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.
The dual-Barab\'asi-Albert model
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
The ability to sample random networks that can accurately represent real social contact networks is essential to the study of viral epidemics. The Barab\'asi-Albert model and its extensions attempt to capture reality by generating networks with power-law degree distributions, but properties of the resulting distributions (e.g. minimum, average, and maximum degree) are often unrealistic of the social contacts the models attempt to capture. I propose a novel extension of the Barab\'asi-Albert model, which I call the "dual-Barab\'asi-Albert" (DBA) model, that attempts to better capture these properties of real networks of social contact.
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.