Quantum Batteries Leverage Kurtosis Control and Noise-induced Coherence for Ergotropy Prediction Via Machine Learning

Quantum batteries represent a potentially revolutionary approach to energy storage, and a team led by Bitap Raj Thakuria, Trishna Kalita, and Manash Jyoti Sarmah from Gauhati University, alongside Himangshu Prabal Goswami et al., now demonstrates a crucial link between internal quantum fluctuations and a battery’s ability to deliver power. The researchers reveal that simply measuring the average flow of energy is insufficient to assess performance, and instead, the shape of the fluctuations, specifically, the kurtosis of quanta exchange within the battery and the coherence arising from noise in its leakage channel, accurately predicts how much power the battery can provide. This discovery establishes a new framework for optimising quantum battery design, moving beyond conventional parameters to harness the subtle interplay between quantum coherence and fluctuations for significantly improved energy storage capabilities. The team’s findings offer a pathway towards building quantum batteries that can be reliably controlled and efficiently deliver power on demand.

Quantum Battery Performance via Machine Learning

This research details investigations into quantum batteries, focusing on enhancing their performance and understanding their behaviour through a combination of theoretical physics and machine learning techniques. The study explores how quantum effects like coherence and entanglement can potentially improve charging power, efficiency, and storage capacity compared to classical batteries. Researchers delve into the role of noise and fluctuations in quantum battery performance, and how these can be harnessed or mitigated. The research utilizes full counting statistics, a powerful tool for analysing fluctuations in energy transfer and work extraction, providing insights beyond average values.

The cumulant generating function and large deviation theory are also employed to characterise statistical properties and understand rare events. Coherence and entanglement are investigated as resources to enhance battery performance, with particular attention paid to noise-induced coherence, exploring how noise can create coherence and boost battery power. Machine learning models, including deep neural networks and random forests, are used to predict and optimise battery charging protocols, maximising power and efficiency. Machine learning algorithms decode complex relationships between battery parameters, environmental factors, and performance metrics, identifying hidden patterns and correlations.

Tabular foundation models improve prediction accuracy and generalisation. The research also addresses data leakage in machine learning models, ensuring reliable predictions. The research demonstrates that, under certain conditions, noise can increase the power output of a quantum battery by inducing coherence, confirming its crucial role in enhancing performance. Machine learning algorithms successfully identify charging protocols that maximise battery power and efficiency, effectively decoding the complex relationships between battery parameters and performance metrics. This multidisciplinary effort combines theoretical physics with the predictive capabilities of machine learning, suggesting that quantum batteries have the potential to outperform classical batteries.

Kurtosis and Coherence Control Quantum Battery Performance

Scientists have demonstrated a novel quantum battery design utilising a cavity-coupled finite system capable of storing energy by harnessing noise-induced coherences. The research focuses on understanding how fluctuations in energy exchange impact the battery’s performance and its ability to deliver work, employing full counting statistics to capture higher-order fluctuations. Experiments reveal that traditional quantum and thermodynamic variables are inadequate for accurately identifying regimes of high ergotropy; instead, the kurtosis of quanta exchange in the storage and the noise-induced coherence in the leakage mode emerge as dominant quantities controlling performance. The team identified a minimal predictive feature set from the battery’s operating parameters, enabling accurate classification of ergotropy into different regimes.

Further investigation demonstrates the importance of a cavity-mediated coherent channel connecting the storage subspace, which acts as a stabiliser by imposing a controllable interaction between charging and storage stations. This design minimises backflow and increases extractable work. The research establishes a rigorous methodology combining full counting statistics with machine learning algorithms to optimise quantum batteries under realistic, non-equilibrium conditions. The team successfully generated synthetic datasets, allowing machine learning models to identify optimal operation regimes and reveal correlations between fluctuations, coherences, and work extraction.

Coherence and Kurtosis Control Quantum Battery Performance

This research introduces a novel quantum battery design based on a finite quantum system coupled to a cavity, where both population and coherence play crucial roles. The team demonstrates that incorporating coherence, arising from asymmetric coupling with noisy stations, significantly alters the battery’s charging, storage, leakage, and ultimately, its ergotropy. A key finding is that conventional parameters alone are insufficient for accurately identifying high-ergotropy regimes; instead, the kurtosis of quanta exchange within the storage component and noise-induced coherence in the leakage mode become dominant factors controlling performance. By integrating full counting statistics with machine learning techniques, the researchers developed a predictive framework for ergotropy, identifying a minimal set of features from the battery’s operating parameters. This approach allows for accurate classification of ergotropy into different regimes, even with limited data. The research highlights the importance of higher-order fluctuations in understanding and optimising quantum battery performance under realistic, open conditions.