Dephasing enabled fast charging of quantum batteries

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED npj Quantum Information Pub Date : 2025-01-19 DOI:10.1038/s41534-025-00959-5
Rahul Shastri, Chao Jiang, Guo-Hua Xu, B. Prasanna Venkatesh, Gentaro Watanabe
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Abstract

We propose and analyze a universal method to obtain fast charging of a quantum battery by a driven charger system using controlled, pure dephasing of the charger. While the battery displays coherent underdamped oscillations of energy for weak charger dephasing, the quantum Zeno freezing of the charger energy at high dephasing suppresses the rate of transfer of energy to the battery. Choosing an optimum dephasing rate between the regimes leads to a fast charging of the battery. We illustrate our results with the charger and battery modeled by either two-level systems or harmonic oscillators. Apart from the fast charging, the dephasing also renders the charging performance more robust to detuning between the charger, drive, and battery frequencies for the two-level systems case.

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减相使量子电池能够快速充电
我们提出并分析了一种通用的方法来实现量子电池的快速充电的驱动充电器系统,利用控制,纯减相的充电器。当电池在弱充电器减相时显示出能量的相干欠阻尼振荡时,在高减相时充电器能量的量子芝诺冻结抑制了能量转移到电池的速率。在两种状态之间选择最佳的脱相速率可以实现电池的快速充电。我们用两级系统或谐波振荡器建模的充电器和电池来说明我们的结果。除快速充电外,对于两级系统情况下,减相还使充电性能对充电器、驱动器和电池频率之间的失谐更加稳健。
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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
期刊最新文献
Faster-than-Clifford simulations of entanglement purification circuits and their full-stack optimization Fabrication and characterization of low-loss Al/Si/Al parallel plate capacitors for superconducting quantum information applications Long-time error-mitigating simulation of open quantum systems on near term quantum computers Dephasing enabled fast charging of quantum batteries Noise-agnostic quantum error mitigation with data augmented neural models
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