WeChat Mini Program
Old Version Features

Discrete-time Walk on One-Dimensional Lattice under Stochastic Resetting: Advantage of Quantum over Classical Scenario.

Przemysław Chełminiak,Jan Wójcik, Antoni Wójcik

Physical review E(2025)

Cited 0|Views0
Abstract
We investigate the effect of stochastic restart/resetting on discrete-time quantum walks concerning its impact on the meantime to absorption in the walk on the one-dimensional lattice terminated by a pair of totally absorbing sites. The two distinguished sites also play the role of an apparatus that detects the moment of time when a quantum walk reaches these boundaries. Our results reveal a notable difference between the quantum walk and its classical counterpart. Specifically, we focus on instances of classical random walks in which stochastic resetting fails to reduce the meantime to absorption. Conversely, we show that stochastic resetting can be utilized to effectively diminish the meantime to absorption in the quantum version of this walk. We relate the observed difference between quantum and classical cases to the fact that restarting the quantum state not only changes position distribution but inevitably changes complementary quasimomentum distribution.
More
Translated text
求助PDF
上传PDF
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
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