Zobrazeno 1 - 10
of 305
pro vyhledávání: '"Zhang, KaiNing"'
Quantum machine learning, which involves running machine learning algorithms on quantum devices, may be one of the most significant flagship applications for these devices. Unlike its classical counterparts, the role of data in quantum machine learni
Externí odkaz:
http://arxiv.org/abs/2408.09937
Despite remarkable successes in solving various complex decision-making tasks, training an imitation learning (IL) algorithm with deep neural networks (DNNs) suffers from the high computation burden. In this work, we propose quantum imitation learnin
Externí odkaz:
http://arxiv.org/abs/2304.02480
Autor:
Zhang, Kaining1 (AUTHOR), Yang, Yun1 (AUTHOR), Yu, Wen2 (AUTHOR), Qi, Yubin1 (AUTHOR), Ren, Yanjun1 (AUTHOR), Wu, Yingguang1 (AUTHOR), Shan, Wa1 (AUTHOR), Zhu, Fengxiang3 (AUTHOR), Chen, Feifei1 (AUTHOR) chenfeifei733733@126.com
Publikováno v:
Pain & Therapy. Dec2024, Vol. 13 Issue 6, p1559-1570. 12p.
Autor:
Li, Renjie, Wang, Xinyi, Huang, Guan, Yang, Wenli, Zhang, Kaining, Gu, Xiaotong, Tran, Son N., Garg, Saurabh, Alty, Jane, Bai, Quan
Deep supervision, or known as 'intermediate supervision' or 'auxiliary supervision', is to add supervision at hidden layers of a neural network. This technique has been increasingly applied in deep neural network learning systems for various computer
Externí odkaz:
http://arxiv.org/abs/2207.02376
Autor:
Tian, Jinkai, Sun, Xiaoyu, Du, Yuxuan, Zhao, Shanshan, Liu, Qing, Zhang, Kaining, Yi, Wei, Huang, Wanrong, Wang, Chaoyue, Wu, Xingyao, Hsieh, Min-Hsiu, Liu, Tongliang, Yang, Wenjing, Tao, Dacheng
Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is through quantum machine learning, especially quantum generative learning.
Externí odkaz:
http://arxiv.org/abs/2206.03066
Publikováno v:
In Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 5 March 2025 328
Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poor trainability due to the exponentially vanishing gradient with respec
Externí odkaz:
http://arxiv.org/abs/2203.09376
Quantum Neural Networks (QNNs) with random structures have poor trainability due to the exponentially vanishing gradient as the circuit depth and the qubit number increase. This result leads to a general belief that a deep QNN will not be feasible. I
Externí odkaz:
http://arxiv.org/abs/2112.15002
Publikováno v:
In Materials & Design September 2024 245
Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up. Despite the potential to outperform classical models, serious bottlenecks exist for training QNNs; namely, QNN
Externí odkaz:
http://arxiv.org/abs/2011.06258