This paper reports the experimental realization of quantum end-to-end machine learning on a superconducting processor. The trained model achieved 98% recognition accuracy for two handwritten digits (using two qubits) and 89% for four digits (using three qubits) from the MNIST database. The results demonstrate the potential of this approach for complex real-world tasks with more qubits.
