Self-paced hard-example mining, which trains a quantum convolutional network on its ten highest-loss states each epoch, outperforms standard training on two spin-chain phase recognition benchmarks.
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Learning complexity gradually in quantum machine learning models
Self-paced hard-example mining, which trains a quantum convolutional network on its ten highest-loss states each epoch, outperforms standard training on two spin-chain phase recognition benchmarks.