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Recurrent quantum embedding neural network and its application in vulnerability detection paper by Independent / Community
⭐ 62.3
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Paper

Recurrent quantum embedding neural network and its application in vulnerability detection

by Independent / Community 02300c55c3f95879f73ea7e1e3cfca2538ae18bb
Free2AITools Nexus Index
62.3
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 68
P: Popularity 43
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and language. Since NLP usually consumes a lot of computing resources, its combination with quantum computing is becoming a valuable research direction. In this paper, we present a Recurrent Quantum Embedding Neural Network (RQENN) for vulnerability detection. It aims to reduc...

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Semantic Scholar 3 Citations
Paper Information Summary
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Registry ID 02300c55c3f95879f73ea7e1e3cfca2538ae18bb
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{02300c55c3f95879f73ea7e1e3cfca2538ae18bb,
  author = {Unknown},
  title = {Recurrent quantum embedding neural network and its application in vulnerability detection Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/02300c55c3f95879f73ea7e1e3cfca2538ae18bb}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Recurrent quantum embedding neural network and its application in vulnerability detection [Paper]. Free2AITools. https://api.semanticscholar.org/02300c55c3f95879f73ea7e1e3cfca2538ae18bb

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 68
Popularity (P) 43
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Recurrent quantum embedding neural network and its application in vulnerability detection: Authority (A:68), Popularity (P:43), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and language. Since NLP usually consumes a lot of computing resources, its combination with quantum computing is becoming a valuable research direction. In this paper, we present a Recurrent Quantum Embedding Neural Network (RQENN) for vulnerability detection. It aims to reduc..."

❝ Cite Node 

@article{Unknown2026Recurrent,
  title={Recurrent quantum embedding neural network and its application in vulnerability detection},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ3CitationsSemantic Scholar
πŸ›οΈ68AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈknowledge retrievalField

🏷️ Research Topics

embeddings
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semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

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params billions
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