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

• URL: http://arxiv.org/abs/2309.03890v4
• Date: Sat, 10 Aug 2024 12:09:05 GMT
• Title: XpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement
• Authors: Ali Kookani, Yousef Mafi, Payman Kazemikhah, Hossein Aghababa, Kazim Fouladi, Masoud Barati,
• Abstract summary: We introduce 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. First and foremost, quantum states should be classified more precisely to examine fully and partially entangled states.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• Shedding Light on the Future: Exploring Quantum Neural Networks through Optics [3.1935899800030096]
  Quantum neural networks (QNNs) play an important role as an emerging technology in the rapidly developing field of quantum machine learning. This article reviews the concept of QNNs and their physical realizations, particularly implementations based on quantum optics.
  arXiv  Detail & Related papers  (2024-09-04T08:49:57Z)
• End-to-End Quantum Vision Transformer: Towards Practical Quantum Speedup in Large-Scale Models [20.72342380227143]
  This paper introduces an end-to-end Quantum Vision Transformer (QViT), which incorporates an innovative quantum residual connection technique. Our thorough analysis of the QViT reveals a theoretically exponential complexity and empirically speedup, showcasing the model's efficiency and potential in quantum computing applications.
  arXiv  Detail & Related papers  (2024-02-29T08:12:02Z)
• Quantum Generative Adversarial Networks: Bridging Classical and Quantum Realms [0.6827423171182153]
  We explore the synergistic fusion of classical and quantum computing paradigms within the realm of Generative Adversarial Networks (GANs) Our objective is to seamlessly integrate quantum computational elements into the conventional GAN architecture, thereby unlocking novel pathways for enhanced training processes. This research is positioned at the forefront of quantum-enhanced machine learning, presenting a critical stride towards harnessing the computational power of quantum systems.
  arXiv  Detail & Related papers  (2023-12-15T16:51:36Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• An Invitation to Distributed Quantum Neural Networks [0.0]
  We review the current state of the art in distributed quantum neural networks. We find that the distribution of quantum datasets shares more similarities with its classical counterpart than does the distribution of quantum models.
  arXiv  Detail & Related papers  (2022-11-14T00:27:01Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• Entanglement Classification via Neural Network Quantum States [58.720142291102135]
  In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
  arXiv  Detail & Related papers  (2019-12-31T07:40:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.