<ul><li>Quantum machine learning models with data re-uploading circuits are popular for their expressivity and trainability.</li><li>However, a study suggests a limitation in predictive performance with deep encoding layers in these models.</li><li>The research shows a decrease in predictive performance with increased encoding layers when processing high-dimensional data.</li><li>Experiments on various datasets confirm the need for wider circuit architectures in quantum data re-uploading models for high-dimensional data.</li></ul>

Predictive Performance of Deep Quantum Data Re-uploading Models

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