Quantum Neural Networks: Concepts, Applications, and Challenges

• URL: http://arxiv.org/abs/2108.01468v1
• Date: Mon, 2 Aug 2021 04:32:15 GMT
• Title: Quantum Neural Networks: Concepts, Applications, and Challenges
• Authors: Yunseok Kwak, Won Joon Yun, Soyi Jung, Joongheon Kim
• Abstract summary: Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks. This paper explains the backgrounds and basic principles of quantum deep learning and also introduces major achievements.
• Score: 11.370663646213606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks. The research topics and directions of deep learning and quantum computing have been separated for long time, however by discovering that quantum circuits can act like artificial neural networks, quantum deep learning research is widely adopted. This paper explains the backgrounds and basic principles of quantum deep learning and also introduces major achievements. After that, this paper discusses the challenges of quantum deep learning research in multiple perspectives. Lastly, this paper presents various future research directions and application fields of quantum deep learning.

Related papers

• Towards Federated Learning on the Quantum Internet [2.869565958847859]
  The quantum internet may allow a plethora of applications such as distributed or blind quantum computing. We evaluate a potential application for the quantum internet, namely quantum federated learning.
  arXiv  Detail & Related papers  (2024-02-15T11:58:42Z)
• Complex Quantum Networks: a Topical Review [0.9217021281095907]
  Review of the rising field of complex quantum networks. New generation of quantum algorithms to infer significant network properties. Four main research lines: network-generalized, quantum-applied, quantum-generalized and quantum-enhanced.
  arXiv  Detail & Related papers  (2023-11-27T19:13:19Z)
• Entanglement-Assisted Quantum Networks: Mechanics, Enabling Technologies, Challenges, and Research Directions [66.27337498864556]
  This paper presents a comprehensive survey of entanglement-assisted quantum networks. It provides a detailed overview of the network structure, working principles, and development stages. It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
  arXiv  Detail & Related papers  (2023-07-24T02:48:22Z)
• 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)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation [10.40861455010028]
  We will introduce the concept of quantum reinforcement learning using a variational quantum circuit. We will discuss the power and possibility of quantum reinforcement learning from the experimental results obtained through this work.
  arXiv  Detail & Related papers  (2021-08-16T01:05:49Z)
• 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)
• Advances in Quantum Deep Learning: An Overview [9.188318506016898]
  We review the different schemes proposed to model quantum neural networks (QNNs) and other variants like quantum convolutional networks (QCNNs) We briefly describe the recent progress in quantum inspired classic deep learning algorithms and their applications to natural language processing.
  arXiv  Detail & Related papers  (2020-05-08T23:36:50Z)
• An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
  We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
  arXiv  Detail & Related papers  (2020-05-06T14:18:44Z)

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.