Quantum batteries are a promising technology that could surpass their classical counterparts and play a critical role in the advancement of quantum technologies. In this work, we develop a quantum machine learning algorithm to improve the charging power of quantum batteries. To model an N-cell quantum battery, we use a quantum circuit with N quantum bits. During the charging process, we apply a set of quantum gates with trainable parameters to the circuit. Next, we evaluate the power extraction and optimize it using a classical computer. We find a remarkable maximum power when increasing N. Our work has the potential to create sustainable and scalable energy storage technologies in the future
Quantum batteries are a promising technology that could surpass their classical counterparts and play a critical role in the advancement of quantum technologies. In this work, we develop a quantum machine learning algorithm to improve the charging power of quantum batteries. To model an N-cell quantum battery, we use a quantum circuit with N quantum bits. During the charging process, we apply a set of quantum gates with trainable parameters to the circuit. Next, we evaluate the power extraction and optimize it using a classical computer. We find a remarkable maximum power when increasing N. Our work has the potential to create sustainable and scalable energy storage technologies in the future
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.
This research improves batteries using AI and quantum processing. Quantum computing uses quantum physics to quickly search for many solutions to manage large amounts of data. Deep learning, reinforcement learning, and other machine intelligence use massive datasets to uncover patterns and improve algorithms for quantum computing. To test alternative configurations simultaneously, the authors record operating parameters, ambient variables, and battery attributes in a quantum state. They want to utilize reinforcement learning algorithms to improve charging and draining methods so they operate well and can be used in many situations. This research aims to reduce degradation, improve energy efficiency, and extend battery life. Machine intelligence and quantum computation are used to analyze batteries and optimize performance. Bringing together experts from different sectors could help construct strong, environmentally friendly power networks. This modification may affect energy storage technology greatly. The research's findings could impact electric cars, power grid security, and renewable energy.
Given their high power density, low discharge rate, and decreasing cost, rechargeable lithium-ion batteries (LiBs) have found a wide range of applications such as power grid-level storage systems, electric vehicles (EVs), and mobile devices. Developing a framework to accurately model the nonlinear degradation process of LiBs, which is indeed a supervised learning problem, becomes an important research topic. This paper presents a classical-quantum hybrid machine learning approach to capture the LiB degradation model that assesses the loss of battery cell life from the operating profiles. Our work is motivated by recent advances in quantum computers as well as the similarity between neural networks and quantum circuits. Similarly to adjusting weight parameters in conventional neural networks, the parameters of the quantum circuit, namely, the degree of freedom of the qubits, can be tuned to learn a nonlinear function in a supervised learning fashion. As a proof of concept paper, our obtained numerical results with the battery dataset provided by NASA demonstrate the ability of the quantum neural networks in modeling the nonlinear relationship between the degraded capacity and the operating cycles. We also discuss the potential advantage of the quantum approach compared to conventional neural networks in classical computers in dealing with massive data, especially in the context of future penetration of EVs and energy storage.
Lithium-ion batteries (LIBs) are critical in modern energy storage systems, powering everything from portable electronics to electric vehicles. However, optimizing their performance and longevity remains a significant challenge. Quantum simulation has emerged as a promising tool to model the complex electrochemical processes within LIBs, offering insights into charging mechanisms and degradation pathways th'at classical methods struggle to capture. This paper presents a systematic literature review of quantum simulation techniques applied to LIBs, focusing on charging and degradation modeling. We analyze the current state-of-the-art, identify key techniques, and discuss the potential of quantum computing to revolutionize battery research. Our findings highlight the advantages of quantum simulations in capturing quantum mechanical and quantum chemistry effects which are critical for accurate battery modeling. Trends in the literature suggest a move toward algorithm optimization, integration with classical methods, and the development of quantum-inspired techniques.
The increasing complexity of modern power grids, driven by the integration of renewable energy sources, decentralized energy generation, and dynamic load variations, necessitates advanced computational approaches for efficient grid optimization. Conventional algorithms often struggle with the high-dimensionality and computational intensity of power system optimization problems, prompting the exploration of quantum computing as a transformative alternative. Quantum computing, particularly quantum machine learning (QML) and quantum optimization algorithms, has demonstrated significant potential in accelerating grid stability analysis, energy dispatch, and fault detection. This chapter provides a comprehensive investigation into quantum computing applications for power electronics, focusing on quantum-inspired machine learning models and hybrid quantum-classical frameworks for IoT-enabled smart grids. The potential advantages of quantum algorithms, including the Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Grover’s search algorithm, are analyzed in the context of grid optimization, fault detection, and cybersecurity. Additionally, a comparative analysis of classical and quantum machine learning techniques highlights key performance differences and the challenges associated with practical quantum implementations. Despite the promising advancements, several challenges, including quantum hardware limitations, noise susceptibility, and scalability issues, must be addressed for real-world deployment. Future directions emphasize the need for noise-resilient quantum algorithms, efficient hybrid architectures, and quantum-inspired classical methods to bridge the gap between existing computational techniques and next-generation quantum solutions for power grid management.
In the field of quantum thermodynamics, optimizing the performance of quantum battery (QB) under environmental effects remains a primary research interest. However, environmental control remains challenging due to its high-dimensional degrees of freedom, particularly in the non-Markovian regimes. This work investigates the charging dynamics of a QB model subjected to a tunable non-Markovian environment. We show that a controllable non-Markovian setting can be exploited to improve the QB's primary figure of merit. Specifically, we show that by tuning the relative ratio of QB-charger coupling strength and charger-bath coupling strength, the maximum dynamic values of the charging power, energy storage, and work extraction of QB can be significantly increased. Our findings suggest that employing non-Markovian effects in a tunable way offers a promising avenue for enhancing the performance of quantum thermodynamic devices, and sheds new light on the intersection between the reservior engneering and quantum thermodynamics.