Negativity Percolation in Continuous-Variable Quantum Networks

Fuente: arXiv
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Main Authors: Zhao, Yaqi, He, Kan, Zhang, Yongtao, Hou, Jinchuan, Gao, Jianxi, Havlin, Shlomo, Meng, Xiangyi
Format: Preprint
Published: 2025
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author Zhao, Yaqi
He, Kan
Zhang, Yongtao
Hou, Jinchuan
Gao, Jianxi
Havlin, Shlomo
Meng, Xiangyi
author_facet Zhao, Yaqi
He, Kan
Zhang, Yongtao
Hou, Jinchuan
Gao, Jianxi
Havlin, Shlomo
Meng, Xiangyi
contents Quantum networks (QNs) have been predominantly driven by discrete-variable (DV) architectures. Yet, optical platforms naturally generate Gaussian states--the common states of continuous-variable (CV) systems, making CV-based QNs an attractive route toward scalable, chip-integrated quantum computation and communication. To bridge the gap between well-studied DV entanglement percolation theories and their CV counterpart, we introduce a Gaussian-to-Gaussian entanglement distribution scheme that deterministically transports two-mode squeezed vacuum states across large CV networks. Analysis of the scheme's collective behavior using statistical-physics methods reveals a new form of entanglement percolation--negativity percolation theory (NegPT)--characterized by a bounded entanglement measure called the ratio negativity. We discover that NegPT exhibits a mixed-order phase transition, marked simultaneously by both an abrupt change in global entanglement and a long-range correlation between nodes. This distinctive behavior places CV-based QNs in a new universality class, fundamentally distinct from DV systems. Additionally, the abruptness of this transition introduces a critical vulnerability of CV-based QNs: conventional feedback mechanism becomes inherently unstable near the threshold, highlighting practical implications for stabilizing large-scale CV-based QNs. Our results unify statistical models for CV-based entanglement distribution and uncover previously unexplored critical phenomena unique to CV systems, providing valuable insights and guidelines essential for developing robust, feedback-stabilized QNs.
format Preprint
id arxiv_https___arxiv_org_abs_2507_16417
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Negativity Percolation in Continuous-Variable Quantum Networks
Zhao, Yaqi
He, Kan
Zhang, Yongtao
Hou, Jinchuan
Gao, Jianxi
Havlin, Shlomo
Meng, Xiangyi
Quantum Physics
Quantum networks (QNs) have been predominantly driven by discrete-variable (DV) architectures. Yet, optical platforms naturally generate Gaussian states--the common states of continuous-variable (CV) systems, making CV-based QNs an attractive route toward scalable, chip-integrated quantum computation and communication. To bridge the gap between well-studied DV entanglement percolation theories and their CV counterpart, we introduce a Gaussian-to-Gaussian entanglement distribution scheme that deterministically transports two-mode squeezed vacuum states across large CV networks. Analysis of the scheme's collective behavior using statistical-physics methods reveals a new form of entanglement percolation--negativity percolation theory (NegPT)--characterized by a bounded entanglement measure called the ratio negativity. We discover that NegPT exhibits a mixed-order phase transition, marked simultaneously by both an abrupt change in global entanglement and a long-range correlation between nodes. This distinctive behavior places CV-based QNs in a new universality class, fundamentally distinct from DV systems. Additionally, the abruptness of this transition introduces a critical vulnerability of CV-based QNs: conventional feedback mechanism becomes inherently unstable near the threshold, highlighting practical implications for stabilizing large-scale CV-based QNs. Our results unify statistical models for CV-based entanglement distribution and uncover previously unexplored critical phenomena unique to CV systems, providing valuable insights and guidelines essential for developing robust, feedback-stabilized QNs.
title Negativity Percolation in Continuous-Variable Quantum Networks
topic Quantum Physics
url https://arxiv.org/abs/2507.16417