  Adiabatic quantum control protocols have been of wide interest to quantum
computation due to their robustness and insensitivity to their actual duration
of execution. As an extension of previous quantum learning algorithms, this
work proposes to execute some quantum learning protocols based entirely on
adiabatic quantum evolution, hence dubbed as ``adiabatic quantum learning". In
a conventional quantum machine learning protocol, the output is usually the
expectation value of a pre-selected observable and the projective measurement
of which forces a quantum circuit to run many times to obtain the output with a
reasonable precision. By contrast, the proposed adiabatic quantum learning here
may be integrated with future adiabatic weak measurement protocols, where a
single measurement of the system allows to extract the expectation value of
observables of interest without disrupting the concerned quantum states. Our
main idea is illustrated with simple examples.
