Demonstration of quantum advantage in machine learning

Large advantage in small quantum computers Quantum computing promises to revolutionize all fields of science by solving problems that are too complex for conventional computers. However, the realization of a full-fledged, universal quantum computer is still far ahead, requiring millions of quantum b...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Diego Ristè, Marcus P. da Silva, Colm A. Ryan, Andrew W. Cross, Antonio D. Córcoles, John A. Smolin, Jay M. Gambetta, Jerry M. Chow, Blake R. Johnson
Format: article
Langue:EN
Publié: Nature Portfolio 2017
Sujets:
Accès en ligne:https://doaj.org/article/b5566ab8f0a84a03ad96e12cf550175d
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
Description
Résumé:Large advantage in small quantum computers Quantum computing promises to revolutionize all fields of science by solving problems that are too complex for conventional computers. However, the realization of a full-fledged, universal quantum computer is still far ahead, requiring millions of quantum bits and very low error rates. Despite this, D. Ristè and colleagues at Raytheon BBN Technologies, with collaborators at IBM, have demonstrated that a quantum advantage already appears with only a few quantum bits and a highly noisy system. The team solved a particular problem, known as learning parity with noise, using a five-qubit superconducting quantum processor. Counting the number of times that the processor runs, they demonstrate that the implemented quantum algorithm finds the solution much faster than by classical methods