# Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes

arXiv: https://arxiv.org/abs/2506.01715 · HTML: https://mlsanity.com/paper/2506.01715/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies](https://mlsanity.com/paper/2506.19973/) (2025)
- [Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors](https://mlsanity.com/paper/2604.13951/) (2026)
- [Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution](https://mlsanity.com/paper/2607.29228/) (2026)

## Abstract (arXiv)

Variational Quantum Algorithms (VQAs) are a leading approach for near-term quantum computing but face major optimization challenges from noise, barren plateaus, and complex energy landscapes. We benchmarked more than fifty metaheuristic algorithms for the Variational Quantum Eigensolver (VQE) using a three-phase procedure: initial screening on the Ising model, scaling tests up to nine qubits, and convergence on a 192-parameter Hubbard model. Landscape visualizations revealed that smooth convex basins in noiseless settings become distorted and rugged under finite-shot sampling, explaining the failure of gradient-based local methods. Across models, CMA-ES and iL-SHADE consistently achieved the best performance, while Simulated Annealing (Cauchy), Harmony Search, and Symbiotic Organisms Search also showed robustness. In contrast, widely used optimizers such as PSO, GA, and standard DE variants degraded sharply with noise. These results identify a small set of resilient algorithms for noisy VQE and provide guidance for optimization strategies on near-term quantum devices.