Abstract In the realm of quantum batteries (QBs), model construction and performance optimization are central tasks which can be addressed by exploiting machine learning algorithms. Here, we propose a cavity-Heisenberg spin chain 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. By employing a reinforcement learning (RL) algorithm to modulate the cavity-battery coupling, we further optimize the QB performance, enhancing the charging capability of the spin chain. It is shown that the charging energy and the power of the QB are significantly improved with the spin size. In particular, the use of a RL algorithm in case of large spin ( j = 3 / 2 ) in presence of cavity losses allows for more stability in the optimization of the cavity-spin coupling strength, which in perspective makes an experimental realization more feasible. We analyze the optimization mechanism and find an intrinsic relationship between cavity-spin entanglement and charging performance: while in the closed-system scenario the charging energy increases together with the cavity-spin entanglement, in the open-system scenario the increase of the charging energy can be accompanied by a decrease of entanglement. Our results provide a possible scheme for design and optimization of QBs.
Abstract In the realm of quantum batteries (QBs), model construction and performance optimization are central tasks which can be addressed by exploiting machine learning algorithms. Here, we propose a cavity-Heisenberg spin chain 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. By employing a reinforcement learning (RL) algorithm to modulate the cavity-battery coupling, we further optimize the QB performance, enhancing the charging capability of the spin chain. It is shown that the charging energy and the power of the QB are significantly improved with the spin size. In particular, the use of a RL algorithm in case of large spin ( j = 3 / 2 ) in presence of cavity losses allows for more stability in the optimization of the cavity-spin coupling strength, which in perspective makes an experimental realization more feasible. We analyze the optimization mechanism and find an intrinsic relationship between cavity-spin entanglement and charging performance: while in the closed-system scenario the charging energy increases together with the cavity-spin entanglement, in the open-system scenario the increase of the charging energy can be accompanied by a decrease of entanglement. Our results provide a possible scheme for design and optimization of QBs.
We propose a cavity Heisenberg-spin-chain (CHS) quantum battery (QB) with the long-range interactions and investigate its charging process. The performance of the CHS QB is substantially improved compared to the Heisenberg spin chain (HS) QB. When the number of spins $N\ensuremath{\gg}1$, the quantum advantage $\ensuremath{\alpha}$ of the QB's maximum charging power can be obtained, which approximately satisfies the superlinear scaling relation ${P}_{\mathrm{max}}\ensuremath{\propto}{N}^{\ensuremath{\alpha}}$. The CHS QB can approach $\ensuremath{\alpha}=2$ by optimizing the parameters. We find that the maximum stored energy of the CHS QB has a critical phenomenon. By analyzing the Wigner function, von Neumann entropy, and logarithmic negativity, we demonstrate that entanglement can be a necessary ingredient for QB to store more energy, but not sufficient.
Quantum batteries are energy-storing devices, governed by quantum mechanics, that promise high charging performance thanks to collective effects. Because of its experimental feasibility, the Dicke battery-which comprises N two-level systems coupled to a common photon mode-is one of the most promising designs for quantum batteries. However, the chaotic nature of the model severely hinders the extractable energy (ergotropy). Here, we use reinforcement learning to optimize the charging process of a Dicke battery either by modulating the coupling strength, or the system-cavity detuning. We find that the ergotropy and quantum mechanical energy fluctuations (charging precision) can be greatly improved with respect to standard charging strategies by countering the detrimental effect of quantum chaos. Notably, the collective speedup of the charging time can be preserved even when nearly fully charging the battery.
Although entanglement is considered as a crucial resource for quantum information processing, it is still unknown whether the presence of quantum entanglement in the working medium can enhance the charging performance of a quantum battery (QB). Here, we address this problem by considering a QB with working medium consisted of an entangled coupled-cavity array. We show that the optimal charging performance of the QB is achieved in the two-cavity case when cavities are initially in a maximum entangled state without cavity couplings. We then extend our discussion to multicavity case. It is demonstrated that the charging power of the QB under an entangled coupled-cavity array can be further enhanced by increasing the number of cavities. Our findings reveal the advantage of an entangled working medium in enhancing the charging performance of a QB, and therefore, contribute to the experimental realization of quantum batteries with excellent performance.
Abstract This work investigates the energy storage properties of quantum spin chains in the context of quantum batteries (QBs) by introducing Heisenberg spin network models organized into different configurations: open, closed, supercube geometries, and c -regular graphs. The charging dynamics of these systems are examined using Hamiltonians that include contributions from the battery, spin-spin interactions, and a transverse magnetic field. Incorporating the Dzyaloshinskii–Moriya interaction (DMI) into the charging Hamiltonian is found to enhance the ergotropy in the XXZ model, particularly for the supercube configuration, thereby improving QB performance. To explore the role of structural variations, we extend our study to c -regular graphs with system sizes ranging from 3 to 12 qubits, including highly symmetric geometries such as the tetrahedron, octahedron, and icosahedron. These analyzes reveal that such symmetric structures retain ideal sinusoidal charging–discharging behavior when DMI is tuned appropriately, establishing symmetry and coordination as key principles for scalable QB architectures.
Kitaev Quantum Batteries: Super-Extensive Scaling of Ergotropy in 1D Spin−1/2XY−Γ(γ) Chain
We investigate the performance of a novel model based on a one-dimensional (1D) spin-1/2 Heisenberg XY−Γ(γ) quantum chain, also known as 1D Kitaev chain, as a working medium for a quantum battery (QB) in both closed and open system scenarios. We analyze the closed QB scenario by analytically evaluating ergotropy across different spin-spin couplings, anisotropies in spin interactions, Zeeman field strengths, charging field intensities, Γ interactions, and temperature. Our results indicate that the ergotropy is highly dependent on spin-spin coupling and anisotropy. Under variable parameters, an increase in the spin-spin coupling strength displays quenches and exhibits non-equilibrium trends in ergotropy. After a quench, ergotropy may experience a sharp increase or drop, suggesting optimal operational conditions for QB performance. In the open QB scenario, we examine spin chains of sizes 2≤N≤8 under the influence of dephasing, focusing on the evolution of ergotropy. We study two charging schemes: parallel charging, where spins are non-interacting, and collective charging, involving spin-spin coupling. In the former, increased Zeeman field strength enhances both the peak ergotropy and charging rate, although without any quantum advantage or super-extensive scaling. In the latter, increasing spin-spin coupling might not achieve super-extensive scaling without introducing anisotropy in the spin-spin interaction. Our results suggest that optimal QB performance and a quantum advantage in scaling can be achieved by leveraging anisotropic spin-spin couplings and non-zero Γ interactions, allowing for faster charging and higher ergotropy under super-extensive scaling conditions up to α=1.24 for the given size of the spin chain.
The performance of open quantum batteries (QBs) is severely limited by decoherence due to the interaction with the surrounding environment. So, protecting the charging processes against decoherence is of great importance for realizing QBs. In this work we address this issue by developing a charging process of a qubit-based open QB composed of a qubit-battery and a qubit-charger, where each qubit moves inside an independent cavity reservoir. Our results show that, in both the Markovian and non-Markovian dynamics, the charging characteristics, including the charging energy, efficiency and ergotropy, regularly increase with increasing the speed of charger and battery qubits. Interestingly, when the charger and battery move with higher velocities, the initial energy of the charger is completely transferred to the battery in the Markovian dynamics. In this situation, it is possible to extract the total stored energy as work for a long time. Our findings show that open moving-qubit systems are robust and reliable QBs, thus making them a promising candidate for experimental implementations.