Enhanced Distributed VQE for Large-Scale MaxCut
Published:Dec 26, 2025 15:20
•1 min read
•ArXiv
Analysis
This paper presents an improved distributed variational quantum eigensolver (VQE) for solving the MaxCut problem, a computationally hard optimization problem. The key contributions include a hybrid classical-quantum perturbation strategy and a warm-start initialization using the Goemans-Williamson algorithm. The results demonstrate the algorithm's ability to solve MaxCut instances with up to 1000 vertices using only 10 qubits and its superior performance compared to the Goemans-Williamson algorithm. The application to haplotype phasing further validates its practical utility, showcasing its potential for near-term quantum-enhanced combinatorial optimization.
Key Takeaways
- •Proposes an enhanced distributed VQE for the MaxCut problem.
- •Integrates a hybrid classical-quantum perturbation strategy.
- •Employs a warm-start initialization strategy using the Goemans-Williamson algorithm.
- •Demonstrates superior performance compared to the Goemans-Williamson algorithm.
- •Validates practical utility through application to haplotype phasing.
Reference
“The algorithm solves weighted MaxCut instances with up to 1000 vertices using only 10 qubits, and numerical results indicate that it consistently outperforms the Goemans-Williamson algorithm.”