  Toward quantum machine learning deployed on imperfect near-term
intermediate-scale quantum (NISQ) processors, the entire physical
implementation of should include as less as possible hand-designed modules with
only a few ad-hoc parameters to be determined. This work presents such a
hardware-friendly end-to-end quantum machine learning scheme that can be
implemented with imperfect near-term intermediate-scale quantum (NISQ)
processors. The proposal transforms the machine learning task to the
optimization of controlled quantum dynamics, in which the learning model is
parameterized by experimentally tunable control variables. Our design also
enables automated feature selection by encoding the raw input to quantum states
through agent control variables. Comparing with the gate-based parameterized
quantum circuits, the proposed end-to-end quantum learning model is easy to
implement as there are only few ad-hoc parameters to be determined. Numerical
simulations on the benchmarking MNIST dataset demonstrate that the model can
achieve high performance using only 3-5 qubits without downsizing the dataset,
which shows great potential for accomplishing large-scale real-world learning
tasks on NISQ processors.arning models. The scheme is promising for efficiently
performing large-scale real-world learning tasks using intermediate-scale
quantum processors.
