Boltzmann machines, a class of machine learning models, are the basis of several deep learning methods that have been successfully applied to both supervised and unsupervised machine learning tasks. These models assume that some given dataset is generated according to a Boltzmann distribution, and the goal of the training procedure is to learn the set of parameters that most closely match the input data distribution. Training such models is difficult due to the intractability of traditional sampling techniques, and proposals using quantum annealers for sampling hope to mitigate the cost associated with sampling. However, real physical devices will inevitably be coupled to the environment, and the strength of this coupling affects the effective temperature of the distributions from which a quantum annealer samples. To counteract this problem, error correction schemes that can effectively reduce the temperature are needed if there is to be some benefit in using quantum annealing f...

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纠错量子退火在提高玻尔兹曼机性能方面的局限性

Boltzmann机器是一类机器学习模型，是几种深度学习方法的基础，这些方法已成功应用于有监督和无监督的机器学习任务。这些模型假定某个给定的数据集是根据Boltzmann分布生成的，并且训练过程的目标是学习与输入数据分布最接近的一组参数。由于传统采样技术的难处理性，难以训练此类模型，并且使用量子退火器进行采样的提案希望减轻与采样相关的成本。但是，实际的物理设备将不可避免地与环境耦合，并且这种耦合的强度会影响量子退火器从中采样的分布的有效温度。为了解决这个问题，