Adaptive Error Correction for Entanglement Distillation

• URL: http://arxiv.org/abs/2504.11670v1
• Date: Tue, 15 Apr 2025 23:52:25 GMT
• Title: Adaptive Error Correction for Entanglement Distillation
• Authors: Sijie Cheng, Narayanan Rengaswamy,
• Abstract summary: We focus on quantum error correction-based entanglement distillation in a linear chain of quantum repeaters.<n>We propose a novel adaptive error correction scheme that boosts end-to-end metrics.<n>We compare the performance of four different protocols that combine the codes in different ways.
• Score: 10.19019476978683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum network applications impose a variety of requirements on entanglement resources in terms of rate, fidelity, latency, and more. The repeaters in the quantum network must combine good methods for entanglement generation, effective entanglement distillation, and smart routing protocols to satisfy these application requirements. In this work, we focus on quantum error correction-based entanglement distillation in a linear chain of quantum repeaters. While conventional approaches reuse the same distillation scheme over multiple hop lengths after entanglement swaps, we propose a novel adaptive error correction scheme that boosts end-to-end metrics. Specifically, depending on the network operation point, we adapt the code used in distillation over successive rounds to monotonically increase the rate while also improving fidelity. We demonstrate the effectiveness of this strategy using three codes: [[9,1,3]], [[9,2,3]], [[9,3,3]]. We compare the performance of four different protocols that combine the codes in different ways, where we define a new performance metric, efficiency, that incorporates both overall rate and fidelity. While we highlight our innovation under minimal assumptions on noise, the method can be easily generalized to realistic network settings. By combining our approach with good entanglement generation methods and smart routing protocols, we can achieve application requirements in a systematic, resource-efficient, way.

Related papers

• Comparing One- and Two-way Quantum Repeater Architectures [9.942288691108914]
  Quantum repeaters are an essential building block for realizing long-distance quantum communications. Due to the fragile nature of quantum information, these repeaters suffer from loss and operational errors. Recent increases in the number of available memories, and introduction of entanglement generation through multiplexing motivate a re-comparison of one-way and two-way repeater architectures.
  arXiv  Detail & Related papers  (2024-09-10T01:55:01Z)
• Fast quantum interconnects via constant-rate entanglement distillation [0.0]
  We develop constant-rate entanglement distillation methods for quantum interconnects. We prove the scheme has constant-rate in expectation and numerically optimize to achieve low practical overhead. We find our optimized schemes outperform existing computationally efficient quantum interconnect schemes by an order of magnitude in relevant regimes.
  arXiv  Detail & Related papers  (2024-08-28T16:54:54Z)
• Analysis of Asynchronous Protocols for Entanglement Distribution in Quantum Networks [9.971549076128268]
  We explore two minimal asynchronous protocols for entanglement in quantum networks. A parallel scheme generating entanglement independently at the link level, and a sequential scheme extending entanglement iteratively from one party to the other. Our findings suggest the sequential scheme's superiority due to comparable performance with the parallel scheme, coupled with simpler implementation.
  arXiv  Detail & Related papers  (2024-05-03T18:04:11Z)
• 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)
• Expeditious Saliency-guided Mix-up through Random Gradient Thresholding [89.59134648542042]
  Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks. In this paper, inspired by the superior qualities of each direction over one another, we introduce a novel method that lies at the junction of the two routes. We name our method R-Mix following the concept of "Random Mix-up" In order to address the question of whether there exists a better decision protocol, we train a Reinforcement Learning agent that decides the mix-up policies.
  arXiv  Detail & Related papers  (2022-12-09T14:29:57Z)
• 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)
• Manifold Regularized Dynamic Network Pruning [102.24146031250034]
  This paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost.
  arXiv  Detail & Related papers  (2021-03-10T03:59:03Z)
• Fully Quantized Image Super-Resolution Networks [81.75002888152159]
  We propose a Fully Quantized image Super-Resolution framework (FQSR) to jointly optimize efficiency and accuracy. We apply our quantization scheme on multiple mainstream super-resolution architectures, including SRResNet, SRGAN and EDSR. Our FQSR using low bits quantization can achieve on par performance compared with the full-precision counterparts on five benchmark datasets.
  arXiv  Detail & Related papers  (2020-11-29T03:53:49Z)
• Entanglement-assisted entanglement purification [62.997667081978825]
  We present a new class of entanglement-assisted entanglement purification protocols that can generate high-fidelity entanglement from noisy, finite-size ensembles. Our protocols can deal with arbitrary errors, but are best suited for few errors, and work particularly well for decay noise.
  arXiv  Detail & Related papers  (2020-11-13T19:00:05Z)
• Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations [52.493315075385325]
  We show that a family of regularizers, including weight decay, is ineffective at penalizing the intrinsic norms of weights for networks with homogeneous activation functions. We propose an improved regularizer that is invariant to weight scale shifting and thus effectively constrains the intrinsic norm of a neural network.
  arXiv  Detail & Related papers  (2020-08-07T02:55:28Z)
• Optimising repeater schemes for the quantum internet [0.0]
  We study three different experimental quantum repeater implementations on their ability to distribute entanglement. We use the algorithm to study three different experimental quantum repeater implementations on their ability to distribute entanglement.
  arXiv  Detail & Related papers  (2020-06-22T13:18:49Z)

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.