The authors evolve max-cut graph instances in a graph autoencoder's latent space that are easy or hard for RQAOA relative to the Goemans-Williamson algorithm, then analyze their features.
Title resolution pending
1 Pith paper cite this work, alongside 18 external citations. Polarity classification is still indexing.
1
Pith paper citing it
18
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
fields
cs.ET 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms
The authors evolve max-cut graph instances in a graph autoencoder's latent space that are easy or hard for RQAOA relative to the Goemans-Williamson algorithm, then analyze their features.