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Quantum Optimization
Reforming Quantum Microgrid Formation
arXiv
Authors: Chaofan Lin, Peng Zhang, Mikhail A. Bragin, Yacov A. Shamash
Year
2024
Paper ID
66751
Status
Preprint
Abstract Read
~2 min
Abstract Words
123
Citations
N/A
Abstract
This letter introduces a novel compact and lossless quantum microgrid formation (qMGF) approach to achieve efficient operational optimization of the power system and improvement of resilience. This is achieved through lossless reformulation to ensure that the results are equivalent to those produced by the classical MGF by exploiting graph-theory-empowered quadratic unconstrained binary optimization (QUBO) that avoids the need for redundant encoding of continuous variables. Additionally, the qMGF approach utilizes a compact formulation that requires significantly fewer qubits compared to other quantum methods thereby enabling a high-accuracy and low-complexity deployment of qMGF on near-term quantum computers. Case studies on real quantum processing units (QPUs) empirically demonstrated that qMGF can achieve the same high accuracy as classic results with a significantly reduced number of qubits.
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