A study of heuristic guesses for adiabatic quantum computation. - Alejandro Perdomo-Ortiz; Salvador E. Venegas-Andraca; Alán Aspuru-Guzik (Quantum Inf. Process., 2011)

Bayesian Network Structure Learning Using Quantum Annealing. - Bryan O'Gorman; Alejandro Perdomo-Ortiz; Ryan Babbush; Alán Aspuru-Guzik; Vadim Smelyanskiy (CoRR, 2014)

Quantum-assisted learning of graphical models with arbitrary pairwise connectivity. - Marcello Benedetti; John Realpe-Gomez; Rupak Biswas; Alejandro Perdomo-Ortiz (CoRR, 2016)

Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. - Alejandro Perdomo-Ortiz; Marcello Benedetti; John Realpe-Gómez; Rupak Biswas (CoRR, 2017)

Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices. - Marcello Benedetti; John Realpe-Gómez; Alejandro Perdomo-Ortiz (CoRR, 2017)

A NASA perspective on quantum computing: Opportunities and challenges. - 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; et al. (Parallel Comput., 2017)

Classical versus quantum models in machine learning: insights from a finance application. - Javier Alcazar; Vicente Leyton-Ortega; Alejandro Perdomo-Ortiz (Mach. Learn. Sci. Technol., 2020)

Entanglement types for two-qubit states with real amplitudes. - Oscar J. Perdomo; Vicente Leyton-Ortega; Alejandro Perdomo-Ortiz (Quantum Inf. Process., 2021)

Robust implementation of generative modeling with parametrized quantum circuits. - Vicente Leyton-Ortega; Alejandro Perdomo-Ortiz; Oscar J. Perdomo (Quantum Mach. Intell., 2021)

Evaluating Generalization in Classical and Quantum Generative Models. - Kaitlin Gili; Marta Mauri; Alejandro Perdomo-Ortiz (CoRR, 2022)

Do Quantum Circuit Born Machines Generalize? - Kaitlin Gili; Mohamed Hibat-Allah; Marta Mauri; Chris Ballance; Alejandro Perdomo-Ortiz (CoRR, 2022)

A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models. - Mohamed Hibat-Allah; Marta Mauri; Juan Carrasquilla; Alejandro Perdomo-Ortiz (CoRR, 2023)

Generative Learning of Continuous Data by Tensor Networks. - Alex Meiburg; Jing Chen; Jacob Miller 003; Raphaëlle Tihon; Guillaume Rabusseau; Alejandro Perdomo-Ortiz (CoRR, 2023)

Neural network enhanced measurement efficiency for molecular groundstates. - Dmitri Iouchtchenko; Jérôme F. Gonthier; Alejandro Perdomo-Ortiz; Roger G. Melko (Mach. Learn. Sci. Technol., 2023)

Symmetric tensor networks for generative modeling and constrained combinatorial optimization. - Javier Lopez-Piqueres; Jing Chen 011; Alejandro Perdomo-Ortiz (Mach. Learn. Sci. Technol., 2023)