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The application of machine learning models in quantum information theory hassurged in recent years, driven by the recognition of entanglement and quantumstates, which are the essence of this field. However, most of these studiesrely on existing prefabricated models, leading to inadequate accuracy. Thiswork aims to bridge this gap by introducing a custom deep convolutional neuralnetwork (CNN) model explicitly tailored to quantum systems. Our proposed CNNmodel, the so-called XpookyNet, effectively overcomes the challenge of handlingcomplex numbers data inherent to quantum systems and achieves an accuracy of98.5%. Developing this custom model enhances our ability to analyze andunderstand quantum states. However, first and foremost, quantum states shouldbe classified more precisely to examine fully and partially entangled states,which is one of the cases we are currently studying. As machine learning andquantum information theory are integrated into quantum systems analysis,various perspectives, and approaches emerge, paving the way for innovativeinsights and breakthroughs in this field.

XpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement