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We present hierarchical learning, a novel variational architecture for efficient training of large-scale variational quantum circuits.
A quantum approximate optimization algorithm, 2014
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The theory of variational hybrid quantum-classical algorithms
Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik · 2016
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Dave Wecker, Matthew B. Hastings, and Matthias Troyer · 2016
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Barren plateaus in quantum neural network training landscapes
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Challenges and opportunities in quantum machine learning
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Power of Quantum Generative Learning
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Generative quantum learning of joint probability distribution functions
Elton Yechao Zhu, Sonika Johri, Dave Bacon, Mert Esencan, Jungsang Kim, Mark Muir, Nikhil Murgai, Jason Nguyen, Neal Pisenti, Adam Schouela, Ksenia Sosnova, and Ken Wright · 2022
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Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Zoë Holmes, Kunal Sharma, M. Cerezo, and Patrick J. Coles · 2022
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M. Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J. Coles · 2021
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Entanglement devised barren plateau mitigation
Taylor L. Patti, Khadijeh Najafi, Xun Gao, and Susanne F. Yelin · 2021
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Noise-Induced Barren Plateaus in Variational Quantum Algorithms
Samson Wang, Enrico Fontana, M. Cerezo, Kunal Sharma, Akira Sone, Lukasz Cincio, and Patrick J. Coles · 2021
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Generation and verification of 27-qubit greenberger-horne-zeilinger states in a superconducting quantum computer
Gary J Mooney, Gregory A L White, Charles D Hill, and Lloyd C L Hollenberg · 2021
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Evidence for the utility of quantum computing before fault tolerance
Youngseok Kim, Andrew Eddins, Sajant Anand, Ken Xuan Wei, Ewout van den Berg, Sami Rosenblatt, Hasan Nayfeh, Yantao Wu, Michael Zaletel, Kristan Temme, and Abhinav Kandala · 2023
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Aquila: Quera’s 256-qubit neutral-atom quantum computer, 2023
Jonathan Wurtz, Alexei Bylinskii, Boris Braverman, Jesse Amato-Grill, Sergio H. Cantu, Florian Huber, Alexander Lukin, Fangli Liu, Phillip Weinberg, John Long, Sheng-Tao Wang, Nathan Gemelke, and Alexander Keesling · 2023
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Trainability barriers and opportunities in quantum generative modeling
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Experimental benchmarking of an automated deterministic error-suppression workflow for quantum algorithms
Pranav S. Mundada, Aaron Barbosa, Smarak Maity, Yulun Wang, Thomas Merkh, T.M. Stace, Felicity Nielson, Andre R.R. Carvalho, Michael Hush, Michael J. Biercuk, and Yuval Baum · 2023
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