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dc.contributor.author
Domingo, L.
dc.contributor.author
Carlo, Gabriel Gustavo
dc.contributor.author
Borondo, F.
dc.date.available
2024-04-15T12:11:04Z
dc.date.issued
2023-04
dc.identifier.citation
Domingo, L.; Carlo, Gabriel Gustavo; Borondo, F.; Taking advantage of noise in quantum reservoir computing; Nature; Scientific Reports; 13; 1; 4-2023; 1-9
dc.identifier.uri
http://hdl.handle.net/11336/232961
dc.description.abstract
The biggest challenge that quantum computing and quantum machine learning are currently facingis the presence of noise in quantum devices. As a result, big efforts have been put into correctingor mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, wedemonstrate that under some circumstances, quantum noise can be used to improve the performanceof quantum reservoir computing, a prominent and recent quantum machine learning algorithm.Our results show that the amplitude damping noise can be beneficial to machine learning, whilethe depolarizing and phase damping noises should be prioritized for correction. This critical resultsheds new light into the physical mechanisms underlying quantum devices, providing solid practicalprescriptions for a successful implementation of quantum information processing in nowadayshardware.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Nature
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
Reservoir Computing
dc.subject
Quantum Computation
dc.subject
Quantum Machine Learning
dc.subject.classification
Otras Ciencias Físicas
dc.subject.classification
Ciencias Físicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Taking advantage of noise in quantum reservoir computing
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-04-12T12:38:56Z
dc.identifier.eissn
2045-2322
dc.journal.volume
13
dc.journal.number
1
dc.journal.pagination
1-9
dc.journal.pais
Reino Unido
dc.description.fil
Fil: Domingo, L.. Instituto de Ciencias Matemáticas; España. Universidad Autónoma de Madrid; España. Universidad Politécnica de Madrid; España
dc.description.fil
Fil: Carlo, Gabriel Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina
dc.description.fil
Fil: Borondo, F.. Universidad Autónoma de Madrid; España
dc.journal.title
Scientific Reports
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/s41598-023-35461-5
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