Adaptive Quantum Head Reaches 80.9% Accuracy on Fashion-MNIST

Researchers of the New York University Abu Dhabi have demonstrated that a quantum machine learning system, Adaptive KetGPT-QTL, achieved 80.9% accuracy on the Fashion-MNIST dataset, surpassing the performance of a comparable classical system. The work introduces QSTAR: Quantum Selective Transfer with Adaptive Routing, a new framework that strategically employs quantum processing only when a classical model is uncertain. This approach separates the evaluation of quantum circuit design from assessing the actual utility of quantum computation in machine learning. Specifically, KetGPT #180 improved accuracy by 6.82, 4.31, and 3.03 percentage points on low-confidence samples compared to a classical model, suggesting a key role for quantum heads in handling ambiguous data. A…