  Machine learning can be substantially powered by a quantum computer owing to
its huge Hilbert space and inherent quantum parallelism. In the pursuit of
quantum advantages for machine learning with noisy intermediate-scale quantum
devices, it was proposed that the learning model can be designed in an
end-to-end fashion, i.e., the quantum ansatz is parameterized by directly
manipulable control pulses without circuit design and compilation. Such
gate-free models are hardware friendly and can fully exploit limited quantum
resources. Here, we report the first experimental realization of quantum
end-to-end machine learning on a superconducting processor. The trained model
can achieve 98% recognition accuracy for two handwritten digits (via two
qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National
Institute of Standards and Technology) database. The experimental results
exhibit the great potential of quantum end-to-end learning for resolving
complex real-world tasks when more qubits are available.
