  The hybrid quantum-classical learning scheme provides a prominent way to
achieve quantum advantages on near-term quantum devices. A concrete example
towards this goal is the quantum neural network (QNN), which has been developed
to accomplish various supervised learning tasks such as classification and
regression. However, there are two central issues that remain obscure when QNN
is exploited to accomplish classification tasks. First, a quantum classifier
that can well balance the computational cost such as the number of measurements
and the learning performance is unexplored. Second, it is unclear whether
quantum classifiers can be applied to solve certain problems that outperform
their classical counterparts. Here we devise a Grover-search based quantum
learning scheme (GBLS) to address the above two issues. Notably, most existing
QNN-based quantum classifiers can be seamlessly embedded into the proposed
scheme. The key insight behind our proposal is reformulating the classification
tasks as the search problem. Numerical simulations exhibit that GBLS can
achieve comparable performance with other quantum classifiers under various
noise settings, while the required number of measurements is dramatically
reduced. We further demonstrate a potential quantum advantage of GBLS over
classical classifiers in the measure of query complexity. Our work provides
guidance to develop advanced quantum classifiers on near-term quantum devices
and opens up an avenue to explore potential quantum advantages in various
classification tasks.
