  We propose a quantum machine learning algorithm for efficiently solving a
class of problems encoded in quantum controlled unitary operations. The central
physical mechanism of the protocol is the iteration of a quantum time-delayed
equation that introduces feedback in the dynamics and eliminates the necessity
of intermediate measurements. The performance of the quantum algorithm is
analyzed by comparing the results obtained in numerical simulations with the
outcome of classical machine learning methods for the same problem. The use of
time-delayed equations enhances the toolbox of the field of quantum machine
learning, which may enable unprecedented applications in quantum technologies.
