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.

提出了一个基于 Ising 结构的新型机器学习模型，可以通过梯度下降进行高效训练，实验证明了 Ising 机器在不同的学习任务中的新可能性，尤其在量子机器学习领域中，量子资源被用于模型的执行和训练，提供了有希望的前景。