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ORCIDAlejandro Perdomo-Ortiz
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2020 – today
- 2023
[j7]Dmitri Iouchtchenko
, Jérôme F. Gonthier, Alejandro Perdomo-Ortiz, Roger G. Melko:
Neural network enhanced measurement efficiency for molecular groundstates. Mach. Learn. Sci. Technol. 4(1): 15016 (2023)- [j6]Javier Lopez-Piqueres, Jing Chen, Alejandro Perdomo-Ortiz:
Symmetric tensor networks for generative modeling and constrained combinatorial optimization. Mach. Learn. Sci. Technol. 4(3): 35009 (2023) - [i8]Mohamed Hibat-Allah, Marta Mauri, Juan Carrasquilla, Alejandro Perdomo-Ortiz:
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models. CoRR abs/2303.15626 (2023) - [i7]Alex Meiburg, Jing Chen, Jacob Miller, Raphaëlle Tihon, Guillaume Rabusseau, Alejandro Perdomo-Ortiz:
Generative Learning of Continuous Data by Tensor Networks. CoRR abs/2310.20498 (2023) - 2022
- [i6]Kaitlin Gili, Marta Mauri, Alejandro Perdomo-Ortiz:
Evaluating Generalization in Classical and Quantum Generative Models. CoRR abs/2201.08770 (2022) - [i5]Kaitlin Gili, Mohamed Hibat-Allah, Marta Mauri, Chris Ballance, Alejandro Perdomo-Ortiz:
Do Quantum Circuit Born Machines Generalize? CoRR abs/2207.13645 (2022) - 2021
- [j5]Oscar J. Perdomo, Vicente Leyton-Ortega, Alejandro Perdomo-Ortiz:
Entanglement types for two-qubit states with real amplitudes. Quantum Inf. Process. 20(3): 1-12 (2021) - [j4]Vicente Leyton-Ortega, Alejandro Perdomo-Ortiz, Oscar J. Perdomo:
Robust implementation of generative modeling with parametrized quantum circuits. Quantum Mach. Intell. 3(1): 1-10 (2021) - 2020
- [j3]Javier Alcazar, Vicente Leyton-Ortega, Alejandro Perdomo-Ortiz:
Classical versus quantum models in machine learning: insights from a finance application. Mach. Learn. Sci. Technol. 1(3): 35003 (2020)
2010 – 2019
- 2017
- [j2]Rupak Biswas, Zhang Jiang, Kostya Kechezhi, Sergey Knysh, Salvatore Mandrà, Bryan O'Gorman, Alejandro Perdomo-Ortiz, Andre Petukhov, John Realpe-Gomez, Eleanor Gilbert Rieffel, Davide Venturelli, Fedir Vasko, Zhihui Wang:
A NASA perspective on quantum computing: Opportunities and challenges. Parallel Comput. 64: 81-98 (2017) - [i4]Alejandro Perdomo-Ortiz, Marcello Benedetti, John Realpe-Gómez, Rupak Biswas:
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. CoRR abs/1708.09757 (2017) - [i3]Marcello Benedetti, John Realpe-Gómez, Alejandro Perdomo-Ortiz:
Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices. CoRR abs/1708.09784 (2017) - 2016
- [i2]Marcello Benedetti, John Realpe-Gomez, Rupak Biswas, Alejandro Perdomo-Ortiz:
Quantum-assisted learning of graphical models with arbitrary pairwise connectivity. CoRR abs/1609.02542 (2016) - 2014
- [i1]Bryan O'Gorman, Alejandro Perdomo-Ortiz, Ryan Babbush, Alán Aspuru-Guzik, Vadim Smelyanskiy:
Bayesian Network Structure Learning Using Quantum Annealing. CoRR abs/1407.3897 (2014) - 2011
- [j1]Alejandro Perdomo-Ortiz, Salvador E. Venegas-Andraca, Alán Aspuru-Guzik:
A study of heuristic guesses for adiabatic quantum computation. Quantum Inf. Process. 10(1): 33-52 (2011)
Coauthor Index
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