# Quantum Computing for Power Flow Algorithms: Testing on real Quantum Computers

arXiv: https://arxiv.org/abs/2204.14028 · HTML: https://mlsanity.com/paper/2204.14028/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2022.

## Built on by (most cited first, 5 of 5)

- [Quantum Power Flows: From Theory to Practice](https://mlsanity.com/paper/2211.05728/) (2022)
- [Enigma: Application-Layer Privacy for Quantum Optimization on Untrusted Computers](https://mlsanity.com/paper/2311.13546/) (2023)
- [Quantum Neural Networks for Power Flow Analysis](https://mlsanity.com/paper/2311.06293/) (2023)
- [Solving Conic Programs over Sparse Graphs using a Variational Quantum Approach: The Case of the AC Optimal Power Flow](https://mlsanity.com/paper/2509.00341/) (2025)
- [Learning AC Power Flow Solutions using a Data-Dependent Variational Quantum Circuit](https://mlsanity.com/paper/2509.03495/) (2025)

## Abstract (arXiv)

Quantum computing has the potential to solve many computational problems exponentially faster than classical computers. The high shares of renewables and the wide deployment of converter-interfaced resources require new tools that shall drastically accelerate power system computations, including optimization and security assessment, which can benefit from quantum computing. To the best of our knowledge, this is the first paper that goes beyond quantum computing simulations and performs an experimental application of Quantum Computing for power systems on a real quantum computer. We use five different quantum computers, apply the HHL quantum algorithm, and examine the impact of current noisy quantum hardware on the accuracy and speed of an AC power flow algorithm. We perform the same studies on a 3-bus and a 5-bus system with real quantum computers to identify challenges and open research questions related with the scalability of these algorithms.