arXiv subjects

Peng-Yu Sun

Publications and source records attributed to Peng-Yu Sun.

3 recordsLinked to original sources

arXiv2026

Quantum advantage of nonlinear quantum battery and superconducting circuit implementation

A quantum battery is a novel energy storage device that operates on the principles of quantum mechanics. To enhance the charging performance of quantum batteries and further provide theoretical support for their physical implementation, we constructed an optical-field-dependent nonlinear quantum battery model. Meanwhile, we solved for the unbiased form of nonlinear interactions in this model, where the charging power of the proposed model exhibits a superlinear quantum advantage, and the charging time saturates the quantum speed limit. Through theoretical analysis, we confirm that this quantum advantage arises from the quantum effect of multiphoton absorption. Subsequently, with the derived nonlinear function form, we further investigated other properties of this nonlinear quantum battery. Finally, an experimental design scheme for this nonlinear quantum battery in superconducting quantum circuits is presented.

quant-ph↗

arXiv2026

Proposed mixing between $2P$ and $1F$ wave charmonia

We investigate $2P$-$1F$ mixing in charmonium, focusing on the close-in-mass $\chi_{c2}(2P)$ and $\chi_{c2}(1F)$ states. The conventional tensor force yields negligible mixing, motivating the inclusion of coupled-channel effects. Our unquenched calculation reveals sizable mixing angles of $7.5^\circ$ and $15.4^\circ$. We predict the corresponding two-photon and two-gluon decay widths as key observables for experimental verification. Additionally, we discuss the production of these two $2P$-$1F$ mixed states of charmonium via $\gamma\gamma$ fusion. Current data are insufficient to determine the mixing, highlighting the need for precise future measurements to resolve this aspect of charmonium spectroscopy.

hep-ph↗

arXiv2024

Cavity-Heisenberg spin-$j$ chain quantum battery and reinforcement learning optimization

Machine learning offers a promising methodology to tackle complex challenges in quantum physics. In the realm of quantum batteries (QBs), model construction and performance optimization are central tasks. Here, we propose a cavity-Heisenberg spin chain quantum battery (QB) model with spin-$j (j=1/2,1,3/2)$ and investigate the charging performance under both closed and open quantum cases, considering spin-spin interactions, ambient temperature, and cavity dissipation. It is shown that the charging energy and power of QB are significantly improved with the spin size. By employing a reinforcement learning algorithm to modulate the cavity-battery coupling, we further optimize the QB performance, enabling the stored energy to approach, even exceed its upper bound in the absence of spin-spin interaction. We analyze the optimization mechanism and find an intrinsic relationship between cavity-spin entanglement and charging performance: increased entanglement enhances the charging energy in closed systems, whereas the opposite effect occurs in open systems. Our results provide a possible scheme for design and optimization of QBs.

quant-ph↗