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Quantum machine learning (QML) based on Noisy Intermediate-Scale Quantum (NISQ) devices hinges on the optimal utilization of limited quantum resources.
“Almost any quantum logic gate is universal”
Seth Lloyd · 1995
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“Notions of controllability for bilinear multilevel quantum systems”
Francesca Albertini and Domenico D’Alessandro · 2003
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“Control of inhomogeneous quantum ensembles”
Jr-Shin Li and Navin Khaneja · 2006
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“Adam: A method for stochastic optimization” (2014)
Diederik P Kingma and Jimmy Ba · 2014
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“Driven quantum dynamics: Will it blend?”
Leonardo Banchi, Daniel Burgarth, and Michael J Kastoryano · 2017
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“Classification with quantum neural networks on near term processors” (2018)
Edward Farhi and Hartmut Neven · 2018
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“Barren plateaus in quantum neural network training landscapes”
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
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“Quantum supremacy using a programmable superconducting processor”
Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, et al · 2019
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“Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms”
Sukin Sim, Peter D Johnson, and Alán Aspuru-Guzik · 2019
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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
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“Quantum machine learning in feature hilbert spaces”
Maria Schuld and Nathan Killoran · 2019
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“Quantum computational advantage using photons”
Han-Sen Zhong, Hui Wang, Yu-Hao Deng, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Jian Qin, Dian Wu, Xing Ding, Yi Hu, et al · 2020
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“End-to-end quantum machine learning implemented with controlled quantum dynamics”
Re-Bing Wu, Xi Cao, Pinchen Xie, and Yu-xi Liu · 2020
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“Expressive power of parametrized quantum circuits”
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, and Dacheng Tao · 2020
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“On the universality of the quantum approximate optimization algorithm”
Mauro ES Morales, Jacob D Biamonte, and Zoltán Zimborás · 2020
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“Pseudo-dimension of quantum circuits”
Matthias C Caro and Ishaun Datta · 2020
Cited alongside, same era.
“The capacity of quantum neural networks”
Logan G Wright and Peter L McMahon · 2020
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“Data re-uploading for a universal quantum classifier”
Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre · 2020
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“Optimized quantum compilation for near-term algorithms with openpulse”
Pranav Gokhale, Ali Javadi-Abhari, Nathan Earnest, Yunong Shi, and Frederic T Chong · 2020
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“Hybrid quantum-classical algorithms and quantum error mitigation”
Suguru Endo, Zhenyu Cai, Simon C Benjamin, and Xiao Yuan · 2021
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“Universal variational quantum computation”
Jacob Biamonte · 2021
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“Quantum computational advantage via 60-qubit 24-cycle random circuit sampling”
Qingling Zhu, Sirui Cao, Fusheng Chen, Ming-Cheng Chen, Xiawei Chen, Tung-Hsun Chung, Hui Deng, Yajie Du, Daojin Fan, Ming Gong, et al · 2022
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“Quantum advantage in learning from experiments”
Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, et al · 2022
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“Hybrid quantum-classical algorithms in the noisy intermediate-scale quantum era and beyond”
Adam Callison and Nicholas Chancellor · 2022
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“Variational quantum pulse learning”
Zhiding Liang, Hanrui Wang, Jinglei Cheng, Yongshan Ding, Hang Ren, Zhengqi Gao, Zhirui Hu, Duane S Boning, Xuehai Qian, Song Han, et al · 2022
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“Evaluation of parameterized quantum circuits with cross-resonance pulse-driven entanglers”
Mohannad M Ibrahim, Hamed Mohammadbagherpoor, Cynthia Rios, Nicholas T Bronn, and Gregory T Byrd · 2022
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“Universal discriminative quantum neural networks”
Hongxiang Chen, Leonard Wossnig, Simone Severini, Hartmut Neven, and Masoud Mohseni · 2021
Cited alongside, same era.
“Effect of data encoding on the expressive power of variational quantum-machine-learning models”
Maria Schuld, Ryan Sweke, and Johannes Jakob Meyer · 2021
Cited alongside, same era.
“The power of quantum neural networks”
Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, and Stefan Woerner · 2021
Cited alongside, same era.
“Supervised quantum machine learning models are kernel methods” (2021)
Maria Schuld · 2021
Cited alongside, same era.
“Universal approximation property of quantum machine learning models in quantum-enhanced feature spaces”
Takahiro Goto, Quoc Hoan Tran, and Kohei Nakajima · 2021
Cited alongside, same era.
“Expressivity of quantum neural networks”
Yadong Wu, Juan Yao, Pengfei Zhang, and Hui Zhai · 2021
Cited alongside, same era.
“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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“Fock state-enhanced expressivity of quantum machine learning models”
Beng Yee Gan, Daniel Leykam, and Dimitris G Angelakis · 2022
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“Diagnosing barren plateaus with tools from quantum optimal control”
Martin Larocca, Piotr Czarnik, Kunal Sharma, Gopikrishnan Muraleedharan, Patrick J Coles, and Marco Cerezo · 2022
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“Power and limitations of single-qubit native quantum neural networks”
Zhan Yu, Hongshun Yao, Mujin Li, and Xin Wang · 2022
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“Effects of dynamical decoupling and pulse-level optimizations on ibm quantum computers”
Siyuan Niu and Aida Todri-Sanial · 2022
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“Experimental quantum end-to-end learning on a superconducting processor”
Xiaoxuan Pan, Xi Cao, Weiting Wang, Ziyue Hua, Weizhou Cai, Xuegang Li, Haiyan Wang, Jiaqi Hu, Yipu Song, Dong-Ling Deng, et al · 2023
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“Pulse-efficient quantum machine learning”
André Melo, Nathan Earnest-Noble, and Francesco Tacchino · 2023
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“Hybrid gate-pulse model for variational quantum algorithms”
Zhiding Liang, Zhixin Song, Jinglei Cheng, Zichang He, Ji Liu, Hanrui Wang, Ruiyang Qin, Yiru Wang, Song Han, Xuehai Qian, et al · 2023
Later among the works it cites.