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Quantum machine learning
Optimizer-Dependent Generalization Bound for Quantum Neural Networks
Quantum neural networks (QNNs) play a pivotal role in addressing complex tasks within quantum machine learning, analogous to classical …
Chenghong Zhu
,
Hongshun Yao
,
Yingjian Liu
,
Xin Wang
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DOI
Provable Advantage of Parameterized Quantum Circuit in Function Approximation
Parameterized quantum circuits (PQCs) have emerged as a promising approach for quantum neural networks. However, understanding their …
Zhan Yu
,
Qiuhao Chen
,
Yuling Jiao
,
Yinan Li
,
Xiliang Lu
,
Xin Wang
,
Jerry Zhijian Yang
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Predicting quantum learnability from landscape fluctuation
The conflict between trainability and expressibility is a key challenge in variational quantum computing and quantum machine learning. …
Hao-Kai Zhang
,
Chenghong Zhu
,
Xin Wang
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DOI
Quantum Sequential Scattering Model for Quantum State Learning
Learning probability distribution is an essential framework in classical learning theory. As a counterpart, quantum state learning has …
Mingrui Jing
,
Geng Liu
,
Hongbin Ren
,
Xin Wang
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DOI
Mitigating Barren Plateaus of Variational Quantum Eigensolvers
Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent …
Xia Liu
,
Geng Liu
,
Hao-Kai Zhang
,
Jiaxin Huang
,
Xin Wang
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DOI
Quantum self-attention neural networks for text classification
An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial …
Guangxi Li
,
Xuanqiang-Zhao
,
Xin Wang
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DOI
Exponential Hardness of Optimization from the Locality in Quantum Neural Networks
Quantum neural networks (QNNs) have become a leading paradigm for establishing near-term quantum applications in recent years. The …
Hao-Kai Zhang
,
Chengkai Zhu
,
Geng Liu
,
Xin Wang
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DOI
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Quantum neural networks (QNNs) have been a promising framework in pursuing near-term quantum advantage in various fields, where many …
Hao-Kai Zhang
,
Chenghong Zhu
,
Mingrui Jing
,
Xin Wang
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