  The application of machine learning models in quantum information theory has
surged in recent years, driven by the recognition of entanglement and quantum
states, which are the essence of this field. However, most of these studies
rely on existing prefabricated models, leading to inadequate accuracy. This
work aims to bridge this gap by introducing a custom deep convolutional neural
network (CNN) model explicitly tailored to quantum systems. Our proposed CNN
model, the so-called XpookyNet, effectively overcomes the challenge of handling
complex numbers data inherent to quantum systems and achieves an accuracy of
98.5%. Developing this custom model enhances our ability to analyze and
understand quantum states. However, first and foremost, quantum states should
be classified more precisely to examine fully and partially entangled states,
which is one of the cases we are currently studying. As machine learning and
quantum information theory are integrated into quantum systems analysis,
various perspectives, and approaches emerge, paving the way for innovative
insights and breakthroughs in this field.
