Introduces a unified benchmarking methodology for quantum transfer learning in visual classification tasks, finding that no single method dominates and performance varies with dataset, encoding, and circuit design.
Post- variational classical quantum transfer learning for binary classification,
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Towards Fair Benchmarking of Quantum Transfer Learning for Visual Classification
Introduces a unified benchmarking methodology for quantum transfer learning in visual classification tasks, finding that no single method dominates and performance varies with dataset, encoding, and circuit design.