined with variational-quantum-eigensolver (vqe) to create an intelligent social workers’ schedule problem solver,” (2020)
2020
Later among the works it cites.
Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre, “Data re-uploading for a universal quantum classifier,” Quantum 4
2020
Later among the works it cites.
Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I Latorre, “Data re-uploading for a universal quantum classifier,” Quantum 4
2020
Later among the works it cites.
Harikumar Pallathadka, Malik Mustafa, Domenic T Sanchez, Guna Sekhar Sajja, Sanjeev Gour, and Mohd Naved, “Impact of machine learning on management, healthcare and agriculture,” Materials Today: Proceedings (2021)
2021
Later among the works it cites.
community The SciPy, “Cobyla,” (2021)
2021
Later among the works it cites.
Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al. , “Variational quantum algorithms,” Nature Reviews Physics 3
2021
Later among the works it cites.
David Anderson, Margret V Bjarnadottir, and Zlatana Nenova, “Machine learning in healthcare: Operational and financial impact,” Innovative Technology at the Interface of Finance and Operations: Volume I , 153–174 (2022)
2022
Later among the works it cites.
Asel Sagingalieva, Andrii Kurkin, Artem Melnikov, Daniil Kuhmistrov, Michael Perelshtein, Alexey Melnikov, Andrea Skolik, and David Von Dollen, “Hyperparameter optimization of hybrid quantum neural networks for car classification,” (2022)
2022
Later among the works it cites.
Parfait Atchade-Adelomou, “Quantum algorithms for solving hard constrained optimisation problems,” (2022)
2022
Later among the works it cites.
Saul Gonzalez-Bermejo, Guillermo Alonso-Linaje, and Parfait Atchade-Adelomou, “Gps: A new tsp formulation for its generalizations type qubo,” Mathematics 10
2022
Later among the works it cites.
Charles Moussa, Jan N. van Rijn, Thomas Bäck, and Vedran Dunjko (Springer Nature Switzerland, 2022) pp. 32–46
2022
Later among the works it cites.
Raúl Berganza Gómez, Corey O’Meara, Giorgio Cortiana, Christian B Mendl, and Juan Bernabé-Moreno, “Towards autoqml: A cloud-based automated circuit architecture search framework,” in 2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C) (IEEE, 2022) pp. 129–136
2022
Later among the works it cites.
Parfait Atchade-Adelomou and Guillermo Alonso-Linaje, “Quantum-enhanced filter: Qfilter,” Soft Computing , 1–8 (2022)
2022
Later among the works it cites.
Parfait Atchade Adelomou, Daniel Casado Fauli, Elisabet Golobardes Ribé, and Xavier Vilasis-Cardona, “Quantum case-based reasoning (qcbr),” Artificial Intelligence Review , 1–27 (2022)
2022
Later among the works it cites.
Apple, “Macbookpro 15,1,” https://support.apple.com/kb/SP776?locale=es_ES (2022 (accessed Dec 26, 2022))
2022
Later among the works it cites.
Parfait Atchade-Adelomou and Kent Larson, “Fourier series weight in quantum machine learning,” arXiv Pending (2023)
2023
Closest in time.
Alonso-Linaje Guillermo, “¿quieres aprender computación cuántica ?” (2023)
2023
Closest in time.