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Experimental Measurement of the Hilbert-Schmidt Distance between Two-Qubit States as a Means for Reducing the Complexity of Machine Learning

机译：双时态态之间的Hilbert-Schmidt距离的实验测量作为降低机器学习复杂性的手段

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Vojtěch Trávníček; Karol Bartkiewicz; Antonín Černoch; Karel Lemr;
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年度 2019
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1. Experimental Measurement of the Hilbert-Schmidt Distance between Two-Qubit States as a Means for Reducing the Complexity of Machine Learning [J] . Travnicek Vojtech, Bartkiewicz Karol, Cernoch Antonin, Physical review letters . 2019,第26期

机译：双时态态之间的Hilbert-Schmidt距离的实验测量作为降低机器学习复杂性的手段
2. Multiple-measurement vector based implementation for single-measurement vector sparse Bayesian learning with reduced complexity [J] . Jian A. Zhang, Zhuo Chen, Peng Cheng, Signal processing . 2016,第jana期

机译：基于多次测量矢量的单测量矢量稀疏贝叶斯学习的实现，具有降低的复杂度
3. Reducing the complexity of distance measurement methods for circular turbo codes that use structured interleavers [J] . Ould-Cheikh-Mouhamedou Youssouf International journal of communication systems . 2015,第9期

机译：降低使用结构化交织器的圆形Turbo码的距离测量方法的复杂性
4. Learning pressures reduce morphological complexity: Linking corpus, computational and experimental evidence [C] . Christian Bentz, Aleksandrs Berdicevskis Computational linguistics for linguistic complexity . 2016

机译：学习压力降低了形态学的复杂性：链接语料库，计算和实验证据
5. Techniques for performing real-time antenna measurements at reduced distances. [D] . McKay, James Patrick. 1994

机译：用于以缩小的距离执行实时天线测量的技术。
6. LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data [O] . Li Huang, Yifeng Yin, Zeng Fu, 2020

机译：Loadaboost：基于丢失的Adaboost联合机器学习具有降低IID和非IID重症监护数据的计算复杂性
7. Inverse-Free Incremental Learning Algorithms With Reduced Complexity for Regularized Extreme Learning Machine [O] . Hufei Zhu, Yanpeng Wu 2020

机译：无逆增量学习算法，对正则极端学习机的复杂性降低

Experimental Measurement of the Hilbert-Schmidt Distance between Two-Qubit States as a Means for Reducing the Complexity of Machine Learning

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