General Controlled Cyclic Remote State Preparations and Their Analysis
Quantum Information Processing(2024)
Imam Reza International University
Abstract
Remote state preparation (RSP) is a method to transfer a known state from a sender to a receiver some distance away. Based on its importance, many kinds of RSP have been proposed in the literature. In this paper, the controlled cyclic RSP protocol is extended to an arbitrary n number of parties. To accomplish this goal, two protocols are proposed and compared. The first one is based on a 2n+1 entangled state as a channel, and the other is with 2n EPR states. Then, the proposed protocols are analyzed from the controller power’s point of view, and the improved versions are presented. Finally, the protocols have been proposed to send the states with an arbitrary number of qubits. Furthermore, the performance of the protocol is analyzed in the noisy environments.
MoreTranslated text
Key words
Remote state preparation,Controller,EPR state,Entanglement,Noise
求助PDF
上传PDF
View via Publisher
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
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined