Optimal Interpolation of Entanglement Purification Protocols

• URL: http://arxiv.org/abs/2511.09657v1
• Date: Fri, 14 Nov 2025 01:02:32 GMT
• Title: Optimal Interpolation of Entanglement Purification Protocols
• Authors: Matthew Barber, Stefano Pirandola,
• Abstract summary: Bipartite entanglement purification is the conversion of copies of weakly entangled pairs shared between two separated parties into a smaller number of strongly entangled shared pairs using only local operations and classical communication.<n>We show how to choose this distribution to maximise the rate at which we produce qubit pairs with a given fidelity to a Bell state or, equivalently, to maximise the fidelity to a Bell state of the qubit pairs produced at a given rate.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bipartite entanglement purification is the conversion of copies of weakly entangled pairs shared between two separated parties into a smaller number of strongly entangled shared pairs using only local operations and classical communication. Choosing between different entanglement purification protocols generally involves weighing up a trade-off between the ratio of strongly entangled pairs produced to weakly entangled pairs consumed, which we call the rate of the protocol, and the degree of the entanglement of the strongly entangled pairs, typically measured by the fidelity of those pairs to maximally entangled states. By randomly choosing a protocol according to a probability distribution over a list of protocols for each pair we want to produce, we can achieve rates and fidelities not achieved by any of the original protocols. Here, we show how to choose this distribution to maximise the rate at which we produce qubit pairs with a given fidelity to a Bell state or, equivalently, to maximise the fidelity to a Bell state of the qubit pairs produced at a given rate. We investigate both the asymptotic case, where the number of initial pairs goes to infinity, and the finite-size regime, where protocols are restricted to a finite number of weakly entangled pairs.

Related papers

• Piecemaker: a resource-efficient entanglement distribution protocol [8.247751457673331]
  We introduce multipartite entanglement distribution protocols that use a quantum switch to deliver stabilizer states to a number of remote end users.<n>Unlike existing schemes that wait for all Bell pairs to be established before distributing the desired state, our approach stores only a minimal subset of Bell pairs.<n>Our protocols can achieve the critical fidelity threshold of 1/2 for multipartite entanglement in a wider range of depolarization rates and success probabilities of Bell-pair generation.
  arXiv  Detail & Related papers  (2025-08-20T14:33:40Z)
• Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations [41.94295877935867]
  We use Levy-Prokhorov (LP) ambiguity sets to model distribution shifts.<n>We show that the link between conformal prediction and LP ambiguity sets is a natural one.<n>We construct robust conformal prediction intervals that remain valid under distribution shifts.
  arXiv  Detail & Related papers  (2025-02-19T21:18:11Z)
• 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)
• Breeding protocols are advantageous for finite-length entanglement distillation [0.5439020425818999]
  We propose a framework of converting a stabilizer quantum error-correcting code to a breeding protocol. In this paper, we show an example of a stabilizer that gives a breeding protocol better than hashing protocols.
  arXiv  Detail & Related papers  (2024-01-04T13:33:57Z)
• Secure and robust randomness with sequential quantum measurements [0.0]
  We prove a Tsirelson-like boundary for sequential quantum correlations, which represents a trade-off in nonlocality shared by sequential users.<n>Our simple qubit protocol reaches this boundary, and numerical analysis shows improved robustness under realistic noise.<n>This study advances understanding of sequential quantum correlations and offers insights for efficient device-independent protocols.
  arXiv  Detail & Related papers  (2023-09-21T17:50:29Z)
• Quantum Two-Way Protocol Beyond Superdense Coding: Joint Transfer of Data and Entanglement [33.2699333323263]
  We introduce a generalization of one-way superdense coding to two-way communication protocols for transmitting classical bits by using entangled quantum pairs.<n>The proposed protocol jointly addresses the provision of entangled pairs and superdense coding, introducing an integrated approach for managing entanglement within the communication protocol.<n>We present the results of implementing the protocol in a computer simulation based on the NetSquid framework.
  arXiv  Detail & Related papers  (2023-09-06T08:48:07Z)
• Boosting device-independent cryptography with tripartite nonlocality [0.0]
  Device-independent (DI) protocols certify private randomness by observing nonlocal correlations when two or more parties test a Bell inequality. Here, we consider tripartite DICKA and DIRE protocols based on testing multipartite Bell inequalities. We show that DICKA and DIRE protocols employing tripartite Bell inequalities can significantly outperform their bipartite counterparts.
  arXiv  Detail & Related papers  (2022-09-26T16:35:03Z)
• Optimal supplier of single-error-type entanglement via coherent-state transmission [1.2891210250935146]
  We consider protocol that presents single-error-type entanglement for distant qubits via coherent-state transmission over a lossy channel. This protocol is regarded as a subroutine to serve entanglement for larger protocol to yield a final output, such as ebits or pbits.
  arXiv  Detail & Related papers  (2022-03-31T15:36:54Z)
• A Unified Framework for Multi-distribution Density Ratio Estimation [101.67420298343512]
  Binary density ratio estimation (DRE) provides the foundation for many state-of-the-art machine learning algorithms. We develop a general framework from the perspective of Bregman minimization divergence. We show that our framework leads to methods that strictly generalize their counterparts in binary DRE.
  arXiv  Detail & Related papers  (2021-12-07T01:23:20Z)
• Exact Recovery in the General Hypergraph Stochastic Block Model [92.28929858529679]
  This paper investigates fundamental limits of exact recovery in the general d-uniform hypergraph block model (d-HSBM) We show that there exists a sharp threshold such that exact recovery is achievable above the threshold and impossible below it.
  arXiv  Detail & Related papers  (2021-05-11T03:39:08Z)
• 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)
• Predictive Value Generalization Bounds [27.434419027831044]
  We study a bi-criterion framework for assessing scoring functions in the context of binary classification. We study properties of scoring functions with respect to predictive values by deriving new distribution-free large deviation and uniform convergence bounds.
  arXiv  Detail & Related papers  (2020-07-09T21:23:28Z)
• Bayesian Bits: Unifying Quantization and Pruning [73.27732135853243]
  We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. We experimentally validate our proposed method on several benchmark datasets and show that we can learn pruned, mixed precision networks.
  arXiv  Detail & Related papers  (2020-05-14T16:00:34Z)

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.