Simulating Work Extraction in a Dinuclear Quantum Battery Using a Variational Quantum Algorithm

TL;DR

This paper demonstrates how variational quantum algorithms can simulate work extraction in a dinuclear quantum battery, highlighting the effects of noise and temperature on energy storage and protocol accuracy.

Contribution

It introduces a quantum computational approach to model and analyze work extraction in quantum batteries, emphasizing the impact of noise and temperature on performance.

Findings

Variational quantum algorithms replicate experimental trends.

Noise reduces the accuracy of energy evaluation.

Protocol is highly precise only at low temperatures.

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.