SpookyNet: Advancement in Quantum System Analysis through Convolutional Neural N
Words of the editor about this publication.
SpookyNet is a cutting-edge product that represents a significant advancement in quantum system analysis. It utilizes convolutional neural networks to detect entanglement, a phenomenon that is crucial to the functioning of quantum systems. By leveraging the power of machine learning, SpookyNet is able to provide accurate and efficient analysis of complex quantum systems, enabling researchers to gain deeper insights into the behavior of these systems. With its innovative approach and state-of-the-art technology, SpookyNet is poised to revolutionize the field of quantum system analysis and drive new discoveries in this exciting area of research.
Abstract
Introduction
Conclusion
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 SpookyNet, 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.