Entanglement buffering with two quantum memories

Quantum networks crucially rely on the availability of high-quality entangled pairs of qubits, known as entangled links, distributed across distant nodes. Maintaining the quality of these links is a challenging task due to the presence of time-dependent noise, also known as decoherence. Entanglement purification protocols offer a solution by converting multiple low-quality entangled states into a smaller number of higher-quality ones. In this work, we introduce a framework to analyse the performance of entanglement buffering setups that combine entanglement consumption, decoherence, and entanglement purification. We propose two key metrics: the availability, which is the steady-state probability that an entangled link is present, and the average consumed fidelity, which quantifies the steady-state quality of consumed links. We then investigate a two-node system, where each node possesses two quantum memories: one for long-term entanglement storage, and another for entanglement generation. We model this setup as a continuous-time stochastic process and derive analytical expressions for the performance metrics. Our findings unveil a trade-off between the availability and the average consumed fidelity. We also bound these performance metrics for a buffering system that employs the well-known bilocal Clifford purification protocols. Importantly, our analysis demonstrates that, in the presence of noise, consistently purifying the buffered entanglement increases the average consumed fidelity, even when some buffered entanglement is discarded due to purification failures.

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Publication Details

Journal
Quantum
Published
2024-09-03
DOI
https://doi.org/10.22331/q-2024-09-03-1458
Citations
8
Primary Topic
Quantum Information and Cryptography
Type
article
Field-Weighted Citation Impact
2.78

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article

Entanglement buffering with two quantum memories

Stephanie Wehner, Álvaro G. Iñesta, Bethany Davies
8 citations
Quantum
Quantum Information and Cryptography
2.78
article

Entanglement buffering with two quantum memories

Stephanie Wehner, Álvaro G. Iñesta, Bethany Davies
article en
8 citations

Abstract

Quantum networks crucially rely on the availability of high-quality entangled pairs of qubits, known as entangled links, distributed across distant nodes. Maintaining the quality of these links is a challenging task due to the presence of time-dependent noise, also known as decoherence. Entanglement purification protocols offer a solution by converting multiple low-quality entangled states into a smaller number of higher-quality ones. In this work, we introduce a framework to analyse the performance of entanglement buffering setups that combine entanglement consumption, decoherence, and entanglement purification. We propose two key metrics: the availability, which is the steady-state probability that an entangled link is present, and the average consumed fidelity, which quantifies the steady-state quality of consumed links. We then investigate a two-node system, where each node possesses two quantum memories: one for long-term entanglement storage, and another for entanglement generation. We model this setup as a continuous-time stochastic process and derive analytical expressions for the performance metrics. Our findings unveil a trade-off between the availability and the average consumed fidelity. We also bound these performance metrics for a buffering system that employs the well-known bilocal Clifford purification protocols. Importantly, our analysis demonstrates that, in the presence of noise, consistently purifying the buffered entanglement increases the average consumed fidelity, even when some buffered entanglement is discarded due to purification failures.

QuantumVol. 8
QuTech (NL), Delft University of Technology (NL)
Koninklijke Nederlandse Akademie van Wetenschappen, Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Openalex Percentile: Top 8%
Quantum Information and Cryptography
2.78
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Entanglement buffering with two quantum memories — Stephanie Wehner, Álvaro G. Iñesta, et al. · Quantum (2024) | TGRS Research Map | TGRS