High-efficiency entanglement of microwave fields in cavity opto-magnomechanical systems
Ke Di, Shuai Tan, Liyong Wang, Anyu Cheng, Xi Wang, Yu Liu, Jiajia Du

TL;DR
This paper proposes a scheme to generate high-efficiency entangled microwave fields in a dual opto-magnomechanical system, demonstrating robust entanglement transfer from optical to microwave modes with potential quantum technology applications.
Contribution
It introduces a novel method to produce and transfer entanglement in microwave fields using magnons and optical cavities, achieving high efficiency and robustness.
Findings
Stationary entanglement E_{a_{1}a_{2}}=0.54 at r=1
Entanglement increases with squeezing parameter r
Entanglement survives up to 385 mK environmental temperature
Abstract
We demonstrate a scheme to realize high-efficiency entanglement of two microwave fields in a dual opto-magnomechanical system. The magnon mode simultaneously couples with the microwave cavity mode and phonon mode via magnetic dipole interaction and magnetostrictive interaction, respectively. Meanwhile, the phonon mode couples with the optical cavity mode via radiation pressure. Each magnon mode and optical cavity mode adopts a strong red detuning driving field to activate the beam splitter interaction. Therefore, the entangled state generated by the injected two-mode squeezed light in optical cavities can be eventually transferred into two microwave cavities. A stationary entanglement E_{a_{1}a_{2}}=0.54 is obtained when the input two-mode squeezed optical field has a squeezing parameter r=1. The entanglement E_{a_{1}a_{2}} increases as the squeezing parameter r increases, and it shows…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMechanical and Optical Resonators · Quantum Information and Cryptography · Neural Networks and Reservoir Computing
