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Quantum Optimization
Quantum Machine Learning
Noise-Resilient Quantum Power Flow
arXiv
Authors: Fei Feng, Yifan Zhou, Peng Zhang
Year
2022
Paper ID
6686
Status
Preprint
Abstract Read
~2 min
Abstract Words
127
Citations
N/A
Abstract
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2022 reference point for readers tracking recent quantum research.
- Quantum power flow (QPF) provides inspiring directions for tackling power flow's computational burdens leveraging quantum computing.
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