.png){kind=icon}
返回
Quantum Support Vector Machine for Classifying Noisy Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TC.2024.3416619.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Noisy data is ubiquitous in quantum computer, greatly affecting the performance of various algorithms. However, existing quantum support vector machine models are not equipped with anti-noise ability, and often deliver low performance when learning accurate hyperplane normal vectors from noisy data. To attack this issue, an anti-noise quantum support vector machine algorithm is developed in this paper. Specifically, a weight factor is first embedded into the hinge loss, so as to construct the objective function of anti-noise support vector machine. And then, an alternative iterative optimization strategy and a quantum circuit are designed for solving the objective function, aiming to obtain the normal vector and intercept of the hyperplane that finally divides the data. Finally, the classification and anti-noise effect of the algorithm are verified on artificial dataset and public dataset. Experimental results show that the proposed algorithm is efficient, yet maintains stable accuracy in noisy data.
Keyword:
Support vector machines
Quantum computing
Classification algorithms
Accuracy
Quantum entanglement
Computers
Vectors
Quantum support vector machine
quantum computer
noisy data
classification
期刊
IF:
3.8
论文数:
5.4K
被引数:
9.8K
机构
引用论文
.png){kind=icon}
Seasonal variations of nitrogen and digestible energy intake of cattle on tropical pasture热带牧场牛的氮和可消化能量采食量的季节性变化
PREAI
分享收藏
分享收藏
Inhibitor(s) of prostaglandin biosynthesis in extracts of oat (Avena sativa) seeds燕麦(Avena sativa)种子提取物中的前列腺素生物合成抑制剂
PREAI
分享收藏
查看更多内容