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Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices.
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Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2019
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Learnability of quantum neural networks
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, and Dacheng Tao · 2021
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Cost function dependent barren plateaus in shallow parametrized quantum circuits
Marco Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J Coles · 2021
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Noise-induced barren plateaus in variational quantum algorithms
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Learning-based quantum error mitigation
Armands Strikis, Dayue Qin, Yanzhu Chen, Simon C Benjamin, and Ying Li · 2021
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Error mitigation with clifford quantum-circuit data
Piotr Czarnik, Andrew Arrasmith, Patrick J Coles, and Lukasz Cincio · 2021
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Structure optimization for parameterized quantum circuits
Mateusz Ostaszewski, Edward Grant, and Marcello Benedetti · 2021
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A domain-agnostic, noise-resistant, hardware-efficient evolutionary variational quantum eigensolver
Arthur G Rattew, Shaohan Hu, Marco Pistoia, Richard Chen, and Steve Wood · 2019
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An adaptive variational algorithm for exact molecular simulations on a quantum computer
Harper R Grimsley, Sophia E Economou, Edwin Barnes, and Nicholas J Mayhall · 2019
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Training deep quantum neural networks
Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J Osborne, Robert Salzmann, Daniel Scheiermann, et al · 2020
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Quantum natural gradient
James Stokes, Josh Izaac, Nathan Killoran, and Giuseppe Carleo · 2020
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Hartree-fock on a superconducting qubit quantum computer
Google AI Quantum et al · 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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Layerwise learning for quantum neural networks
Andrea Skolik, Jarrod R McClean, Masoud Mohseni, Patrick van der Smagt, and Martin Leib · 2021
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Toward trainability of deep quantum neural networks
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu, and Dacheng Tao · 2021
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Training variational quantum algorithms is np-hard
Lennart Bittel and Martin Kliesch · 2021
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Entanglement-induced barren plateaus
Carlos Ortiz Marrero, Mária Kieferová, and Nathan Wiebe · 2021
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Capacity and quantum geometry of parametrized quantum circuits
Tobias Haug, Kishor Bharti, and M.S. Kim · 2021
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Power of data in quantum machine learning
Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, et al · 2021
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A grover-search based quantum learning scheme for classification
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, and Dacheng Tao · 2021
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Towards understanding the power of quantum kernels in the nisq era
Xinbiao Wang, Yuxuan Du, Yong Luo, and Dacheng Tao · 2021
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Reinforcement learning for many-body ground-state preparation inspired by counterdiabatic driving
Jiahao Yao, Lin Lin, and Marin Bukov · 2021
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Greedynasv2: Greedier search with a greedy path filter
Tao Huang, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, et al · 2021
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Large gradients via correlation in random parameterized quantum circuits
Tyler Volkoff and Patrick J Coles · 2021
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qubit-adapt-vqe: An adaptive algorithm for constructing hardware-efficient ansätze on a quantum processor
Ho Lun Tang, VO Shkolnikov, George S Barron, Harper R Grimsley, Nicholas J Mayhall, Edwin Barnes, et al · 2021
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Abrupt transitions in variational quantum circuit training
Ernesto Campos, Aly Nasrallah, and Jacob Biamonte · 2021
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Noisy intermediate-scale quantum algorithms
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, et al · 2022
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Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Zoë Holmes, Kunal Sharma, Marco Cerezo, and Patrick J Coles · 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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Efficient bipartite entanglement detection scheme with a quantum adversarial solver
Xu-Fei Yin, Yuxuan Du, Yue-Yang Fei, Rui Zhang, Li-Zheng Liu, Yingqiu Mao, et al · 2022
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