Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2601.12183 · Physical Review A

Maximum precision charging of multi-qubit quantum batteries

Davide Rinaldi, Radim Filip, Dario Gerace, Giacomo Guarnieri

quant-phSubmitted Jan 17, 2026• Announced Jan 20, 2026• 1 citation
v1 · Jan 17, 2026v2 · Sep 2, 2026
arXivHTMLPDF

Abstract

Precision, robustness, and efficiency are crucial aspects in the design of quantum technologies. Here, we show how genuine quantum features, together with non-Gaussianity, can be the key elements to achieve the best of these three aspects during a quantum battery-charging process. Taking inspiration from a light-matter interaction paradigm, i.e., the Jaynes-Cummings model, we employ the Full Counting Statistics to study the stochastic exchanges of energy between an entire stack of qubits and a single-mode electromagnetic field (or mechanical oscillator). Our study allows to conclude that charging the battery through a sequential protocol involving a quantum non-Gaussian field state guarantees extremely high-performances in the charging process, whose precision is maximized even under sub-optimal operating conditions. These results highlight the potential of non-Gaussian quantum state charging to achieve a robust quantum precision advantage over Gaussian states of the field by suppressing detrimental quantum fluctuations, thus making it suitable to ultimate tasks for which a significant degree of accuracy is required.

Journal reference
Phys. Rev. A 114, L030601 (2026)
DOI
10.1103/b19l-sxnt
Comments
Main text: 5 pages, 4 figures. Bibliography: 2 pages. Supplemental material: 10 pages, 6 figures

Related papers

Reliable quantum advantage in quantum battery charging

Davide Rinaldi, Radim Filip, Dario Gerace +1 more

Quantum batteries represent one of the most promising applications of quantum thermodynamics, whose goal is not only to store energy inside small quantum systems but also to potentially leverage genuine quantum effects to outperform classical counterparts. In this context, however, energy fluctuations become extremely relevant and have a significant impact on the charging efficiency. In our work, we consider a simple yet paradigmatic model in which a flying qubit (the battery) coherently interacts with a single mode optical cavity (the charger) through a number conserving Jaynes-Cummings interaction. By making use of full-counting statistics techniques, we fully characterize the average charging power, its fluctuations and the associated charging efficiency for several different choices of initial states of the optical cavity, demonstrating that preparing the latter in a genuinely quantum non-Gaussian Fock state (rather than a classical or even non-classical Gaussian state) leads to a definite and (in principle) measurable advantage in all these figures of merit.

arXiv:2412.15339•Physical Review A•Jun 9, 2025•19 citations

Precision bounds for bosonic quantum batteries

Beatriz Polo, Federico Centrone

We study precision charging in bosonic quantum batteries under a finite-energy constraint, using the signal-to-noise ratio (SNR) of delivered excitations as an operational metric directly tied to the energy measured at a load. At the state level, we derive a classical bound whose violation is equivalent to antibunching and certifies non-classicality, and a Gaussian bound whose violation certifies non-Gaussianity under fixed temperature and energy-input constraints. We identify experimentally accessible non-Gaussian families that surpass this Gaussian bound at finite temperature, thereby establishing non-Gaussianity as a resource for enhanced charging precision. Finally, we introduce a linear photodetection model which, under standard linear-response assumptions, propagates these bounds to the photocurrent level and enables both witnesses to be evaluated solely from electrical statistics. Together, these results provide a realistic route to demonstrating an operational quantum advantage-defined as surpassing classical and Gaussian precision bounds-in a thermodynamically motivated energy-conversion task, with plausible near-term applications to the precision charging of fragile nanoscopic loads.

arXiv:2505.24604•May 30, 2025•0 citations

Finite-Time Protocols Stabilize Charging in Noisy Ising Quantum Batteries

Riccardo Grazi, Henrik Johannesson, Dario Ferraro +1 more

Reliable charging protocols are crucial for advancing quantum batteries toward practical use. We investigate a transverse-field Ising chain as a quantum battery, focusing on the combined role of qubit interactions in the battery model and finite charging time. This interplay yields smoother and more controllable charging compared to sudden protocols or non-interacting batteries. Introducing stochastic noise reveals a strong dependence on the charging trajectory. Protocols that weakly excite the system gain energy under noise but lose extractable work. In contrast, protocols that strongly excite many modes show the opposite trend: noise reduces stored energy yet improves efficiency, defined as the ratio of ergotropy to stored energy. These findings demonstrate that finite-time ramps stabilize charging and highlight that noise can either hinder or enhance quantum-battery performance depending on the protocol.

