  Accurately solving the Schr\"odinger equation for intricate systems remains a
prominent challenge in physical sciences. A paradigm-shifting approach to
address this challenge involves the application of artificial intelligence
techniques. In this study, we introduce a machine-learning model named
QiankunNet, based on the transformer architecture employed in language models.
By incorporating the attention mechanism, QiankunNet adeptly captures intricate
quantum correlations, which enhances its expressive power. The autoregressive
attribute of QiankunNet allows for the adoption of an exceedingly efficient
sampling technique to estimate the total energy, facilitating the model
training process. Additionally, performance of QiankunNet can be further
improved via a pre-training process. This work not only demonstrates the power
of artificial intelligence in quantum mechanics but also signifies a pivotal
advancement in extending the boundary of systems which can be studied with a
full-configuration-interaction accuracy.
