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Toward quantum machine learning deployed on imperfect near-termintermediate-scale quantum (NISQ) processors, the entire physicalimplementation of should include as less as possible hand-designed modules withonly a few ad-hoc parameters to be determined. This work presents such ahardware-friendly end-to-end quantum machine learning scheme that can beimplemented with imperfect near-term intermediate-scale quantum (NISQ)processors. The proposal transforms the machine learning task to theoptimization of controlled quantum dynamics, in which the learning model isparameterized by experimentally tunable control variables. Our design alsoenables automated feature selection by encoding the raw input to quantum statesthrough agent control variables. Comparing with the gate-based parameterizedquantum circuits, the proposed end-to-end quantum learning model is easy toimplement as there are only few ad-hoc parameters to be determined. Numericalsimulations on the benchmarking MNIST dataset demonstrate that the model canachieve high performance using only 3-5 qubits without downsizing the dataset,which shows great potential for accomplishing large-scale real-world learningtasks on NISQ processors.arning models. The scheme is promising for efficientlyperforming large-scale real-world learning tasks using intermediate-scalequantum processors.

End-to-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics