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Tphysicsletters/6879/10/1490/585470tpl/SpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement
Sunday, September 10, 2023 at 2:30:00 PM UTC
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SpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement
Ali Kookani1,3, Yousef Mafi2,3, Payman Kazemikhah2,3
Hossein Aghababa4,5, Kazim Fouladi 1
Masoud Barati6
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1 School of Engineering, College of Farabi, University of Tehran, Tehran, Iran
2 School of Electrical and Computer Engineering, College of Engineering,
University of Tehran, Tehran, Iran
3 Quantum Computation and Communication Laboratory (QCCL), University
of Tehran, Tehran, Iran
4 Department of Engineering, Loyola University Maryland, Maryland
5 Founder of Quantum Computation and Communication Laboratory (QCCL),
University of Tehran, Tehran, Iran
6 Swanson School of Engineering, Electrical Engineering, University of
Pittsburgh, Pittsburgh, Pennsylvania
Acknowledgement
NA
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Abstract
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.
Introduction
In quantum mechanics, an extraordinary phenomenon known as quantum entanglement arises when two or more particles interact so that their quantum states become related [1]. This relation indicates that the particles become correlated and can no longer be described independently [2]. Any change made to one particle will be instantaneously reflected in the others, even if they are far apart [3]. Creating and increasing entanglement in arbitrary qubits for quantum algorithms and quantum information (QI) theory protocols, in which entanglement is a vital resource, plays an influential role [4]. As proof, it excludes undesirable energy levels in quantum annealing [5] and facilitates the exchange of quantum information over long distances [6]. It also provides conditions for transferring classical bits of information with fewer qubits [7]. The first step in creating and increasing entanglement is recognizing its existence and amount. In recent years, various entanglement detection criteria have been proposed [8]. Yet, the positive partial transpose (PPT) criterion determines entanglement only in 2⊗2 and 2⊗3 non-mixed bi-party states by indicating the state is separable if the partial transpose of the density matrix is positive semi-definite [9]. In other words, there are some mixed states that are entangled but still meet the PPT conditions, which are called bound entangled states, as they cannot be used to create a maximally entangled state through local operations and classical communication (LOCC), even though the reduction criterion has been practical here [10]. Moreover, Werner states are another instance in which PPT is violated [11].
Conclusion
In recent years, quantum information theory has witnessed rapid growth and faced notable challenges. One significant hurdle is applying artificial intelligence (AI) to this field due to the complexity of feeding data with complex numbers into conventional AI models. However, this article presents a groundbreaking solution that addresses this challenge and propels the field forward. The key contribution of this research is the development of an advanced deep Convolutional Neural Network (CNN) model, boasting an impressive accuracy rate of 98.5%. This innovative model successfully overcomes the limitations of handling data with complex numbers, thereby unlocking new possibilities for effectively leveraging advanced machine learning techniques in processing quantum information.
References
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