论文标题
有效训练的半量子限制的玻尔兹曼机器
Effectively Trainable Semi-Quantum Restricted Boltzmann Machine
论文作者
论文摘要
我们为受限的玻尔兹曼机器（RBM）提出了一个新型的量子模型，其中可见单元保持经典，而隐藏的单元则被量化为非相互作用的费米子。费米子的自由运动与可见单元的经典信号进行了参数耦合。该模型具有量子行为，例如隐藏单元之间的连贯性。数值实验表明，这一事实使其比具有相同数量的隐藏单元的经典RBM更强大。同时，所提出的模型比量子玻尔兹曼机器（QBM）的其他方法的显着优势在于，它是可以在古典计算机上解决的，并且可以在古典计算机上有效训练：相对于其参数，log-likikelihood梯度有一个封闭的表达式。这一事实使其不仅有趣，不仅是假设量子模拟器的模型，而且作为量子启发的经典机器学习算法。
We propose a novel quantum model for the restricted Boltzmann machine (RBM), in which the visible units remain classical whereas the hidden units are quantized as noninteracting fermions. The free motion of the fermions is parametrically coupled to the classical signal of the visible units. This model possesses a quantum behaviour such as coherences between the hidden units. Numerical experiments show that this fact makes it more powerful than the classical RBM with the same number of hidden units. At the same time, a significant advantage of the proposed model over the other approaches to the Quantum Boltzmann Machine (QBM) is that it is exactly solvable and efficiently trainable on a classical computer: there is a closed expression for the log-likelihood gradient with respect to its parameters. This fact makes it interesting not only as a model of a hypothetical quantum simulator, but also as a quantum-inspired classical machine-learning algorithm.
