# Evaluate Quantum Combinatorial Optimization for Distribution Network Reconfiguration

arXiv: https://arxiv.org/abs/2208.07513 · HTML: https://mlsanity.com/paper/2208.07513/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2022.

## Built on by (most cited first, 1 of 1)

- [A Quantum Neural Network Regression for Modeling Lithium-ion Battery Capacity Degradation](https://mlsanity.com/paper/2302.02547/) (2023)

## Abstract (arXiv)

This paper aims to implement and evaluate the performance of quantum computing on solving combinatorial optimization problems arising from the operations of the power grid. To this end, we construct a novel mixed integer conic programming formulation for the reconfiguration of radial distribution network in response to faults in distribution lines. Comparing to existing bus injection model in the literature, our formulation based the branch flows model is theoretically equivalent without needing non-explainable variables, thus being more numerically stable. The network reconfiguration model is then used as a benchmark to evaluate the performance of quantum computing algorithms in real quantum computers. It shows that while current quantum computing algorithms with fast execution time in quantum computers can be a promising solution candidate, its heuristic nature stem from its theoretical foundation should be considered carefully when applying into power grid optimization problems.