# Noise-Resilient Quantum Power Flow

arXiv: https://arxiv.org/abs/2211.10555 · HTML: https://mlsanity.com/paper/2211.10555/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2022.

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

- [Quantum Neural Networks for Power Flow Analysis](https://mlsanity.com/paper/2311.06293/) (2023)

## Abstract (arXiv)

Quantum power flow (QPF) provides inspiring directions for tackling power flow's computational burdens leveraging quantum computing. However, existing QPF methods are mainly based on noise-sensitive quantum algorithms, whose practical utilization is significantly hindered by the limited capability of today's noisy-intermediate-scale quantum (NISQ) devices. This paper devises a NISQ-QPF algorithm, which enables power flow calculation on noisy quantum computers. The main contributions include: (1) a variational quantum circuit (VQC)-based AC power flow formulation, which enables QPF using short-depth quantum circuits; (2) noise-resilient QPF solvers based on the variational quantum linear solver (VQLS) and modified fast decoupled power flow; (3) a practical NISQ-QPF framework for implementable and reliable power flow analysis on noisy quantum machines. Promising case studies validate the effectiveness and accuracy of NISQ-QPF on IBM's real, noisy quantum devices.