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Parameterized quantum circuits (PQCs) have emerged as a promising approach for quantum neural networks.
The Generalized Weierstrass Approximation Theorem
M. H. Stone · 1948
Earlier work this paper cites.
Necessary and Sufficient Conditions for the Uniform Convergence of Means to their Expectations
V. N. Vapnik and A. Ya. Chervonenkis · 1982
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
Optimal nonlinear approximation
Ronald A. DeVore, Ralph Howard, and Charles Micchelli · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
A.R. Barron · 1993
Earlier work this paper cites.
ϵ \epsilon -Entropy and ϵ \epsilon -Capacity of Sets In Functional Spaces
V. M. Tikhomirov · 1993
Earlier work this paper cites.
Quantum Amplitude Amplification and Estimation
Gilles Brassard, Peter Hoyer, Michele Mosca, and Alain Tapp · 2002
Earlier work this paper cites.
Real analysis with real applications
Kenneth R. Davidson and Allan P. Donsig · 2002
Earlier work this paper cites.
Simulation and Inverse Modeling of Semiconductor Manufacturing Processes
Clemens Heitzinger · 2002
Earlier work this paper cites.
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
Peter L. Bartlett and Shahar Mendelson · 2003
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Quantum computation and quantum information
Michael A Nielsen and Isaac L Chuang · 2010
Earlier work this paper cites.
Hamiltonian simulation using linear combinations of unitary operations
Andrew M. Childs and Nathan Wiebe · 2012
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Methodology of Resonant Equiangular Composite Quantum Gates
Guang Hao Low, Theodore J. Yoder, and Isaac L. Chuang · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
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Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M. Chow, and Jay M. Gambetta · 2017
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Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky · 2017
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Optimal Hamiltonian Simulation by Quantum Signal Processing
Guang Hao Low and Isaac L. Chuang · 2017
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Quantum Signal Processing by Single-Qubit Dynamics
Guang Hao Low · 2017
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Quantum Computing in the NISQ era and beyond
John Preskill · 2018
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Quantum Circuit Learning
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii · 2018
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Optimal approximation of continuous functions by very deep ReLU networks
Dmitry Yarotsky · 2018
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Optimal approximation of piecewise smooth functions using deep ReLU neural networks
Philipp Petersen and Felix Voigtlaender · 2018
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Quantum chemistry in the age of quantum computing
Yudong Cao, Jonathan Romero, Jonathan P. Olson, Matthias Degroote, Peter D. Johnson, Mária Kieferová, Ian D. Kivlichan, Tim Menke, Borja Peropadre, Nicolas P. D. Sawaya, Sukin Sim, Libor Veis, and Alán Aspuru-Guzik · 2019
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One qubit as a universal approximant
Adrián Pérez-Salinas, David López-Núñez, Artur García-Sáez, P. Forn-Díaz, and José I. Latorre · 2021
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Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces
Takahiro Goto, Quoc Hoan Tran, and Kohei Nakajima · 2021
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The power of quantum neural networks
Amira Abbas, David Sutter, Christa Zoufal, Aurelien Lucchi, Alessio Figalli, and Stefan Woerner · 2021
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Experimental quantum adversarial learning with programmable superconducting qubits
Wenhui Ren, Weikang Li, Shibo Xu, Ke Wang, Wenjie Jiang, Feitong Jin, Xuhao Zhu, Jiachen Chen, Zixuan Song, Pengfei Zhang, Hang Dong, Xu Zhang, Jinfeng Deng, Yu Gao, Chuanyu Zhang, Yaozu Wu, Bing Zhang, Qiujiang Guo, Hekang Li, Zhen Wang, Jacob Biamonte, Chao Song, Dong-Ling Deng, and H. Wang · 2022
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The barron space and the flow-induced function spaces for neural network models
E Weinan, Chao Ma, and Lei Wu · 2022
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Supervised learning with quantum-enhanced feature spaces
Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta · 2019
Cited alongside, same era.
Quantum singular value transformation and beyond: Exponential improvements for quantum matrix arithmetics
András Gilyén, Yuan Su, Guang Hao Low, and Nathan Wiebe · 2019
Cited alongside, same era.
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Data re-uploading for a universal quantum classifier
Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre · 2020
Cited alongside, same era.
The phase diagram of approximation rates for deep neural networks
Dmitry Yarotsky and Anton Zhevnerchuk · 2020
Cited alongside, same era.
Power and limitations of single-qubit native quantum neural networks
Zhan Yu, Hongshun Yao, Mujin Li, and Xin Wang · 2022
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Mathematical aspects of deep learning
Philipp Grohs and Gitta Kutyniok · 2022
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Linear-depth quantum circuits for multiqubit controlled gates
Adenilton J. da Silva and Daniel K. Park · 2022
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Efficient Measure for the Expressivity of Variational Quantum Algorithms
Yuxuan Du, Zhuozhuo Tu, Xiao Yuan, and Dacheng Tao · 2022
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Statistical complexity of quantum circuits
Kaifeng Bu, Dax Enshan Koh, Lu Li, Qingxian Luo, and Yaobo Zhang · 2022
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General Vapnik–Chervonenkis dimension bounds for quantum circuit learning
Chih-Chieh Chen, Masaru Sogabe, Kodai Shiba, Katsuyoshi Sakamoto, and Tomah Sogabe · 2022
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The Barron Space and the Flow-Induced Function Spaces for Neural Network Models
Weinan E, Chao Ma, and Lei Wu · 2022
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Parametrized Quantum Circuits and their approximation capacities in the context of quantum machine learning, July 2023
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Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines
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Variational quantum state eigensolver
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Theoretical error performance analysis for variational quantum circuit based functional regression
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, and Min-Hsiu Hsieh · 2056
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Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2058
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