Applying PCA to a training set of optimal QAOA parameters lets QAOA-PCA optimize larger MaxCut instances with far fewer optimizer iterations, trading a slight loss in approximation ratio for substantial efficiency gains.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis
Applying PCA to a training set of optimal QAOA parameters lets QAOA-PCA optimize larger MaxCut instances with far fewer optimizer iterations, trading a slight loss in approximation ratio for substantial efficiency gains.