quant-pharXiv:2512.14521•Advanced Quantum Technologies•Dec 17, 2025•2 citations

Quantum Charging Advantage in Superconducting Solid-State Batteries

Chang-Kang Hu, Chilong Liu, Jingchao Zhao +20 more

Quantum battery, as a novel energy storage device, offers the potential for unprecedented efficiency and performance beyond the capabilities of classical systems, with broad implications for future quantum technologies. Here, we experimentally \RefC{demonstrate quantum charging advantage (QCA)} in a scalable solid-state quantum battery. More specifically, we show how double-excitation Hamiltonians for two-level systems promote scalable QCA \RefB{with standard methods.} We effectively implement the collective evolution of quantum systems with 2 up to 12 battery cells in a superconducting quantum processor, and study the performance of quantum charging compared to its uncorrelated classical counterpart. The model considered is a linear chain of superconducting transmon qubits with only \textit{nearest-neighbor} and \textit{pairwise} interactions, which constitute the simplest model of a multi-cell quantum battery. Our results empirically realize substantial QCA without the necessity of adopting long-range and many-body interactions \RefB{ and showcase the quantum features of the QB charging processes with measurements of non-zero coherent ergotropy, incoherent ergotropy and entanglement,} revealing a promising prospect for further developments of efficient and experimentally feasible protocols for QCA.

quant-pharXiv:2602.08610•Physical Review Letters•Feb 10, 2026•11 citations

High-capacity and high-power collective charging with spin chargers

Yong Huangfu, Jun Jing

Quantum battery works as a micro- or nanodevice to store and redistribute energy at the quantum level. Here we propose a spin-charger protocol, in which the battery cells are charged by a finite number of spins through a general Heisenberg XY interaction. Under the isotropic interaction, the spin-charger protocol is endowed with a higher capacity in terms of the maximum stored energy than the conventional protocols, where the battery is charged by a continuous-variable system, e.g., a cavity mode. By tuning the charger size, a tradeoff between the maximum stored energy and the average charging power is found in comparison to the cavity-charger protocol in the Tavis-Cummings model. Quantum advantage of our protocol is manifested by the scaling behavior of the optimal average power with respect to the battery size, in comparing the collective charging scheme to its parallel counterpart. We also discuss the detrimental effect on the charging performance from the anisotropic interaction between the battery and the charger, the nonideal initial states for both of them, and the crosstalk among the charger spins. A strong charger-charger interaction can be used to decouple the battery and the charger. Our findings about the advantages of the spin-charger protocol over the conventional cavity-charger protocols, including the high capacity of energy storage and the superior power law in the collective charging, provide an insight to exploit an efficient quantum battery based on the spin-spin-environment model.

arXiv:2109.00687•Physical review. E•Aug 23, 2021•39 citations

Multi-Stage Reinforcement Learning for Robust Charging of Quantum Batteries (Student Abstract)

Beomdo Park, Hyeok Jang, Junseong Park +9 more

Quantum batteries have emerged as a next-generation energy storage solution, leveraging quantum phenomena such as superabsorption to overcome the limitations of conventional energy technologies. However, noise arising from interactions with the external environment degrades the charging efficiency and stability of the battery by disrupting the system's quantum coherence. To address this challenge, this study proposes a robust charging framework for a single-qubit quantum battery based on the Jaynes-Cummings (JC) model. The proposed framework combines the Proximal Policy Optimization (PPO) algorithm with a multi-stage reinforcement learning structure. The agent first learns fundamental control principles in a noise-free, ideal environment and subsequently performs robust learning in progressively noisier and more complex settings. Simulation results demonstrate that the trained agent navigates a stable charging trajectory on the Bloch sphere, thereby achieving high ergotropy even in the presence of noise. These findings suggest that multi-stage reinforcement learning is an effective solution for control problems in noisy quantum systems and provides a theoretical foundation for designing charging protocols for multi-qubit systems.

