Research Paper#Quantum Computing, Optimization, QAOA, MaxCut, Barren Plateaus🔬 ResearchAnalyzed: Jan 3, 2026 08:54
QAOA Suffers from Barren Plateaus for Most MaxCut Instances
Published:Dec 31, 2025 03:02
•1 min read
•ArXiv
Analysis
This paper investigates the trainability of the Quantum Approximate Optimization Algorithm (QAOA) for the MaxCut problem. It demonstrates that QAOA suffers from barren plateaus (regions where the loss function is nearly flat) for a vast majority of weighted and unweighted graphs, making training intractable. This is a significant finding because it highlights a fundamental limitation of QAOA for a common optimization problem. The paper provides a new algorithm to analyze the Dynamical Lie Algebra (DLA), a key indicator of trainability, which allows for faster analysis of graph instances. The results suggest that QAOA's performance may be severely limited in practical applications.
Key Takeaways
- •QAOA suffers from barren plateaus for most MaxCut instances, making training difficult.
- •The DLA dimension grows exponentially for a large fraction of graphs.
- •A new algorithm is developed to analyze the DLA, improving computational efficiency.
- •The findings suggest limitations in QAOA's practical applicability for MaxCut.
Reference
“The paper shows that the DLA dimension grows as $Θ(4^n)$ for weighted graphs (with continuous weight distributions) and almost all unweighted graphs, implying barren plateaus.”