Entanglement Accessibility Measures for the Quantum Internet

• URL: http://arxiv.org/abs/2003.05239v1
• Date: Wed, 11 Mar 2020 11:56:04 GMT
• Title: Entanglement Accessibility Measures for the Quantum Internet
• Authors: Laszlo Gyongyosi, Sandor Imre
• Abstract summary: We define metrics and measures to characterize the ratio of accessible quantum entanglement for complex network failures in the quantum Internet. The proposed methods can be applied to an arbitrary topology quantum network to extract relevant statistics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We define metrics and measures to characterize the ratio of accessible quantum entanglement for complex network failures in the quantum Internet. A complex network failure models a situation in the quantum Internet in which a set of quantum nodes and a set of entangled connections become unavailable. A complex failure can cover a quantum memory failure, a physical link failure, an eavesdropping activity, or any other random physical failure scenario. Here, we define the terms entanglement accessibility ratio, cumulative probability of entanglement accessibility ratio, probabilistic reduction of entanglement accessibility ratio, domain entanglement accessibility ratio, and occurrence coefficient. The proposed methods can be applied to an arbitrary topology quantum network to extract relevant statistics and to handle the quantum network failure scenarios in the quantum Internet.

Related papers

• Guarantees on the structure of experimental quantum networks [105.13377158844727]
  Quantum networks connect and supply a large number of nodes with multi-party quantum resources for secure communication, networked quantum computing and distributed sensing. As these networks grow in size, certification tools will be required to answer questions regarding their properties. We demonstrate a general method to guarantee that certain correlations cannot be generated in a given quantum network.
  arXiv  Detail & Related papers  (2024-03-04T19:00:00Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• An operational definition of quantum information scrambling [0.0]
  Quantum information scrambling (QIS) is a characteristic feature of several quantum systems. We propose a novel and computationally efficient QIS quantifier based on a formulation of QIS in terms of quantum state discrimination. We show that the optimal guessing probability, which reflects the degree of QIS induced by an isometric quantum evolution, is directly connected to the accessible min-information.
  arXiv  Detail & Related papers  (2023-12-18T19:00:01Z)
• Practical limitations on robustness and scalability of quantum Internet [0.7499722271664144]
  We study the limitations on the scaling and robustness of quantum Internet. We present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes. For some examples of quantum networks, we present algorithms to perform different quantum network tasks of interest.
  arXiv  Detail & Related papers  (2023-08-24T12:32:48Z)
• Quantum-enhanced metrology with network states [8.515162179098382]
  We prove a general bound that limits the performance of using quantum network states to estimate a global parameter. Our work establishes both the limitation and the possibility of quantum metrology within quantum networks.
  arXiv  Detail & Related papers  (2023-07-15T09:46:35Z)
• DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization in Future Networks [54.03701670739067]
  We propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks. Based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning.
  arXiv  Detail & Related papers  (2022-09-16T02:44:52Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Quantum Network Tomography with Multi-party State Distribution [10.52717496410392]
  characterization of quantum channels in a quantum network is of paramount importance. We introduce the problem of Quantum Network Tomography. We study this problem in detail for the case of arbitrary star quantum networks with quantum channels described by a single Pauli operator.
  arXiv  Detail & Related papers  (2022-06-06T21:47:09Z)
• Direct Quantum Communications in the Presence of Realistic Noisy Entanglement [69.25543534545538]
  We propose a novel quantum communication scheme relying on realistic noisy pre-shared entanglement. Our performance analysis shows that the proposed scheme offers competitive QBER, yield, and goodput.
  arXiv  Detail & Related papers  (2020-12-22T13:06:12Z)
• Quantum Internet under random breakdowns and intentional attacks [1.14219428942199]
  It is important to understand the robustness of large-scale quantum networks, similar to what has been done for the classical counterpart---the Internet. Our results apply to quantum internet based on fibers for all kinds of quantum communications.
  arXiv  Detail & Related papers  (2020-12-03T20:12:14Z)
• 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.