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arXiv:2512.07906 · Physical Review A

Quantum catalysis-enhanced extract energy in qubit quantum battery

Shun-Cai Zhao

quant-phSubmitted Dec 7, 2025• Announced Dec 9, 2025• 0 citations• Revised Dec 25, 2025 (v2)
v1 · Dec 7, 2025v2 · Dec 25, 2025v3 · Apr 28, 2026v4 · May 29, 2026
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Abstract

What physical mechanism enables quantum catalysis to boost quantum battery (QB) performance in open systems? We investigate an external-field-driven qubit QB coupled to a harmonic oscillator catalyst, revealing a key thermodynamic mechanism: the catalyst induces transient negative heat flow ($J(t)<0$, or energy backflow) into the battery. This backflow actively counters dephasing losses, rapidly pushing the qubit into non-passive states, and results in a drastic enhancement of extractable work (Ergotropy). Leveraging the quantum first law, we precisely quantify this causal link between negative heat flux and QB performance enhancement. Our work uncovers the fundamental role of transient thermodynamic backflow in quantum catalysis, offering a crucial blueprint for high-performance quantum energy storage devices.

Journal reference
Phys. Rev. A 114, 023106(2026)
DOI
10.1103/8yfq-dcvx
Comments
5 pages, 3 figures

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What physical mechanism enables quantum catalysis to boost quantum battery (QB) performance in open systems? We investigate an external-field-driven qubit QB coupled to a harmonic oscillator catalyst, revealing a key thermodynamic mechanism: the catalyst induces transient negative heat flow (J(t)<0J(t)<0J(t)<0, or energy backflow) into the battery. This backflow actively counters dephasing losses, rapidly pushing the qubit into non-passive states, and results in a drastic enhancement of extractable work (Ergotropy). Leveraging the quantum first law, we precisely quantify this causal link between negative heat flux and QB performance enhancement. Our work uncovers the fundamental role of transient thermodynamic backflow in quantum catalysis, offering a crucial blueprint for high-performance quantum energy storage devices.
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We investigate ergotropy dynamics in a graphene-based quantum battery modeled as a four-level spin--valley system under different dissipative environments. The battery is charged via a Gaussian pulse and subsequently evolves under amplitude damping, dephasing, and both Markovian and non-Markovian reservoirs. We find that amplitude damping, while inducing energy loss, can stabilize non-passive steady states with finite ergotropy, whereas pure dephasing suppresses coherence and eliminates work extraction. On the other hand, non-Markovian memory slows ergotropy loss and enables partial recovery through information backflow. These results identify coherence and reservoir memory as essential resources for enhancing the long-time performance of graphene quantum batteries.