  We propose a classical-quantum hybrid algorithm for machine learning on
near-term quantum processors, which we call quantum circuit learning. A quantum
circuit driven by our framework learns a given task by tuning parameters
implemented on it. The iterative optimization of the parameters allows us to
circumvent the high-depth circuit. Theoretical investigation shows that a
quantum circuit can approximate nonlinear functions, which is further confirmed
by numerical simulations. Hybridizing a low-depth quantum circuit and a
classical computer for machine learning, the proposed framework paves the way
toward applications of near-term quantum devices for quantum machine learning.
