Noise-Tolerant Optomechanical Entanglement via Synthetic Magnetism

被引:72
|
作者
Lai, Deng-Gao [1 ,2 ,3 ]
Liao, Jie-Qiao [1 ,2 ]
Miranowicz, Adam [3 ,4 ]
Nori, Franco [3 ,5 ]
机构
[1] Hunan Normal Univ, Dept Phys, Key Lab Matter Microstruct & Funct Hunan Prov, Minist Educ,Key Lab Low Dimens Quantum Struct & Q, Changsha 410081, Peoples R China
[2] Hunan Normal Univ, Synerget Innovat Ctr Quantum Effects & Applicat, Changsha 410081, Peoples R China
[3] RIKEN Cluster Pioneering Res, Theoret Quantum Phys Lab, Wako, Saitama 3510198, Japan
[4] Adam Mickiewicz Univ, Fac Phys, Inst Spintron & Quantum Informat, PL-61614 Poznan, Poland
[5] Univ Michigan, Phys Dept, Ann Arbor, MI 48109 USA
基金
中国国家自然科学基金; 日本科学技术振兴机构; 日本学术振兴会;
关键词
QUANTUM ENTANGLEMENT; GROUND-STATE; PHOTONS; PHONONS; ATOM; MIRRORS; SYSTEM; MOTION; LIGHT;
D O I
10.1103/PhysRevLett.129.063602
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Entanglement of light and multiple vibrations is a key resource for multichannel quantum information processing and memory. However, entanglement generation is generally suppressed, or even fully destroyed, by the dark-mode (DM) effect induced by the coupling of multiple degenerate or near -degenerate vibrational modes to a common optical mode. Here we propose how to generate optomechanical entanglement via DM breaking induced by synthetic magnetism. We find that at nonzero temperature, light and vibrations are separable in the DM-unbreaking regime but entangled in the DM-breaking regime. Remarkably, the threshold thermal phonon number for preserving entanglement in our simulations has been observed to be up to 3 orders of magnitude stronger than that in the DM-unbreaking regime. The application of the DM-breaking mechanism to optomechanical networks can make noise-tolerant entanglement networks feasible. These results are quite general and can initiate advances in quantum resources with immunity against both dark modes and thermal noise.
引用
收藏
页数:8
相关论文
共 50 条
  • [31] Digital Noise-tolerant Silicon Nanophotonic Switch
    Van Campenhout, J.
    Green, W. M. J.
    Assefa, S.
    Vlasov, Y. A.
    2010 CONFERENCE ON LASERS AND ELECTRO-OPTICS (CLEO) AND QUANTUM ELECTRONICS AND LASER SCIENCE CONFERENCE (QELS), 2010,
  • [32] Noise-tolerant wavefront shaping in a Hadamard basis
    Mastiani, Bahareh
    Vellekoop, Ivo M.
    OPTICS EXPRESS, 2021, 29 (11): : 17534 - 17541
  • [33] DatRel: a noise-tolerant data relocation approach for effective synthetic data generation in imbalanced classifiers
    Saglam, Fatih
    MACHINE LEARNING, 2025, 114 (05)
  • [34] Optomechanical entanglement in the presence of laser phase noise
    Ghobadi, R.
    Bahrampour, A. R.
    Simon, C.
    PHYSICAL REVIEW A, 2011, 84 (06):
  • [35] Noise-Tolerant Wireless Sensor Networks Localization via Multinorms Regularized Matrix Completion
    Xiao, Fu
    Liu, Wei
    Li, Zhetao
    Chen, Lei
    Wang, Ruchuan
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (03) : 2409 - 2419
  • [36] Generalized non-reciprocity in an optomechanical circuit via synthetic magnetism and reservoir engineering
    Fang, Kejie
    Luo, Jie
    Metelmann, Anja
    Matheny, Matthew H.
    Marquardt, Florian
    Clerk, Aashish A.
    Painter, Oskar
    NATURE PHYSICS, 2017, 13 (05) : 465 - 471
  • [37] Generalized non-reciprocity in an optomechanical circuit via synthetic magnetism and reservoir engineering
    Fang K.
    Luo J.
    Metelmann A.
    Matheny M.H.
    Marquardt F.
    Clerk A.A.
    Painter O.
    Nature Physics, 2017, 13 (5) : 465 - 471
  • [38] HyperMatch: Noise-Tolerant Semi-Supervised Learning via Relaxed Contrastive Constraint
    Zhou, Beitong
    Lu, Jing
    Liu, Kerui
    Xu, Yunlu
    Cheng, Zhanzhan
    Niu, Yi
    2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2023, : 24017 - 24026
  • [39] Noise-tolerant stimulus discrimination by synchronization with depressing synapses
    Tomoki Fukai
    Seinichi Kanemura
    Biological Cybernetics, 2001, 85 : 107 - 116
  • [40] Noise-Tolerant Interactive Learning Using Pairwise Comparisons
    Xu, Yichong
    Zhang, Hongyang
    Miller, Kyle
    Singh, Aarti
    Dubrawski, Artur
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), 2017, 30