Mixed Symmetry States in 92Zr and 94Mo Nuclei

Mixed-symmetry states of 92Zr and 94Mo isotopes are investigated with the use of the collective models, Interacting Boson Model-2 (IBM-2) and Quasiparticle Phonon Model (QPM). The energy spectra and electromagnetic transition rates for these isotopes are calculated. The results of IBM-2 and QPM are compared with available experimental data. We have obtained a satisfactory agreement, and IBM-2 gives a better description. In these isotopes, we observe a few states having a mixed symmetry such as 2+2, 2+3, 3+1, and 1+s. It is found that these isotopes have a vibrational shape corresponding to the U(5) symmetry.

Shape coexistence in $^{76}$Se within the neutron-proton interacting boson model

We have investigated the low-lying energy spectrum and electromagnetic transition strengths in even-even $^{76}$Se using the proton-neutron interacting boson model (IBM-2). The theoretical calculation for the energy levels and $E2$ and $M1$ transition strengths is in good agreement with the experimental data. Especially, the excitation energy and $E2$ transition of $0^+_2$ state, which is intimately associated with shape coexistence, can be well reproduced. The analysis on low-lying states and some key structure indicators indicates that there is a coexistence between spherical shape and $\gamma$-soft shape in $^{76}$Se.

Speeding up quantum dissipative dynamics of open systems with kernel methods

The future forecasting ability of machine learning (ML) makes ML a promising tool for predicting long-time quantum dissipative dynamics of open systems. In this Article, we employ nonparametric machine learning algorithm (kernel ridge regression as a representative of the kernel methods) to study the quantum dissipative dynamics of the widely-used spin-boson model. Our ML model takes short-time dynamics as an input and is used for fast propagation of the long-time dynamics, greatly reducing the computational effort in comparison with the traditional approaches. Presented results show that the ML model performs well in both symmetric and asymmetric spin-boson models. Our approach is not limited to spin-boson model and can be extended to complex systems.