Analysis of Influencing Factors of CBOW Model in Natural Language Processing Based on Quantum Neural Network.

Meng Zhang, Jian Kang, Bing Han, Qian Wu

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Meng ZhangDivision of Quantum Information Technology Standardization, China National Institute of Standardization, Beijing 100191, China.

Jian KangDivision of Quantum Information Technology Standardization, China National Institute of Standardization, Beijing 100191, China.

Bing HanDivision of Quantum Information Technology Standardization, China National Institute of Standardization, Beijing 100191, China.

Qian WuDivision of Quantum Information Technology Standardization, China National Institute of Standardization, Beijing 100191, China.

Funding

China National Institute of Standardization 552025Y-12625-1

6 · The paper itself

Abstract

To address the problems of the limited feature extraction capability and insufficient training efficiency of the traditional Continuous Bag-of-Words (CBOW) model in Natural Language Processing (NLP), the Quantum Neural Network-enhanced CBOW model (QNN-CBOW) integrates Quantum Neural Networks (QNN) with the CBOW model, effectively enhancing training performance. This work aims to systematically investigate the sensitivity and influence patterns of key factors (activation function type, number of quantum feature extraction layers, context window size, and quantum gate noise level) on model behavior under controlled small-scale simulation conditions. Comparative experiments are carried out using the control variable method to clarify the influence mechanism of each factor. This paper presents a NISQ-era proof-of-concept study, which provides a theoretical basis and practical reference for the fusion and optimization of quantum neural networks and traditional NLP models.

Indexed as

CBOW modelinfluencing factornatural language processingperformance optimizationquantum circuitquantum neural network

Identifiers

PMID42352123

DOI10.3390/e28060613

PMCPMC13297788

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.