Telecom Quantum Photonic Interface for a 40ca+ Single-Ion Quantum Memory

Elena Arenskoetter,Tobias Bauer,Stephan Kucera,Matthias Bock,Juergen Eschner,Christoph Becher

NPJ QUANTUM INFORMATION（2023）

Cited 10|Views15

Abstract

Entanglement-based quantum networks require quantum photonic interfaces between stationary quantum memories and photons, enabling entanglement distribution. Here we present such a photonic interface, designed for connecting a 40Ca+ single-ion quantum memory to the telecom C-band. The interface combines a memory-resonant, cavity-enhanced spontaneous parametric down-conversion photon pair source with bi-directional polarization-conserving quantum frequency conversion. We demonstrate preservation of high-fidelity entanglement during conversion, fiber transmission over up to 40 km and back-conversion to the memory wavelength. Even for the longest distance and bi-directional conversion the entanglement fidelity remains larger than 95% (98%) without (with) background correction.

Translated text

Key words

Quantum optics,Single photons and quantum effects,Physics,general,Quantum Physics,Quantum Information Technology,Spintronics,Quantum Computing,Quantum Field Theories,String Theory,Classical and Quantum Gravitation,Relativity Theory

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper