Inspired by the universal approximation theorem and widespread adoption of artificial neural network techniques in a diversity of fields, feed‐forward neural networks are proposed as a general purpose trial wave function for quantum Monte Carlo simulations of continuous many‐body systems. Whereas for simple model systems the whole many‐body wave function can be represented by a neural network, the antisymmetry condition of non‐trivial fermionic systems is incorporated by means of a Slater determinant. To demonstrate the accuracy of the trial wave functions, an exactly solvable model system of two trapped interacting particles, as well as the hydrogen dimer, is studied.

中文翻译：

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人工神经网络作为量子蒙特卡洛的试验波函数

受通用逼近定理和人工神经网络技术在各个领域的广泛采用的启发，前馈神经网络被提出作为通用的试验波函数，用于连续多体系统的量子蒙特卡罗模拟。对于简单的模型系统，整个多体波函数可以用神经网络表示，而非平凡的铁离子系统的反对称条件是通过Slater行列式合并的。为了证明试验波函数的准确性，研究了两个截留的相互作用粒子以及氢二聚体的可精确求解的模型系统。