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.

翻译：伊辛机是耦合自旋伊辛模型的硬件实现，在人工智能（AI）起源阶段的非监督学习算法发展中具有重要影响。然而，由于监督训练方法与伊辛机物理特性之间的匹配复杂性，其在AI领域的应用受到限制，尽管这些方法对于实现高精度至关重要。本研究提出一种通过均衡传播算法以监督方式训练伊辛机的新方法，取得了与软件实现相当的结果。我们利用D-Wave伊辛机的量子退火程序在MNIST数据集上训练全连接神经网络。此外，我们证明该机器的连接性支持卷积运算，从而能够以每个神经元最少自旋数训练紧凑型卷积网络。我们的研究确立了伊辛机作为有前景的可训练AI硬件平台的地位，具有增强机器学习应用的潜力。