File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Quantum-enhanced learning of rotations about an unknown direction

TitleQuantum-enhanced learning of rotations about an unknown direction
Authors
KeywordsMatrix product states
Quantum data compression
Quantum machine learning
Quantum many-body systems
Tensor networks
Issue Date2019
PublisherIOP Publishing: Open Access Journals. The Journal's web site is located at http://iopscience.iop.org/1367-2630/
Citation
New Journal of Physics, 2019, v. 21 n. 11, p. article no. 113003 How to Cite?
AbstractWe design machines that learn how to rotate a quantum bit about an initially unknown direction, encoded in the state of a spin-j particle. We show that a machine equipped with a quantum memory of $O(mathrm{log}j)$ qubits can outperform all machines with purely classical memory, even if the size of their memory is arbitrarily large. The advantage is present for every finite j and persists as long as the quantum memory is accessed for no more than $O(j)$ times. We establish these results by deriving the ultimate performance achievable with purely classical memories, thus providing a benchmark that can be used to experimentally demonstrate the implementation of quantum-enhanced learning.
Persistent Identifierhttp://hdl.handle.net/10722/284901
ISSN
2023 Impact Factor: 2.8
2023 SCImago Journal Rankings: 1.090
ISI Accession Number ID
Grants

 

DC FieldValueLanguage
dc.contributor.authorMO, Y-
dc.contributor.authorChiribella, G-
dc.date.accessioned2020-08-07T09:04:07Z-
dc.date.available2020-08-07T09:04:07Z-
dc.date.issued2019-
dc.identifier.citationNew Journal of Physics, 2019, v. 21 n. 11, p. article no. 113003-
dc.identifier.issn1367-2630-
dc.identifier.urihttp://hdl.handle.net/10722/284901-
dc.description.abstractWe design machines that learn how to rotate a quantum bit about an initially unknown direction, encoded in the state of a spin-j particle. We show that a machine equipped with a quantum memory of $O(mathrm{log}j)$ qubits can outperform all machines with purely classical memory, even if the size of their memory is arbitrarily large. The advantage is present for every finite j and persists as long as the quantum memory is accessed for no more than $O(j)$ times. We establish these results by deriving the ultimate performance achievable with purely classical memories, thus providing a benchmark that can be used to experimentally demonstrate the implementation of quantum-enhanced learning.-
dc.languageeng-
dc.publisherIOP Publishing: Open Access Journals. The Journal's web site is located at http://iopscience.iop.org/1367-2630/-
dc.relation.ispartofNew Journal of Physics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectMatrix product states-
dc.subjectQuantum data compression-
dc.subjectQuantum machine learning-
dc.subjectQuantum many-body systems-
dc.subjectTensor networks-
dc.titleQuantum-enhanced learning of rotations about an unknown direction-
dc.typeArticle-
dc.identifier.emailChiribella, G: giulio@cs.hku.hk-
dc.identifier.authorityChiribella, G=rp02035-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1088/1367-2630/ab4d9a-
dc.identifier.scopuseid_2-s2.0-85075779404-
dc.identifier.hkuros312268-
dc.identifier.volume21-
dc.identifier.issue11-
dc.identifier.spagearticle no. 113003-
dc.identifier.epagearticle no. 113003-
dc.identifier.isiWOS:000494826200003-
dc.publisher.placeUnited Kingdom-
dc.relation.projectCompressed Quantum Dynamics: Storing, Programming, and Simulating Physical Processes with Minimum-Sized Quantum Systems-
dc.identifier.issnl1367-2630-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats