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| DOI | 10.3390/SYM14122562 | ||||
| Año | 2022 | ||||
| Tipo | artículo de investigación |
Citas Totales
Autores Afiliación Chile
Instituciones Chile
% Participación
Internacional
Autores
Afiliación Extranjera
Instituciones
Extranjeras
Periodically driven quantum many-body systems exhibit novel nonequilibrium states, such as prethermalization, discrete time crystals, and many-body localization. Recently, the general mechanism of fractional resonances has been proposed that leads to slowing the many-body dynamics in systems with both U(1) and parity symmetry. Here, we show that fractional resonance is stable under local noise models. To corroborate our finding, we numerically study the dynamics of a small-scale Bose-Hubbard model that can readily be implemented in existing noisy intermediate-scale quantum (NISQ) devices. Our findings suggest a possible pathway toward a stable nonequilibrium state of matter, with potential applications of quantum memories for quantum information processing.
| Ord. | Autor | Género | Institución - País |
|---|---|---|---|
| 1 | PENA-BERNALES, RUBEN | Hombre |
Universidad de Santiago de Chile - Chile
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| 2 | Kyaw, Thi Ha | Mujer |
LG Elect Toronto Lab - Canadá
LG Electronics Toronto AI Lab - Canadá |
| 3 | ROMERO-HUENCHUNIR, GUILLERMO ESTEBAN | Hombre |
Universidad de Santiago de Chile - Chile
Centro para el Desarrollo de la Nanociencia y la Nanotecnologia - Chile |
| Fuente |
|---|
| Fondo Nacional de Desarrollo Científico y Tecnológico |
| Fondo Nacional de Desarrollo Cientifico y Tecnologico (FONDECYT, Chile) |
| Vicerrectoria de Postgrado USACH |
| Agradecimiento |
|---|
| This research was funded by the Fondo Nacional de Desarrollo Cientifico y Tecnologico (FONDECYT, Chile) grant number 1190727. |
| We thank R. Román-Ancheyta for helpful discussions and for carefully reading our article. R.P. acknowledges the support from Vicerrectoría de Postgrado USACH. G.R acknowledges the support from the Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT, Chile) Grant No. 1190727. We used time-evolving block decimation (TEBD) algorithm [,], from https://github.com/tenpy/tenpy ). |