Quantum advantage in learning from experiments.


Journal

Science (New York, N.Y.)
ISSN: 1095-9203
Titre abrégé: Science
Pays: United States
ID NLM: 0404511

Informations de publication

Date de publication:
10 06 2022
Historique:
entrez: 9 6 2022
pubmed: 10 6 2022
medline: 10 6 2022
Statut: ppublish

Résumé

Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today's quantum processors.

Identifiants

pubmed: 35679419
doi: 10.1126/science.abn7293
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

1182-1186

Commentaires et corrections

Type : CommentIn

Auteurs

Hsin-Yuan Huang (HY)

Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA.
Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA.

Michael Broughton (M)

Google Quantum AI, Venice, CA 90291, USA.

Jordan Cotler (J)

Harvard Society of Fellows, Cambridge, MA 02138, USA.
Black Hole Initiative, Cambridge, MA 02138, USA.

Sitan Chen (S)

Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, USA.
Simons Institute for the Theory of Computing, Berkeley, CA, USA.

Jerry Li (J)

Microsoft Research AI, Redmond, WA 98052, USA.

Masoud Mohseni (M)

Google Quantum AI, Venice, CA 90291, USA.

Hartmut Neven (H)

Google Quantum AI, Venice, CA 90291, USA.

Ryan Babbush (R)

Google Quantum AI, Venice, CA 90291, USA.

Richard Kueng (R)

Institute for Integrated Circuits, Johannes Kepler University Linz, Austria.

John Preskill (J)

Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA.
Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA.
AWS Center for Quantum Computing, Pasadena, CA 91125, USA.

Jarrod R McClean (JR)

Google Quantum AI, Venice, CA 90291, USA.

Classifications MeSH