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.
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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.