XpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866917745201774592 |
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| author | Kookani, Ali Mafi, Yousef Kazemikhah, Payman Aghababa, Hossein Fouladi, Kazim Barati, Masoud |
| author_facet | Kookani, Ali Mafi, Yousef Kazemikhah, Payman Aghababa, Hossein Fouladi, Kazim Barati, Masoud |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_03890 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | XpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement Kookani, Ali Mafi, Yousef Kazemikhah, Payman Aghababa, Hossein Fouladi, Kazim Barati, Masoud Quantum Physics 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. |
| title | XpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2309.03890 |