With the widespread integration of high-penetration renewable energy into distribution networks, the volatility and uncertainty of system operation have increased significantly, posing substantial challenges to traditional reactive voltage-power optimization. Existing methods based on mathematical programming or intelligent optimization generally suffer from low computational efficiency and a tendency to converge to local optima when dealing with high-dimensional non-convex optimization problems. Digital Twin (DT) technology offers a new approach for real-time grid perception and dynamic simulation; however, its decision-making core still relies on classical algorithms, which limit its analytical capability. Quantum Computing (QC), due to its inherent parallelism, provides revolutionary computational potential for solving complex combinatorial optimization problems. We innovatively integrate QC and DT technologies to construct a Quantum Computing-enhanced Digital Twin System (DTS) (QC-enhanced DTS). By establishing a high-fidelity virtual mapping and designing a hybrid quantum-classical algorithm, the system enables advanced predictive inference and cooperative optimization of distribution network operation. Comparative simulation experiments verify that the proposed method outperforms conventional algorithms by more than 24.3% in both convergence speed and global optimization capability, which effectively reduces system network losses and enhances voltage stability. Therefore, this research not only presents a significant exploration into the practical application of QC but also provides a novel direction for building highly resilient smart grids, offering considerable theoretical value and engineering application prospects.
Jiang et al. (Sun,) studied this question.