Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Zejian Ren"'
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
The detection of the effects of spin symmetry in momentum distribution of an SU(N)-symmetric Fermi gas has remained challenging. Here, the authors use supervised machine learning to connect the spin multiplicity to thermodynamic quantities associated
Externí odkaz:
https://doaj.org/article/66743b77dd114252857f7fdcad5029de
Publikováno v:
Physical Review X, Vol 10, Iss 4, p 041053 (2020)
Blurring the boundary between bosons and fermions lies at the heart of a wide range of intriguing quantum phenomena in multiple disciplines, ranging from condensed matter physics and atomic, molecular, and optical physics to high-energy physics. One
Externí odkaz:
https://doaj.org/article/1f3d2a686cc046dea465f1679700f4e8
Autor:
Chengdong He, Zejian Ren, Bo Song, Entong Zhao, Jeongwon Lee, Yi-Cai Zhang, Shizhong Zhang, Gyu-Boong Jo
Publikováno v:
Physical Review Research, Vol 2, Iss 1, p 012028 (2020)
We measure collective excitations of a harmonically trapped two-dimensional (2D) SU(N) Fermi gas of ^{173}Yb confined to a stack of layers formed by a one-dimensional optical lattice. Quadrupole and breathing modes are excited and monitored in the co
Externí odkaz:
https://doaj.org/article/6fab7f5c7bae476abd4d4d514a029389
Publikováno v:
Nature Physics. 18:385-389
Publikováno v:
Journal of the Korean Physical Society. 79:930-936
Ultra-narrow clock transition of ytterbium atoms has enabled not only quantum metrology but also quantum simulation of various quantum phenomena. One of such examples is a new possibility of realizing dissipative open quantum systems with minimal hea
Publikováno v:
Optics express. 30(21)
Although classifying topological quantum phases have attracted great interests, the absence of local order parameter generically makes it challenging to detect a topological phase transition from experimental data. Recent advances in machine learning
Publikováno v:
Nature Communications
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
The power of machine learning (ML) provides the possibility of analyzing experimental measurements with an unprecedented sensitivity. However, it still remains challenging to probe the subtle effects directly related to physical observables and to un