Quantum neural networks for power flow analysis

Journal Article (2024)
Author(s)

Zeynab Kaseb (TU Delft - Intelligent Electrical Power Grids)

Matthias Möller (TU Delft - Numerical Analysis)

G.B.L. Tosti Balducci (TU Delft - Group De Breuker)

Peter Palensky (TU Delft - Electrical Sustainable Energy)

P.P. Vergara Barrios (TU Delft - Intelligent Electrical Power Grids)

Research Group
Intelligent Electrical Power Grids
DOI related publication
https://doi.org/10.1016/j.epsr.2024.110677
More Info
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Publication Year
2024
Language
English
Research Group
Intelligent Electrical Power Grids
Volume number
235
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Abstract

This paper explores the potential application of quantum and hybrid quantum–classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum–classical, and classical neural networks. The comparison is based on (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) training error, and (v) training process stability. The results show that the developed hybrid quantum–classical neural network outperforms both quantum and classical neural networks, and hence can improve deep learning-based power flow analysis in the noisy-intermediate-scale quantum (NISQ) and fault-tolerant quantum (FTQ) era.