期刊信息
- 刊名： 河北师范大学学报（自然科学版）Journal of Hebei Normal University (Natural Science)
- 主办： 河北师范大学
- ISSN： 1000-5854
- CN： 13-1061/N
- 中国科技核心期刊
- 中国期刊方阵入选期刊
- 中国高校优秀科技期刊
- 华北优秀期刊
- 河北省优秀科技期刊
基于量子支持向量机模型的 AI生成图像识别研究
- (1.应急管理大学 理学院，河北 廊坊 065201; 2.脑机接口技术应用应急管理部重点实验室，河北 廊坊 065201)
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DOI：10.13763/j.cnki.jhebnu.nse.202601009
Research of recognition of AI-generated images based on quantum support vector machine model
摘要/Abstract
随着量子技术的发展，量子机器学习在图像识别领域中有着巨大的应用潜力.聚焦于量子支持向量机在识别人工智能（artificial intelligence，AI）生成图像这一具体问题上的应用，在有限的量子比特条件下，探索主成分分析特征降维和超参数调优对量子支持向量机算法评价指标的影响.结果表明，与经典支持向量机相比，应用量子支持向量机明显提升了准确率、精确率、召回率和F1分数；量子支持向量机算法特征降维效果显著，并且对超参数的选择表现出令人满意的鲁棒性.
With the development of quantum technology,quantum machine learning holds immense potential for applications in the field of image recognition.This paper focuses on the application of quantum support vector machines for the specific task of identifying AI-generated images.For a limited number of qubits,we explored the impact of principal component analysis for feature dimensionality reduction and hyperparameter tuning on the performance metrics of the quantum support vector machine algorithm.The results indicate that,compared to classical support vector machines,the application of quantum support vector machines significantly improved accuracy,precision,recall,and F1 scores.Furthermore,the feature dimensionality reduction for the quantum support vector machine algorithm proved to be highly effective,and the quantum algorithm demonstrated satisfactory robustness to the selection of hyperparameters.
关键词
参考文献 12
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