Chiral excitation flows of multinode network based on synthetic gauge fields

• URL: http://arxiv.org/abs/2312.02009v1
• Date: Mon, 4 Dec 2023 16:31:02 GMT
• Title: Chiral excitation flows of multinode network based on synthetic gauge fields
• Authors: Fo-Hong Wang, Xian-Liang Lu, Jia-Jin Zou, Ze-Liang Xiang
• Abstract summary: Chiral excitation flows have drawn a lot of attention for their unique unidirectionality. Such flows have been studied in three-node networks with synthetic gauge fields (SGFs) We propose a scheme to achieve chiral flows in $n$-node networks, where an auxiliary node is introduced to govern the system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chiral excitation flows have drawn a lot of attention for their unique unidirectionality. Such flows have been studied in three-node networks with synthetic gauge fields (SGFs), while they are barely realized as the number of nodes increases. In this work, we propose a scheme to achieve chiral flows in $n$-node networks, where an auxiliary node is introduced to govern the system. This auxiliary node is coupled to all the network nodes, forming sub-triangle structures with interference paths in these networks. We find the implicit chiral symmetry behind the perfect chiral flow and propose the universal criteria that incorporate previous models, facilitating the implementation of chiral transmission in various networks. By investigating the symmetries within these models, we present different features of the chiral flow in bosonic and spin networks. Furthermore, we extend the four-node model into a ladder network, which is promising for remote state transfer in practical systems with less complexity. Our scheme can be realized in state-of-the-art experimental systems, such as superconducting circuits and magnetic photonic lattices, thereby opening up new possibilities for future quantum networks.

Related papers

• Approximately-symmetric neural networks for quantum spin liquids [0.4369058206183195]
  We propose and analyze a family of approximately-symmetric neural networks for quantum spin liquid problems. Our work paves the way toward investigating quantum spin liquid problems within interpretable neural network architectures.
  arXiv  Detail & Related papers  (2024-05-27T18:00:00Z)
• Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks [79.16635054977068]
  We present a new class of equivariant neural networks, dubbed Lattice-Equivariant Neural Networks (LENNs) Our approach develops within a recently introduced framework aimed at learning neural network-based surrogate models Lattice Boltzmann collision operators. Our work opens towards practical utilization of machine learning-augmented Lattice Boltzmann CFD in real-world simulations.
  arXiv  Detail & Related papers  (2024-05-22T17:23:15Z)
• Faddeev-Jackiw quantisation of nonreciprocal quasi-lumped electrical networks [0.0]
  We present an exact method for obtaining canonically quantisable Hamiltonian descriptions of nonreciprocal quasi-lumped electrical networks. We show how our method seamlessly facilitates the characterisation of general nonreciprocal, dissipative linear environments.
  arXiv  Detail & Related papers  (2024-01-17T10:49:43Z)
• On the Universal Approximation Property of Deep Fully Convolutional Neural Networks [15.716533830931766]
  We prove that deep residual fully convolutional networks and their continuous-layer counterpart can achieve universal approximation of symmetric functions at constant channel width. We show that these requirements are necessary, in the sense that networks with fewer channels or smaller kernels fail to be universal approximators.
  arXiv  Detail & Related papers  (2022-11-25T12:02:57Z)
• Bandwidth-efficient distributed neural network architectures with application to body sensor networks [73.02174868813475]
  This paper describes a conceptual design methodology to design distributed neural network architectures. We show that the proposed framework enables up to a factor 20 in bandwidth reduction with minimal loss. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.
  arXiv  Detail & Related papers  (2022-10-14T12:35:32Z)
• A scheme for multipartite entanglement distribution via separable carriers [68.8204255655161]
  We develop a strategy for entanglement distribution via separable carriers that can be applied to any number of network nodes. We show that our protocol results in multipartite entanglement, while the carrier mediating the process is always in a separable state with respect to the network.
  arXiv  Detail & Related papers  (2022-06-20T10:50:45Z)
• Towards Understanding Theoretical Advantages of Complex-Reaction Networks [77.34726150561087]
  We show that a class of functions can be approximated by a complex-reaction network using the number of parameters. For empirical risk minimization, our theoretical result shows that the critical point set of complex-reaction networks is a proper subset of that of real-valued networks.
  arXiv  Detail & Related papers  (2021-08-15T10:13:49Z)
• Controllable entangled state distribution in a dual-rail reconfigurable optical network [62.997667081978825]
  Reconfigurable distribution of entangled states is essential for operation of quantum networks connecting multiple devices such as quantum memories and quantum computers. We introduce new quantum distribution network architecture enabling control of the entangled state propagation direction using linear-optical devices and phase shifters.
  arXiv  Detail & Related papers  (2021-08-04T10:26:37Z)
• Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
  This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
  arXiv  Detail & Related papers  (2021-06-15T09:03:28Z)
• Transmission and navigation on disordered lattice networks, directed spanning forests and Brownian web [2.0305676256390934]
  In this work, we investigate the geometry of networks based on randomly perturbed lattices based on spatially dependent point fields. In the regime of low disorder, we show in 2D and 3D that the DSF almost surely consists of a single tree. In 2D, we further establish that the DSF, as a collection of paths, converges under diffusive scaling to the Brownian web.
  arXiv  Detail & Related papers  (2020-02-17T11:45:49Z)
• Potential energy of complex networks: a novel perspective [0.0]
  We present a novel characterization of complex networks, based on the potential of an associated Schr"odinger equation. Crucial information is retained in the reconstructed potential, which provides a compact representation of the properties of the network structure.
  arXiv  Detail & Related papers  (2020-02-11T17:13:07Z)

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.