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arXiv:2502.19331 · Physical review. E

Simulating work extraction in a dinuclear quantum battery using a variational quantum algorithm

Lucas Queiroz Galvão, Ana Clara das Neves, Maron F. Anka, Clebson Cruz

Published May 29, 2025• 2 citations
arXivHTMLPDF

Abstract

Understanding the thermodynamic properties of quantum systems is essential for developing energy-efficient quantum technologies. In this regard, this work explores the application of quantum computational methods to study the quantum properties and work extraction processes in a dinuclear quantum battery model. Our results demonstrate that variational quantum algorithms can reproduce key trends in experimental data, making it possible to analyze the effectiveness of the presented protocol in noisy environments and providing insights into the feasibility of quantum batteries in near-term devices. We have shown that the presence of a noisy environment hinders the accuracy of the evaluation of the amount of energy stored in the system. Additionally, we analyze the work extraction precision, revealing that although the system can store energy at room temperature, the protocol is highly precise only at low temperatures, and its accuracy at ambient conditions remains limited, compromising its usability.

DOI
10.1103/6bm4-8ckl

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Understanding the thermodynamic properties of quantum systems is essential for developing energy-efficient quantum technologies. In this regard, this work explores the application of quantum computational methods to study the quantum properties and work extraction processes in a dinuclear quantum battery model. Our results demonstrate that variational quantum algorithms can reproduce key trends in experimental data, making it possible to analyze the effectiveness of the presented protocol in noisy environments and providing insights into the feasibility of quantum batteries in near-term devices. We have shown that the presence of a noisy environment hinders the accuracy of the evaluation of the amount of energy stored in the system. Additionally, we analyze the work extraction precision, revealing that although the system can store energy at room temperature, the protocol is highly precise only at low temperatures, and its accuracy at ambient conditions remains limited, compromising its usability.
Abstract In this paper, we investigate the use of variational quantum algorithms for simulating the thermodynamic properties of dinuclear metal complexes. Our study highlights the potential of quantum computing to transform advanced simulations and provide insights into the physical behavior of quantum systems. The results demonstrate the effectiveness of variational quantum algorithms in simulating thermal states and exploring the thermodynamic properties of low-dimensional molecular magnetic systems. The findings from this research contribute to broadening our understanding of quantum systems and pave the way for future advancements in materials science through quantum computing.
Abstract {The urgent demand for sustainable energy storage devices necessitates the development of novel conceptual and technological paradigms that extend beyond the constraints imposed by classical thermodynamic limits. in order to present the material innovations required to enable viable room-temperature quantum technologies, this Review outlines the potential of dinuclear Cu(II) complexes as promising model platforms for the development of room-temperature quantum batteries.} We address how the quantum properties of these dinuclear materials can be harnessed to store and extract energy, and how they can be handled and engineered by the management of the intra-molecular magnetic coupling and external thermodynamic conditions. {Through macroscopic magnetometric mappings, we present how the maximum extractable work (ergotropy) can be directly quantified without invasive quantum tomography. Furthermore, we review the operational simulation of these materials using quantum computing approaches using the Variational Quantum Thermalizer (VQT) algorithm on Noisy Intermediate-Scale Quantum (NISQ) devices. Finally, we provide an outlook on future research directions, highlighting the critical material engineering and quantum error mitigation strategies required to transition these coordination compounds from theoretical models to functional macroscopic solid-state quantum energy storage devices at room temperature.}
Quantum batteries are predicted to have the potential to outperform their classical counterparts and are therefore an important element in the development of quantum technologies. Of particular interest is the role of correlations in many-body quantum batteries and how these can affect the maximal work extraction, quantified by the ergotropy. In this paper we simulate the charging process and work extraction of many-body quantum batteries on noisy intermediate-scale quantum devices and devise the variational quantum ergotropy (VQErgo) algorithm, which finds the optimal unitary operation that maximizes work extraction from the battery. We test VQErgo by calculating the ergotropy of a many-body quantum battery undergoing transverse field Ising dynamics following a sudden quench. We investigate the battery for different system sizes and charging times and analyze the minimum number of ansatz circuit repetitions needed for the variational optimization using both ideal and noisy simulators. We also discuss how the growth of long-range correlations can hamper the accuracy of VQErgo in larger systems, requiring increased repetitions of the ansatz circuit to reduce error. Finally, we optimize part of the VQErgo algorithm and calculate the ergotropy on one of IBM's quantum devices. Published by the American Physical Society 2024
One of the most fundamental tasks in quantum thermodynamics is extracting energy from one system and subsequently storing this energy in an appropriate battery. Both of these steps, work extraction and charging, can be viewed as cyclic Hamiltonian processes acting on individual quantum systems. Interestingly, so-called passive states exist, whose energy cannot be lowered by unitary operations, but it is safe to assume that the energy of any not fully charged battery may be increased unitarily. However, unitaries raising the average energy by the same amount may differ in qualities such as their precision, fluctuations, and charging power. Moreover, some unitaries may be extremely difficult to realize in practice. It is hence of crucial importance to understand the qualities that can be expected from practically implementable transformations. Here, we consider the limitations on charging batteries when restricting to the feasibly realizable family of Gaussian unitaries. We derive optimal protocols for general unitary operations as well as for the restriction to easier implementable Gaussian unitaries. We find that practical Gaussian battery charging, while performing significantly less well than is possible in principle, still offers asymptotically vanishing relative charge variances and fluctuations.
We present a theoretical analysis of the energy recovery efficiency for quantum batteries composed of many identical quantum cells undergoing noise. While the possibility of using quantum effects to speed up the charging processes of batteries have been vastly investigated, In order to traslate these ideas into working devices it is crucial to assess the stability of the storage phase in the quantum battery elements when they are in contact with environmental noise. In this work we formalize this problem introducing a series of operationally well defined figures of merit (the work capacitances and the Maximal Asymptotic Work/Energy Ratios) which gauge the highest efficiency one can attain in recovering useful energy from quantum battery models that are formed by large collections of identical and independent elements (quantum cells or q-cells). Explicit evaluations of such quantities are presented for the case where the energy storing system undergoes through dephasing and depolarizing noise.
Quantum computers hold significant promise for the efficient and accurate simulation of strongly correlated materials, including those critical to next-generation battery technologies. However, current quantum hardware is constrained by limited qubit counts and susceptibility to decoherence, making exact simulations of complex materials infeasible. In response, the Noisy Intermediate-Scale Quantum (NISQ) era has fostered the development of hybrid quantum-classical algorithms that can operate effectively within these limitations. In this work, we apply NISQ-compatible methods to investigate fundamental processes occurring inside battery systems, especially in organic electrode materials. A key aspect of our approach is the decomposition of these systems into active spaces - reduced regions that capture the most chemically relevant electrons and interactions. Within these active spaces, quantum algorithms are employed to resolve the electronic structure with high precision. By strategically focusing quantum computational resources where they are most impactful, our approach highlights the potential of quantum computing in advancing the fundamental understanding of battery materials - marking an early but significant step toward practical quantum simulations in energy storage research.