Imperfect quantum networks with tailored resource states

• URL: http://arxiv.org/abs/2403.19778v1
• Date: Thu, 28 Mar 2024 19:00:02 GMT
• Title: Imperfect quantum networks with tailored resource states
• Authors: Maria Flors Mor-Ruiz, Julius Wallnöfer, Wolfgang Dür,
• Abstract summary: Entanglement-based quantum networks exhibit a unique flexibility in the choice of entangled resource states. We study how the flexibility of this approach can be used for the distribution of entanglement in a fully asymmetric network scenario.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entanglement-based quantum networks exhibit a unique flexibility in the choice of entangled resource states that are then locally manipulated by the nodes to fulfill any request in the network. Furthermore, this manipulation is not uniquely defined and thus can be optimized. We tailor the adaptation of the resource state or pre-established entanglement to achieve bipartite communication in an imperfect setting that includes time-dependent memory errors. In this same setting, we study how the flexibility of this approach can be used for the distribution of entanglement in a fully asymmetric network scenario. The considered entanglement topology is a custom one based on the minimization of the required measurements to retrieve a Bell pair. The optimization of the manipulation and the study of such a custom entanglement topology are performed using the noisy stabilizer formalism, a recently introduced method to fully track noise on graph states. We find that exploiting the flexibility of the entanglement topology, given a certain set of bipartite requests, is highly favorable in terms of the fidelity of the final state.

Related papers

• Optimal Second-Order Rates for Quantum Information Decoupling [14.932939960009605]
  We consider the standard quantum information decoupling, in which Alice aims to decouple her system from the environment by local operations and discarding some of her systems. We find an achievability bound in entanglement distillation protocol, where the objective is for Alice and Bob to transform their quantum state to maximally entangled state with largest possible dimension.
  arXiv  Detail & Related papers  (2024-03-21T12:06:30Z)
• Influence of noise in entanglement-based quantum networks [0.0]
  We consider entanglement-based quantum networks, where multipartite entangled resource states are distributed and stored among the nodes. We study the influence of noise in this process, where we consider imperfections in state preparation, memories, and measurements. We find that in large networks, high-dimensional cluster states are favorable and lead to a significantly higher target state fidelity.
  arXiv  Detail & Related papers  (2023-05-05T18:00:06Z)
• Entangled Pair Resource Allocation under Uncertain Fidelity Requirements [59.83361663430336]
  In quantum networks, effective entanglement routing facilitates communication between quantum source and quantum destination nodes. We propose a resource allocation model for entangled pairs and an entanglement routing model with a fidelity guarantee. Our proposed model can reduce the total cost by at least 20% compared to the baseline model.
  arXiv  Detail & Related papers  (2023-04-10T07:16:51Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing [35.57643489979182]
  Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy (DP) This article aims to jointly optimize the quantization and Binomial mechanism parameters and communication resources to maximize the convergence rate under the constraints of the wireless network and DP requirement.
  arXiv  Detail & Related papers  (2022-11-14T07:54:14Z)
• Learning Resilient Radio Resource Management Policies with Graph Neural Networks [124.89036526192268]
  We formulate a resilient radio resource management problem with per-user minimum-capacity constraints. We show that we can parameterize the user selection and power control policies using a finite set of parameters. Thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the 5th percentile rate.
  arXiv  Detail & Related papers  (2022-03-07T19:40:39Z)
• Standard Deviation-Based Quantization for Deep Neural Networks [17.495852096822894]
  Quantization of deep neural networks is a promising approach that reduces the inference cost. We propose a new framework to learn the quantization intervals (discrete values) using the knowledge of the network's weight and activation distributions. Our scheme simultaneously prunes the network's parameters and allows us to flexibly adjust the pruning ratio during the quantization process.
  arXiv  Detail & Related papers  (2022-02-24T23:33:47Z)
• The Sample Complexity of One-Hidden-Layer Neural Networks [57.6421258363243]
  We study a class of scalar-valued one-hidden-layer networks, and inputs bounded in Euclidean norm. We prove that controlling the spectral norm of the hidden layer weight matrix is insufficient to get uniform convergence guarantees. We analyze two important settings where a mere spectral norm control turns out to be sufficient.
  arXiv  Detail & Related papers  (2022-02-13T07:12:02Z)
• A Reconfigurable Quantum Local Area Network Over Deployed Fiber [1.1713998235451095]
  We implement flex-grid entanglement distribution in a deployed network for the first time. We quantify the quality of the distributed polarization entanglement via log-negativity. We demonstrate one possible quantum protocol enabled by the distributed entanglement network.
  arXiv  Detail & Related papers  (2021-02-26T17:08:03Z)
• Optimizing Mode Connectivity via Neuron Alignment [84.26606622400423]
  Empirically, the local minima of loss functions can be connected by a learned curve in model space along which the loss remains nearly constant. We propose a more general framework to investigate effect of symmetry on landscape connectivity by accounting for the weight permutations of networks being connected.
  arXiv  Detail & Related papers  (2020-09-05T02:25:23Z)
• Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks [119.81868223344173]
  This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure. The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required.
  arXiv  Detail & Related papers  (2020-06-26T14:20:48Z)

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.