分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： A quantum network consisting of magnonic and mechanical nodes connected by light is proposed. Recent years have witnessed a significant development in cavity magnonics based on collective spin excitations in ferrimagnetic crystals, such as yttrium iron garnet (YIG). Magnonic systems are considered to be a promising building block for a future quantum network. However, a major limitation of the system is that the coherence time of the magnon excitations is limited by their intrinsic loss (typically in the order of 1 $\mu$s for YIG). Here, we show that by coupling the magnonic system to a mechanical system using optical pulses, an arbitrary magnonic state (either classical or quantum) can be transferred to and stored in a distant long-lived mechanical resonator. The fidelity depends on the pulse parameters and the transmission loss. We further show that the magnonic and mechanical nodes can be prepared in a macroscopic entangled state. These demonstrate the quantum state transfer and entanglement distribution in such a novel quantum network of magnonic and mechanical nodes. Our work shows the possibility to connect two separate fields of optomagnonics and optomechanics, and to build a long-distance quantum network based on magnonic and mechanical systems.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
Weiru Fan
Tianrun Chen
Xingqi Xu
Ziyang Chen
Huizhu Hu
Delong Zhang
Da-Wei Wang
Jixiong Pu
Shi-Yao Zhu
摘要： Optical phase contains key information for biomedical and astronomical imaging. However, it is often obscured by layers of heterogeneous and scattering media, which render optical phase imaging at different depths an utmost challenge. Limited by the memory effect, current methods for phase imaging in strong scattering media are inapplicable to retrieving phases at different depths. To address this challenge, we developed a speckle three-dimensional reconstruction network (STRN) to recognize phase objects behind scattering media, which circumvents the limitations of memory effect. From the single-shot, reference-free and scanning-free speckle pattern input, STRN distinguishes depth-resolving quantitative phase information with high fidelity. Our results promise broad applications in biomedical tomography and endoscopy.
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