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Variational quantum algorithms, which combine highly expressive parameterized quantum circuits (PQCs) and optimization techniques in machine learning, are one of the most promising applications of a near-term quantum computer.
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Tomotaka Kuwahara, Takashi Mori, and Keiji Saito · 2016
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“Quantum machine learning”
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Ying Li and Simon C Benjamin · 2017
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“Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer”
David Wierichs, Christian Gogolin, and Michael Kastoryano · 2020
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“A rigorous and robust quantum speed-up in supervised machine learning”
Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme · 2021
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“Variational quantum algorithms”
Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al · 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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“Large gradients via correlation in random parameterized quantum circuits”
Tyler Volkoff and Patrick J Coles · 2021
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“Chaos and complexity by design”
Daniel A Roberts and Beni Yoshida · 2017
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Adam R Brown, Leonard Susskind, and Ying Zhao · 2017
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“Out-of-time-order correlation for many-body localization”
Ruihua Fan, Pengfei Zhang, Huitao Shen, and Hui Zhai · 2017
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“A rigorous theory of many-body prethermalization for periodically driven and closed quantum systems”
Dmitry Abanin, Wojciech De Roeck, Wen Wei Ho, and François Huveneers · 2017
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“Quantum computing in the NISQ era and beyond”
John Preskill · 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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“Pennylane: Automatic differentiation of hybrid quantum-classical computations” (2018)
Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Shahnawaz Ahmed, Vishnu Ajith, M. Sohaib Alam, Guillermo Alonso-Linaje, et al · 2018
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Arthur Pesah, Marco Cerezo, Samson Wang, Tyler Volkoff, Andrew T Sornborger, and Patrick J Coles · 2021
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“Real-and imaginary-time evolution with compressed quantum circuits”
Sheng-Hsuan Lin, Rohit Dilip, Andrew G Green, Adam Smith, and Frank Pollmann · 2021
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Chae-Yeun Park · 2021
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“Noise-induced barren plateaus in variational quantum algorithms”
Samson Wang, Enrico Fontana, Marco Cerezo, Kunal Sharma, Akira Sone, Lukasz Cincio, and Patrick J Coles · 2021
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“Quantum computational advantage with a programmable photonic processor”
Lars S Madsen, Fabian Laudenbach, Mohsen Falamarzi Askarani, Fabien Rortais, Trevor Vincent, Jacob FF Bulmer, Filippo M Miatto, Leonhard Neuhaus, Lukas G Helt, Matthew J Collins, et al · 2022
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Zoë Holmes, Kunal Sharma, Marco Cerezo, and Patrick J Coles · 2022
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“Mitigating barren plateaus of variational quantum eigensolvers” (2022)
Xia Liu, Geng Liu, Jiaxin Huang, Hao-Kai Zhang, and Xin Wang · 2022
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“Graph neural network initialisation of quantum approximate optimisation”
Nishant Jain, Brian Coyle, Elham Kashefi, and Niraj Kumar · 2022
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“Escaping from the barren plateau via gaussian initializations in deep variational quantum circuits”
Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, and Dacheng Tao · 2022
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“Avoiding barren plateaus via transferability of smooth solutions in a Hamiltonian variational ansatz”
Antonio A. Mele, Glen B. Mbeng, Giuseppe E. Santoro, Mario Collura, and Pietro Torta · 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 M Cerezo · 2022
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“Probing ground-state properties of the kagome antiferromagnetic heisenberg model using the variational quantum eigensolver”
Jan Lukas Bosse and Ashley Montanaro · 2022
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“Variational quantum eigensolver for the heisenberg antiferromagnet on the kagome lattice”
Joris Kattemölle and Jasper van Wezel · 2022
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“Linear growth of quantum circuit complexity”
Jonas Haferkamp, Philippe Faist, Naga BT Kothakonda, Jens Eisert, and Nicole Yunger Halpern · 2022
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“Synergistic pretraining of parametrized quantum circuits via tensor networks”
Manuel S Rudolph, Jacob Miller, Danial Motlagh, Jing Chen, Atithi Acharya, and Alejandro Perdomo-Ortiz · 2023
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“Classically optimized hamiltonian simulation”
Conor Mc Keever and Michael Lubasch · 2023
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“PennyLane–Lightning plugin https://github.com/PennyLaneAI/pennylane-lightning ” (2023)
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“GitHub repository https://github.com/XanaduAI/hva-without-barren-plateaus ” (2023)
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