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Dissipative Dynamics of Charged Graphene Quantum Batteries

Disha Verma, Indrajith VS, R. Sankaranarayanan

TL;DR

This work analyzes the dissipative dynamics of a Gaussian-driven, four-level graphene quantum battery built from spin–valley degrees of freedom. By comparing amplitude-damping and pure-dephasing channels, it shows that energy loss via amplitude damping can paradoxically stabilize finite ergotropy through population imbalances, whereas dephasing eliminates coherence and ergotropy. It further demonstrates that Markovian versus non-Markovian dissipation, controlled by reservoir memory, can significantly enhance long-time ergotropy through information backflow, with moderate memory yielding the strongest practical gains. The findings highlight coherence and reservoir engineering as practical resources for improving the longevity and performance of graphene-based quantum batteries, offering a pathway toward reservoir-engineered nanoscale energy storage devices.

Abstract

We investigate dissipative dynamics in a graphene-based quantum battery modeled as a four level spin valley system. 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.

Dissipative Dynamics of Charged Graphene Quantum Batteries

TL;DR

This work analyzes the dissipative dynamics of a Gaussian-driven, four-level graphene quantum battery built from spin–valley degrees of freedom. By comparing amplitude-damping and pure-dephasing channels, it shows that energy loss via amplitude damping can paradoxically stabilize finite ergotropy through population imbalances, whereas dephasing eliminates coherence and ergotropy. It further demonstrates that Markovian versus non-Markovian dissipation, controlled by reservoir memory, can significantly enhance long-time ergotropy through information backflow, with moderate memory yielding the strongest practical gains. The findings highlight coherence and reservoir engineering as practical resources for improving the longevity and performance of graphene-based quantum batteries, offering a pathway toward reservoir-engineered nanoscale energy storage devices.

Abstract

We investigate dissipative dynamics in a graphene-based quantum battery modeled as a four level spin valley system. 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.

Paper Structure

This paper contains 12 sections, 20 equations, 3 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Model
  3. Graphene-Based Quantum Battery
  4. Charging Protocol
  5. Observables
  6. Energy and Purity
  7. Ergotropy
  8. Results
  9. Effect of Dissipation Strength
  10. Amplitude Damping and Dephasing
  11. Markovian vs Non-Markovian Dissipation
  12. Conclusion

Figures (3)

  • Figure 1: Time evolution of (a) energy, (b) purity, and (c) ergotropy for different dissipation strengths $\gamma = 0.1$, $0.5$, and $1.0$.
  • Figure 2: Comparison of ergotropy evolution for different dissipation rates $\gamma$ under (a) amplitude damping and (b) dephasing noise models.
  • Figure 3: Comparison of energy and ergotropy evolution for markovian and non markovian (solid for $\beta$ = 0.1, 0.5, 1.0 ) environment ($\gamma_0=0.5$, $\omega=1$).