  An Ising machine is any hardware specifically designed for finding the ground
state of the Ising model. Relevant examples are coherent Ising machines and
quantum annealers. In this paper, we propose a new machine learning model that
is based on the Ising structure and can be efficiently trained using gradient
descent. We provide a mathematical characterization of the training process,
which is based upon optimizing a loss function whose partial derivatives are
not explicitly calculated but estimated by the Ising machine itself. Moreover,
we present some experimental results on the training and execution of the
proposed learning model. These results point out new possibilities offered by
Ising machines for different learning tasks. In particular, in the quantum
realm, the quantum resources are used for both the execution and the training
of the model, providing a promising perspective in quantum machine learning.
