QSTAR: Quantum Selective Transfer with Adaptive Routing
Quantum transfer learning (QTL) is often evaluated by replacing a classical classifier with a fixed variational quantum head, but this hides a key question: when is the quantum branch actually useful? We propose QSTAR: Quantum Selective Transfer with Adaptive Routing, a selective QTL framework that keeps high-confidence classical predictions and routes only low-confidence samples to a fallback branch. Using a frozen ResNet18 backbone on Fashion-MNIST, we compare manually designed QTL heads, KetGPT-designed quantum heads, and parameter-matched classical baselines under a common data split and optimization schedule. Standard QTL heads reach at most 57.0% accuracy, while the strongest KetGPT...
| Authors | Saim Rehman, Nouhaila Innan, Muhammad Shafique |
|---|---|
| Primary category | quant-ph |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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