We find an efficient approach to approximately convert matrix product states (MPSs) into restricted Boltzmann machine wave functions consisting of a multinomial hidden unit through a canonical polyadic (CP) decomposition of the MPSs. This method allows us to generate well-behaved initial neural network quantum states for quantum many-body ground-state calculations in polynomial time of the number of variational parameters and systematically shorten the distance between the initial states and the ground states while increasing the rank of the CP decomposition. We demonstrate the efficiency of our method by taking the transverse-field Ising model as an example and discuss possible applications of our method to more general quantum many-body systems in which the ground-state wave functions possess complex nodal structures.

翻译：我们提出了一种高效方法，通过矩阵乘积态（MPS）的规范多分量（CP）分解，将矩阵乘积态近似转换为由多项式隐藏单元组成的受限玻尔兹曼机波函数。该方法使我们能够在变分参数数量的多项式时间内，为量子多体基态计算生成行为良好的初始神经网络量子态，并通过增加CP分解的秩，系统性地缩短初始态与基态之间的距离。我们以横场伊辛模型为例验证了该方法的效率，并讨论了该方法在基态波函数具有复杂节点结构的更一般量子多体系统中的潜在应用。