变分蒙特卡洛方法中的神经量子态：简要总结
Neural Quantum States in Variational Monte Carlo Method: A Brief Summary
AI总结：
本文综述了基于神经量子态的自旋系统变分蒙特卡洛方法，指出神经网络作为波函数能高效表达非局域相互作用并降低高维系统计算资源，展现了处理大规模系统的潜力。
摘要：
本文综述了基于神经量子态的自旋系统变分蒙特卡洛方法。使用神经网络作为波函数，能够更广义地表达包括高度非局域相互作用在内的各类相互作用，这与其非线性激活函数密切相关。此外，在处理高维系统时，神经网络能以相对较小的计算资源表示相对复杂的波函数，这无疑是一种“扁平化”优势。在量子态层析中，神经量子态的表示方法已取得显著成果，暗示了其在处理更大规模系统方面的潜力。
英文摘要：
In this note, variational Monte Carlo method based on neural quantum states for spin systems is reviewed. Using a neural network as the wave function allows for a more generalized expression of various types of interactions, including highly non-local interactions, which are closely related to its non-linear activation functions. Additionally, neural networks can represent relatively complex wave functions with relatively small computational resources when dealing with higher-dimensional systems, which is undoubtedly a "flattening" advantage. In quantum-state tomography, the representation method of neural quantum states has already achieved significant results, hinting at its potential in handling larger-sized systems.
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