Variational quantum algorithm for ergotropy estimation in quantum many-body batteries

TL;DR

This paper introduces the VQErgo algorithm for estimating ergotropy in many-body quantum batteries, demonstrating its effectiveness on noisy quantum devices and analyzing the impact of correlations and noise.

Contribution

The paper presents a variational quantum algorithm for ergotropy estimation, tested on NISQ devices, and analyzes how correlations and noise affect its accuracy.

Findings

VQErgo successfully estimates ergotropy in quantum batteries.

Long-range correlations increase the number of ansatz repetitions needed.

The algorithm performs well on IBM quantum hardware despite noise.

Abstract

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 work we simulate the charging process and work extraction of many-body quantum batteries on noisy-intermediate scale quantum (NISQ) devices, and devise the Variational Quantum Ergotropy (VQErgo) algorithm which finds the optimal unitary operation that maximises 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.