Enhanced Distributed VQE for Large-Scale MaxCut
Research Paper#Quantum Computing, Optimization🔬 Research|Analyzed: Jan 3, 2026 20:14•
Published: Dec 26, 2025 15:20
•1 min read
•ArXivAnalysis
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 / Citation
View Original"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."