A
Alejandro Perdomo-Ortiz
Researcher at University College London
Publications - 62
Citations - 3387
Alejandro Perdomo-Ortiz is an academic researcher from University College London. The author has contributed to research in topics: Quantum computer & Quantum. The author has an hindex of 24, co-authored 53 publications receiving 2647 citations. Previous affiliations of Alejandro Perdomo-Ortiz include Harvard University & Ames Research Center.
Papers
More filters
Journal ArticleDOI
Finding Low-Energy Conformations of Lattice Protein Models by Quantum Annealing
TL;DR: This report presents a benchmark implementation of quantum annealing for lattice protein folding problems (six different experiments up to 81 superconducting quantum bits) and paves the way towards studying optimization problems in biophysics and statistical mechanics using quantum devices.
Journal ArticleDOI
Simulating Chemistry Using Quantum Computers
TL;DR: This review discusses to what extent the ideas in quantum computation, now a well-established field, have been applied to chemical problems and describes algorithms that achieve significant advantages for the electronic-structure problem, the simulation of chemical dynamics, protein folding, and other tasks.
Journal ArticleDOI
Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
TL;DR: A systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures.
Journal ArticleDOI
Training of quantum circuits on a hybrid quantum computer
Daiwei Zhu,Norbert M. Linke,Marcello Benedetti,K. A. Landsman,Nhung H. Nguyen,Cinthia Huerta Alderete,Alejandro Perdomo-Ortiz,N. Korda,A. Garfoot,C. Brecque,Laird Egan,Oscar Perdomo,Christopher Monroe +12 more
TL;DR: In this article, a data-driven quantum circuit training algorithm was implemented on the canonical Bars-and-Stripes dataset using a quantum-classical hybrid machine. And they showed that the convergence of the quantum circuit to the target distribution depends critically on both the quantum hardware and classical optimization strategy.
Posted Content
Finding low-energy conformations of lattice protein models by quantum annealing
TL;DR: In this article, the first implementation of lattice protein folding on a quantum device under the Miyazawa-Jernigan model is presented, which paves the way towards studying optimization problems in biophysics and statistical mechanics using quantum devices.