Efficient entanglement purification based on noise guessing decoding

• URL: http://arxiv.org/abs/2310.19914v4
• Date: Wed, 11 Sep 2024 15:30:04 GMT
• Title: Efficient entanglement purification based on noise guessing decoding
• Authors: André Roque, Diogo Cruz, Francisco A. Monteiro, Bruno C. Coutinho,
• Abstract summary: We propose a novel bipartite entanglement purification protocol built upon hashing and guessing random additive noise decoding (GRAND) Our protocol offers substantial advantages over existing hashing protocols, requiring fewer qubits for purification, achieving higher fidelities, and delivering better yields with reduced computational costs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel bipartite entanglement purification protocol built upon hashing and upon the guessing random additive noise decoding (GRAND) approach recently devised for classical error correction codes. Our protocol offers substantial advantages over existing hashing protocols, requiring fewer qubits for purification, achieving higher fidelities, and delivering better yields with reduced computational costs. We provide numerical and semi-analytical results to corroborate our findings and provide a detailed comparison with the hashing protocol of Bennet et al. Although that pioneering work devised performance bounds, it did not offer an explicit construction for implementation. The present work fills that gap, offering both an explicit and more efficient purification method. We demonstrate that our protocol is capable of purifying states with noise on the order of 10% per Bell pair even with a small ensemble of 16 pairs. The work explores a measurement-based implementation of the protocol to address practical setups with noise. This work opens the path to practical and efficient entanglement purification using hashing-based methods with feasible computational costs. Compared to the original hashing protocol, the proposed method can achieve some desired fidelity with a number of initial resources up to one hundred times smaller. Therefore, the proposed method seems well-fit for future quantum networks with a limited number of resources and entails a relatively low computational overhead.

Related papers

• Bayesian Optimization for Repeater Protocols [0.0]
  "First-generation" quantum repeater chains distribute entanglement by executing protocols composed of probabilistic entanglement generation, swapping and distillation operations. We show that calculating the secretkey rate for a given protocol is non-trivial due to experimental imperfections and the probabilistic nature of the operations. We use our framework to extract insight on how to maximize the efficiency of repeater protocols across varying node configurations and hardware conditions.
  arXiv  Detail & Related papers  (2025-02-04T10:46:12Z)
• Protocols and Trade-Offs of Quantum State Purification [4.732131350249]
  We introduce a general state purification framework designed to achieve the highest fidelity with a specified probability. For i.i.d. quantum states under depolarizing noise, our framework can replicate the purification protocol proposed by Barenco and al. We prove the protocols' optimality for two copies of noisy states with any dimension and confirm its optimality for higher numbers of copies and dimensions.
  arXiv  Detail & Related papers  (2024-04-01T14:34:45Z)
• Statistical evaluation and optimization of entanglement purification protocols [0.0]
  We demonstrate that pioneering protocols are unable to improve the estimated initial average concurrence of almost uniformly sampled density matrices. We also develop a more efficient protocol and investigate it numerically together with a recent proposal based on an entangling rank-$2$ projector.
  arXiv  Detail & Related papers  (2024-02-19T16:58:03Z)
• An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning [55.467047686093025]
  A common approach to alleviate such forgetting is to rehearse samples from prior tasks during fine-tuning. We propose a sampling scheme, textttbf mix-cd, that prioritizes rehearsal of collateral damage'' samples. Our approach is computationally efficient, easy to implement, and outperforms several leading continual learning methods in compute-constrained settings.
  arXiv  Detail & Related papers  (2024-02-12T22:32:12Z)
• Near-term $n$ to $k$ distillation protocols using graph codes [0.0]
  Noisy hardware forms one of the main hurdles to the realization of a near-term quantum internet. We consider here an experimentally relevant class of distillation protocols, which distill $n$ to $k$ end-to-end entangled pairs. We leverage this correspondence to find provably optimal distillation protocols in this class for several tasks important for the quantum internet.
  arXiv  Detail & Related papers  (2023-03-20T21:46:17Z)
• One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching [19.831174790835732]
  Image hashing is a principled approximate nearest neighbor approach to find similar items to a query in a large collection of images. For optimal retrieval performance, producing balanced hash codes with low-quantization error is important. This paper considers an alternative approach to learning the quantization constraints. The task of learning balanced codes with low quantization error is re-formulated as matching the learned distribution of the continuous codes to a pre-defined discrete, uniform distribution.
  arXiv  Detail & Related papers  (2022-05-31T12:11:17Z)
• Data post-processing for the one-way heterodyne protocol under composable finite-size security [62.997667081978825]
  We study the performance of a practical continuous-variable (CV) quantum key distribution protocol. We focus on the Gaussian-modulated coherent-state protocol with heterodyne detection in a high signal-to-noise ratio regime. This allows us to study the performance for practical implementations of the protocol and optimize the parameters connected to the steps above.
  arXiv  Detail & Related papers  (2022-05-20T12:37:09Z)
• Entanglement purification by counting and locating errors with entangling measurements [62.997667081978825]
  We consider entanglement purification protocols for multiple copies of qubit states. We use high-dimensional auxiliary entangled systems to learn about number and positions of errors in the noisy ensemble.
  arXiv  Detail & Related papers  (2020-11-13T19:02:33Z)
• 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)
• CIMON: Towards High-quality Hash Codes [63.37321228830102]
  We propose a new method named textbfComprehensive stextbfImilarity textbfMining and ctextbfOnsistency leartextbfNing (CIMON) First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes.
  arXiv  Detail & Related papers  (2020-10-15T14:47:14Z)
• Reinforcing Short-Length Hashing [61.75883795807109]
  Existing methods have poor performance in retrieval using an extremely short-length hash code. In this study, we propose a novel reinforcing short-length hashing (RSLH) In this proposed RSLH, mutual reconstruction between the hash representation and semantic labels is performed to preserve the semantic information. Experiments on three large-scale image benchmarks demonstrate the superior performance of RSLH under various short-length hashing scenarios.
  arXiv  Detail & Related papers  (2020-04-24T02:23:52Z)

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.