On the Characterization of Quantum Flip Stars with Quantum Network Tomography

• URL: http://arxiv.org/abs/2307.05854v1
• Date: Wed, 12 Jul 2023 00:18:15 GMT
• Title: On the Characterization of Quantum Flip Stars with Quantum Network Tomography
• Authors: Matheus Guedes de Andrade, Jake Navas, In\`es Monta\~no, and Don Towsley
• Abstract summary: Quantum Network Tomography refers to the characterization of channel noise in a quantum network through end-to-end measurements. We propose network tomography protocols for quantum star networks formed by quantum channels characterized by a single, non-trivial Pauli operator. Our results further the end-to-end characterization of quantum bit-flip star networks by introducing tomography protocols where state distribution and measurements are designed separately.
• Score: 11.545489116237102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The experimental realization of quantum information systems will be difficult due to how sensitive quantum information is to noise. Overcoming this sensitivity is central to designing quantum networks capable of transmitting quantum information reliably over large distances. Moreover, the ability to characterize communication noise in quantum networks is crucial in developing network protocols capable of overcoming the effects of noise in quantum networks. In this context, quantum network tomography refers to the characterization of channel noise in a quantum network through end-to-end measurements. In this work, we propose network tomography protocols for quantum star networks formed by quantum channels characterized by a single, non-trivial Pauli operator. Our results further the end-to-end characterization of quantum bit-flip star networks by introducing tomography protocols where state distribution and measurements are designed separately. We build upon previously proposed quantum network tomography protocols, as well as provide novel methods for the unique characterization of bit-flip probabilities in stars. We introduce a theoretical benchmark based on the Quantum Fisher Information matrix to compare the efficiency of quantum network protocols. We apply our techniques to the protocols proposed, and provide an initial analysis on the potential benefits of entanglement for Quantum Network Tomography. Furthermore, we simulate the proposed protocols using NetSquid to assess the convergence properties of the estimators obtained for particular parameter regimes. Our findings show that the efficiency of protocols depend on parameter values and motivate the search for adaptive quantum network tomography protocols.

Related papers

• Guarantees on the structure of experimental quantum networks [109.08741987555818]
  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)
• Quantum information spreading and scrambling in a distributed quantum network: A Hasse/Lamport diagrammatic approach [14.308249733521182]
  Large-scale quantum networks, known as quantum internet, hold great promises for advanced distributed quantum computing and long-distance quantum communication. We propose a novel diagrammatic way of visualizing information flow dynamics within the quantum network. We also propose a quantum information scrambling protocol, where a specific node scrambles secret quantum information across the entire network.
  arXiv  Detail & Related papers  (2023-09-19T06:48:42Z)
• 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)
• Multi-User Entanglement Distribution in Quantum Networks Using Multipath Routing [55.2480439325792]
  We propose three protocols that increase the entanglement rate of multi-user applications by leveraging multipath routing. The protocols are evaluated on quantum networks with NISQ constraints, including limited quantum memories and probabilistic entanglement generation.
  arXiv  Detail & Related papers  (2023-03-06T18:06:00Z)
• 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)
• 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)
• Quantum Semantic Communications for Resource-Efficient Quantum Networking [52.3355619190963]
  This letter proposes a novel quantum semantic communications (QSC) framework exploiting advancements in quantum machine learning and quantum semantic representations. The proposed framework achieves approximately 50-75% reduction in quantum communication resources needed, while achieving a higher quantum semantic fidelity.
  arXiv  Detail & Related papers  (2022-05-05T03:49:19Z)
• Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry [10.286119086329762]
  Compressive sensing is a protocol that facilitates reconstruction of large signals from relatively few measurements. Recent efforts in the literature consider instead a data-driven approach, training tensor networks to learn the structure of signals of interest. We present an alternative "quantum" protocol, in which the state of the tensor network is a quantum state over a set of entangled qubits.
  arXiv  Detail & Related papers  (2022-04-27T16:20:28Z)
• Benchmarking of Quantum Protocols [0.9176056742068812]
  We consider several quantum protocols that enable promising functionalities and services in near-future quantum networks. We use NetSquid simulation platform to evaluate the effect of various sources of noise on the performance of these protocols.
  arXiv  Detail & Related papers  (2021-11-03T21:17:04Z)
• SeQUeNCe: A Customizable Discrete-Event Simulator of Quantum Networks [53.56179714852967]
  This work develops SeQUeNCe, a comprehensive, customizable quantum network simulator. We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories. We are releasing SeQUeNCe as an open source tool and aim to generate community interest in extending it.
  arXiv  Detail & Related papers  (2020-09-25T01:52:15Z)
• Quantum State Discrimination on Reconfigurable Noise-Robust Quantum Networks [6.85316573653194]
  A fundamental problem in Quantum Information Processing is the discrimination amongst a set of quantum states of a system. In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a Quantum Walk. We optimize the parameters of the network to obtain the highest probability of correct discrimination.
  arXiv  Detail & Related papers  (2020-03-25T19:07:03Z)

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.