Ising machines, which are hardware implementations of the Ising model of
coupled spins, have been influential in the development of unsupervised
learning algorithms at the origins of Artificial Intelligence (AI). However,
their application to AI has been limited due to the complexities in matching
supervised training methods with Ising machine physics, even though these
methods are essential for achieving high accuracy. In this study, we
demonstrate a novel approach to train Ising machines in a supervised way
through the Equilibrium Propagation algorithm, achieving comparable results to
software-based implementations. We employ the quantum annealing procedure of
the D-Wave Ising machine to train a fully-connected neural network on the MNIST
dataset. Furthermore, we demonstrate that the machine's connectivity supports
convolution operations, enabling the training of a compact convolutional
network with minimal spins per neuron. Our findings establish Ising machines as
a promising trainable hardware platform for AI, with the potential to enhance
machine learning applications.

本文介绍了一种新的方法，使用平衡传播算法对 Ising 机器进行监督训练，获得与软件实现相当的结果。我们使用 D-Wave Ising 机器的量子退火程序在 MNIST 数据集上训练全连接神经网络，并且证明了该机器的连通性支持卷积操作，从而实现了每个神经元最小旋转的紧凑卷积网络的训练。这些发现将 Ising 机器作为一种有前途的可训练硬件平台，具有提高机器学习应用的潜力。