  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.