Proceedings of the AAAI Conference on Artificial Intelligence•Mar 14, 2026•0 citations
Precision, robustness, and efficiency are crucial aspects in the design of quantum technologies. Here, we show how genuine quantum features, together with non-Gaussianity, can be the key elements to achieve the best of these three aspects during a quantum battery-charging process. Taking inspiration from a light-matter interaction paradigm, i.e., the Jaynes-Cummings model, we employ the Full Counting Statistics to study the stochastic exchanges of energy between an entire stack of qubits and a single-mode electromagnetic field (or mechanical oscillator). Our study allows to conclude that charging the battery through a sequential protocol involving a quantum non-Gaussian field state guarantees extremely high-performances in the charging process, whose precision is maximized even under sub-optimal operating conditions. These results highlight the potential of non-Gaussian quantum state charging to achieve a robust quantum precision advantage over Gaussian states of the field by suppressing detrimental quantum fluctuations, thus making it suitable to ultimate tasks for which a significant degree of accuracy is required.
Quantum batteries represent one of the most promising applications of quantum thermodynamics, whose goal is not only to store energy inside small quantum systems but also to potentially leverage genuine quantum effects to outperform classical counterparts. In this context, however, energy fluctuations become extremely relevant and have a significant impact on the charging efficiency. In our work, we consider a simple yet paradigmatic model in which a flying qubit (the battery) coherently interacts with a single mode optical cavity (the charger) through a number conserving Jaynes-Cummings interaction. By making use of full-counting statistics techniques, we fully characterize the average charging power, its fluctuations and the associated charging efficiency for several different choices of initial states of the optical cavity, demonstrating that preparing the latter in a genuinely quantum non-Gaussian Fock state (rather than a classical or even non-classical Gaussian state) leads to a definite and (in principle) measurable advantage in all these figures of merit.
We study precision charging in bosonic quantum batteries under a finite-energy constraint, using the signal-to-noise ratio (SNR) of delivered excitations as an operational metric directly tied to the energy measured at a load. At the state level, we derive a classical bound whose violation is equivalent to antibunching and certifies non-classicality, and a Gaussian bound whose violation certifies non-Gaussianity under fixed temperature and energy-input constraints. We identify experimentally accessible non-Gaussian families that surpass this Gaussian bound at finite temperature, thereby establishing non-Gaussianity as a resource for enhanced charging precision. Finally, we introduce a linear photodetection model which, under standard linear-response assumptions, propagates these bounds to the photocurrent level and enables both witnesses to be evaluated solely from electrical statistics. Together, these results provide a realistic route to demonstrating an operational quantum advantage-defined as surpassing classical and Gaussian precision bounds-in a thermodynamically motivated energy-conversion task, with plausible near-term applications to the precision charging of fragile nanoscopic loads.
Reliable charging protocols are crucial for advancing quantum batteries toward practical use. We investigate a transverse-field Ising chain as a quantum battery, focusing on the combined role of qubit interactions in the battery model and finite charging time. This interplay yields smoother and more controllable charging compared to sudden protocols or non-interacting batteries. Introducing stochastic noise reveals a strong dependence on the charging trajectory. Protocols that weakly excite the system gain energy under noise but lose extractable work. In contrast, protocols that strongly excite many modes show the opposite trend: noise reduces stored energy yet improves efficiency, defined as the ratio of ergotropy to stored energy. These findings demonstrate that finite-time ramps stabilize charging and highlight that noise can either hinder or enhance quantum-battery performance depending on the protocol.
Quantum battery, as a novel energy storage device, offers the potential for unprecedented efficiency and performance beyond the capabilities of classical systems, with broad implications for future quantum technologies. Here, we experimentally \RefC{demonstrate quantum charging advantage (QCA)} in a scalable solid-state quantum battery. More specifically, we show how double-excitation Hamiltonians for two-level systems promote scalable QCA \RefB{with standard methods.} We effectively implement the collective evolution of quantum systems with 2 up to 12 battery cells in a superconducting quantum processor, and study the performance of quantum charging compared to its uncorrelated classical counterpart. The model considered is a linear chain of superconducting transmon qubits with only \textit{nearest-neighbor} and \textit{pairwise} interactions, which constitute the simplest model of a multi-cell quantum battery. Our results empirically realize substantial QCA without the necessity of adopting long-range and many-body interactions \RefB{ and showcase the quantum features of the QB charging processes with measurements of non-zero coherent ergotropy, incoherent ergotropy and entanglement,} revealing a promising prospect for further developments of efficient and experimentally feasible protocols for QCA.
Quantum battery works as a micro- or nanodevice to store and redistribute energy at the quantum level. Here we propose a spin-charger protocol, in which the battery cells are charged by a finite number of spins through a general Heisenberg XY interaction. Under the isotropic interaction, the spin-charger protocol is endowed with a higher capacity in terms of the maximum stored energy than the conventional protocols, where the battery is charged by a continuous-variable system, e.g., a cavity mode. By tuning the charger size, a tradeoff between the maximum stored energy and the average charging power is found in comparison to the cavity-charger protocol in the Tavis-Cummings model. Quantum advantage of our protocol is manifested by the scaling behavior of the optimal average power with respect to the battery size, in comparing the collective charging scheme to its parallel counterpart. We also discuss the detrimental effect on the charging performance from the anisotropic interaction between the battery and the charger, the nonideal initial states for both of them, and the crosstalk among the charger spins. A strong charger-charger interaction can be used to decouple the battery and the charger. Our findings about the advantages of the spin-charger protocol over the conventional cavity-charger protocols, including the high capacity of energy storage and the superior power law in the collective charging, provide an insight to exploit an efficient quantum battery based on the spin-spin-environment model.
Quantum batteries have emerged as a next-generation energy storage solution, leveraging quantum phenomena such as superabsorption to overcome the limitations of conventional energy technologies. However, noise arising from interactions with the external environment degrades the charging efficiency and stability of the battery by disrupting the system's quantum coherence. To address this challenge, this study proposes a robust charging framework for a single-qubit quantum battery based on the Jaynes-Cummings (JC) model. The proposed framework combines the Proximal Policy Optimization (PPO) algorithm with a multi-stage reinforcement learning structure. The agent first learns fundamental control principles in a noise-free, ideal environment and subsequently performs robust learning in progressively noisier and more complex settings. Simulation results demonstrate that the trained agent navigates a stable charging trajectory on the Bloch sphere, thereby achieving high ergotropy even in the presence of noise. These findings suggest that multi-stage reinforcement learning is an effective solution for control problems in noisy quantum systems and provides a theoretical foundation for designing charging protocols for multi-qubit systems.