Experimental evidence of generalization in quantum machine learning in small-data regime
This work develops a hardware-compatible QCNN with mid-circuit measurement and classical feed-forward, and shows on a binary handwritten-digit task that strong test performance is achievable from as few as 10 training samples, with the generalization error decreasing as the training set grows.