ABSTRACT This review classifies qubit platforms from the perspective of quantum battery applications as inorganic, molecular, and organic material families using an energy‐based approach. The systems are compared in terms of energy level tunability, decoherence mechanisms, and environmental sensitivity. Superconducting and silicon spin qubits stand out with their high control accuracy and long decoherence times (on the order of seconds in purified silicon); however, the need for cryogenic operation and fixed energy levels limits them. In contrast, molecular (lanthanide and transition‐metal complexes) and organic (radical/polymer) systems offer atomically precise tunable energy levels through ligand engineering and chemical design and operate under near‐room temperature conditions. The study evaluates qubit platforms not only in terms of information processing but also with battery‐focused metrics such as energy capacity, charge power, ergotropy, storage lifetime, and quantum advantage arising from entanglement and collective interactions. Thus, molecular and organic systems stand out as the most promising candidates for quantum batteries due to their chemical tunability and bulk charging potential; the study presents a materials‐based roadmap combining quantum computing with energy storage.
ABSTRACT This review classifies qubit platforms from the perspective of quantum battery applications as inorganic, molecular, and organic material families using an energy‐based approach. The systems are compared in terms of energy level tunability, decoherence mechanisms, and environmental sensitivity. Superconducting and silicon spin qubits stand out with their high control accuracy and long decoherence times (on the order of seconds in purified silicon); however, the need for cryogenic operation and fixed energy levels limits them. In contrast, molecular (lanthanide and transition‐metal complexes) and organic (radical/polymer) systems offer atomically precise tunable energy levels through ligand engineering and chemical design and operate under near‐room temperature conditions. The study evaluates qubit platforms not only in terms of information processing but also with battery‐focused metrics such as energy capacity, charge power, ergotropy, storage lifetime, and quantum advantage arising from entanglement and collective interactions. Thus, molecular and organic systems stand out as the most promising candidates for quantum batteries due to their chemical tunability and bulk charging potential; the study presents a materials‐based roadmap combining quantum computing with energy storage.
Quantum computing has progressed greatly, yet no current hardware platform achieves coherence preservation, precise control, and scalability at the same time. In this commentary, we consider molecular qubits as a conceptually distinct approach to quantum hardware, in which part of the required functionality can be encoded directly through chemically programmable structure rather than imposed entirely by external engineering. Owing to their atomic precision, synthetic reproducibility, tunable interactions, and diverse internal energy levels, molecular systems provide attractive opportunities for quantum information science. We review advances in molecular qubits, emphasizing their potential for uniformity, scalability, programmability, and coherence at elevated temperatures. At the same time, major challenges remain, including single-molecule readout, addressability, device integration, and the realization of high-fidelity entangling gates. We argue that the near-term impact of molecular qubits may not directly replace established platforms but instead enable hybrid architectures and promote a new paradigm of quantum hardware: the systematic chemical design of quantum function. In the long term, we may be able to address the challenges outlined in the DiVincenzo criteria and thereby enable universal quantum computation.
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.
Abstract This study explores the energy storage dynamics of a quantum battery (QB) modeled using a dipolar spin system with Dzyaloshinskii–Moriya (DM) interaction. We examine the performance of this system in terms of ergotropy, instantaneous power, capacity, and quantum coherence using a two-qubit model. By solving the system’s time evolution under cyclic unitary processes, we analyze how external parameters such as temperature, magnetic field, and DM interaction influence the charging behavior and quantum resources of the battery. The findings demonstrate that quantum coherence and DM interaction significantly enhance the energy storage efficiency and power output of the quantum battery, offering promising strategies for designing high-performance quantum energy storage devices. Furthermore, we investigate the performance of quantum battery under the influence of a common dephasing environment, which limits the long-term work-extraction capability of dipolar quantum batteries.
We propose and analyze a hybrid qubit-qutrit quantum battery (QB) based on a mixed spin-1/2 and spin-1 system interacting via an anisotropic Heisenberg exchange coupling in the presence of a homogeneous magnetic field. The nonclassical properties of the system are characterized using the l1-norm of coherence and negativity, which quantify quantum coherence and entanglement, respectively. The performance of the quantum battery is evaluated through key indicators such as ergotropy, power, and capacity. Our results reveal that both ergotropy and power exhibit oscillatory dynamics, while the capacity remains constant over time. We further investigate the influence of system parameters and magnetic field strength on both quantum correlations and battery performance, demonstrating that nonclassicality plays a crucial role in enhancing energy-storage efficiency. Importantly, we establish a connection between the theoretical model and an experimentally realizable nickel-radical molecular complex, showing that quantum coherence, entanglement, and efficient energy storage persist even at room temperature. These findings provide a realistic pathway toward the implementation of hybrid qubit-qutrit quantum batteries in solid-state molecular platforms.
Energy storage devices such as lithium-ion batteries and supercapacitors are essential for portable electronics, electric vehicles, and renewable energy systems, yet their long-term reliability is limited by capacity degradation. Accurate prediction of state-of-health (SOH), state-of-charge (SOC), and capacity retention are therefore critical for extending device lifetimes and improving safety. Classical machine learning (ML) methods including LSTMs, CNNs, and ensemble approaches have achieved success in forecasting degradation trends, but they face limitations in scalability, data requirements, and capturing complex electrochemical behaviors. Quantum computing, and in particular quantum machine learning (QML), offers new opportunities by exploiting superposition and entanglement to process information more efficiently and compactly. This review surveys recent advances in applying QML to energy storage, with a focus on CQ approaches (classical data–quantum processing), distinguishing Hybrid-CQ architectures. Comparative analyses highlight trade-offs: CQ and Hybrid-CQ models achieve higher accuracy and parameter efficiency but are constrained by noise, limited qubits, and slower runtimes on real devices. Looking forward, integrating error-mitigation strategies, benchmarking on actual quantum hardware, and embedding physics-informed modeling are critical to closing the gap between theoretical promise and practical deployment. Beyond prediction tasks, quantum approaches hold potential for dynamic charging optimization, materials discovery, and quantum battery concepts. Collectively, these developments underscore how QML can complement and eventually surpass classical ML, paving the way toward more accurate, sustainable, and efficient energy storage systems.
Abstract Finding a quantum battery model that demonstrates a quantum advantage while remaining feasible for experimental production is a considerable challenge. Here, a superconducting quantum battery (SQB) model that exhibits such an advantage is introduced. The model consists of two coupled superconducting qubits that interact during the unitary charging process while remaining in equilibrium with a thermal reservoir. First, the model is described, evidence of the quantum advantage is provided, and then the fabrication process of the battery is discussed using superconducting qubits. Analytical expressions for the ergotropy, instantaneous power, and capacity of the SQB, as well as their connection to quantum coherence are derived. It is demonstrated that leveraging the collective effects of Josephson energies and the coupling energy between qubits allows for optimization, resulting in improved energy redistribution and a significant enhancement in charging efficiency. This work highlights the complexities of tuning system parameters, which increase the potential for work extraction from the SQB, providing a deeper understanding of the charging mechanisms involved. These findings can be applied to superconducting quantum circuit battery architectures, underscoring the feasibility of efficient energy storage in these systems. These results pave the way for proposals of new superconducting devices, emphasizing their potential for efficient energy storage.