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Reforming Quantum Microgrid Formation

Chaofan Lin, Peng Zhang, Mikhail A. Bragin, Yacov A. Shamash

TL;DR

The paper tackles the combinatorial bottleneck of classical microgrid formation by reformulating MGF as a compact, lossless QUBO using a graph-theoretic node-to-branch (N2B) mapping. By avoiding continuous-variable discretization and introducing radial topology and N2B constraints, qMGF achieves MILP-equivalent accuracy with substantially fewer qubits, making it more suitable for near-term quantum hardware. The approach is validated against a modified IEEE 37-node feeder, showing strong accuracy advantages over discretization-based QUBO methods, and is demonstrated on a real quantum processing unit, highlighting both potential and current hardware limitations. The work offers a platform-independent quantum optimization framework with practical implications for resilient power systems on noisy intermediate-scale quantum computers, and points to future acceleration for large-scale deployments with inverter-based resources.

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.

Reforming Quantum Microgrid Formation

TL;DR

The paper tackles the combinatorial bottleneck of classical microgrid formation by reformulating MGF as a compact, lossless QUBO using a graph-theoretic node-to-branch (N2B) mapping. By avoiding continuous-variable discretization and introducing radial topology and N2B constraints, qMGF achieves MILP-equivalent accuracy with substantially fewer qubits, making it more suitable for near-term quantum hardware. The approach is validated against a modified IEEE 37-node feeder, showing strong accuracy advantages over discretization-based QUBO methods, and is demonstrated on a real quantum processing unit, highlighting both potential and current hardware limitations. The work offers a platform-independent quantum optimization framework with practical implications for resilient power systems on noisy intermediate-scale quantum computers, and points to future acceleration for large-scale deployments with inverter-based resources.

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.
Paper Structure (7 sections, 19 equations, 2 figures, 1 table, 1 algorithm)

This paper contains 7 sections, 19 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. State-of-the-Practice Quantum Optimization
  3. A Compact and Lossless QUBO Formulation for qMGF
  4. Case Study
  5. Accuracy Advantage
  6. Performance on Real QPU
  7. Conclusion

Figures (2)

  • Figure 1: The distributions of energy values of 300 samples and the probabilities of the ground state for topology optimization in different system scales.
  • Figure 2: The distributions of energy values of 300 samples for restoration optimization at different topologies of the 4 node system.