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Quantum Neural Networks (QNN) are considered a candidate for achieving quantum advantage in the Noisy Intermediate Scale Quantum computer (NISQ) era.
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Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik · 2016
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“Local Random Quantum Circuits are Approximate Polynomial-Designs”
Fernando G. S. L. Brandão, Aram W. Harrow, and Michał Horodecki · 2016
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Zbigniew Puchała, Łukasz Pawela, and Karol Życzkowski · 2016
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Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
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“Quantum entanglement growth under random unitary dynamics”
Adam Nahum, Jonathan Ruhman, Sagar Vijay, and Jeongwan Haah · 2017
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“Quantum Computing in the NISQ era and beyond”
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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
Cited alongside, same era.
“Entanglement, quantum randomness, and complexity beyond scrambling”
Zi-Wen Liu, Seth Lloyd, Elton Zhu, and Huangjun Zhu · 2018
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“Quantum approximate optimization is computationally universal” (2018) arXiv:1812.11075
Seth Lloyd · 2018
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“Pennylane: Automatic differentiation of hybrid quantum-classical computations” (2018)
Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M Sohaib Alam, Shahnawaz Ahmed, Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, et al · 2018
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“Quantum convolutional neural networks”
Iris Cong, Soonwon Choi, and Mikhail D. Lukin · 2019
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“Effect of barren plateaus on gradient-free optimization”
Andrew Arrasmith, M. Cerezo, Piotr Czarnik, Lukasz Cincio, and Patrick J. Coles · 2021
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“Entanglement-induced barren plateaus”
Carlos Ortiz Marrero, Mária Kieferová, and Nathan Wiebe · 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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“Entanglement devised barren plateau mitigation”
Taylor L. Patti, Khadijeh Najafi, Xun Gao, and Susanne F. Yelin · 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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“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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“Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms”
Sukin Sim, Peter D. Johnson, and Alán Aspuru-Guzik · 2019
Cited alongside, same era.
“The tensor networks anthology: Simulation techniques for many-body quantum lattice systems”
Pietro Silvi, Ferdinand Tschirsich, Matthias Gerster, Johannes Jünemann, Daniel Jaschke, Matteo Rizzi, and Simone Montangero · 2019
Cited alongside, same era.
“Time-evolution methods for matrix-product states”
Sebastian Paeckel, Thomas Köhler, Andreas Swoboda, Salvatore R. Manmana, Ulrich Schollwöck, and Claudius Hubig · 2019
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“The Random Matrix Theory of the Classical Compact Groups”
Elizabeth S. Meckes · 2019
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“Unitary quantum perceptron as efficient universal approximator”
E. Torrontegui and J. J. Garcia-Ripoll · 2019
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“Evaluating analytic gradients on quantum hardware”
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
Cited alongside, same era.
“An initialization strategy for addressing barren plateaus in parametrized quantum circuits”
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti · 2019
Cited alongside, same era.
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“Improved spectral gaps for random quantum circuits: Large local dimensions and all-to-all interactions”
Jonas Haferkamp and Nicholas Hunter-Jones · 2021
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“Supervised quantum machine learning models are kernel methods” (2021) arXiv:2101.11020
Maria Schuld · 2021
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“Theory of overparametrization in quantum neural networks” (2021) arXiv:2109.11676
Martin Larocca, Nathan Ju, Diego García-Martín, Patrick J. Coles, and M. Cerezo · 2021
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“Qiskit: An open-source framework for quantum computing”
Md Sajid Anis et al · 2021
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“Quantum computer simulation via tensor networks”
Marco Ballarin · 2021
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“Magnetic control of tokamak plasmas through deep reinforcement learning”
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de las Casas, Craig Donner, Leslie Fritz, Cristian Galperti, Andrea Huber, James Keeling, Maria Tsimpoukelli, Jackie Kay, Antoine Merle, Jean-Marc Moret, Seb Noury, Federico Pesamosca, David Pfau, Olivier Sauter, Cristian Sommariva, Stefano Coda, Basil Duval, Ambrogio Fasoli, Pushmeet Kohli, Koray Kavukcuoglu, Demis Hassabis, and Martin Riedmiller · 2022
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“Noisy intermediate-scale quantum algorithms”
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, and Alán Aspuru-Guzik · 2022
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“Classical surrogates for quantum learning models” (2022) arXiv:2206.11740
Franz J. Schreiber, Jens Eisert, and Johannes Jakob Meyer · 2022
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“Quantum self-supervised learning”
B Jaderberg, L W Anderson, W Xie, S Albanie, M Kiffner, and D Jaksch · 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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“Equivalence of quantum barren plateaus to cost concentration and narrow gorges”
Andrew Arrasmith, Zoë Holmes, Marco Cerezo, and Patrick J Coles · 2022
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“Avoiding barren plateaus using classical shadows”
Stefan H. Sack, Raimel A. Medina, Alexios A. Michailidis, Richard Kueng, and Maksym Serbyn · 2022
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“Entanglement diagnostics for efficient vqa optimization”
Joonho Kim and Yaron Oz · 2022
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“Is quantum computing green? an estimate for an energy-efficiency quantum advantage”
Daniel Jaschke and Simone Montangero · 2022
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“Entanglement perspective on the quantum approximate optimization algorithm”
Maxime Dupont, Nicolas Didier, Mark J. Hodson, Joel E. Moore, and Matthew J. Reagor · 2022
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“Representation theory for geometric quantum machine learning” (2022) arXiv:2210.07980
Michael Ragone, Paolo Braccia, Quynh T. Nguyen, Louis Schatzki, Patrick J. Coles, Frederic Sauvage, Martin Larocca, and M. Cerezo · 2022
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“Reformulation of the no-free-lunch theorem for entangled datasets”
Kunal Sharma, M. Cerezo, Zoë Holmes, Lukasz Cincio, Andrew Sornborger, and Patrick J. Coles · 2022
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“Quantum variational algorithms are swamped with traps”
Eric R. Anschuetz and Bobak T. Kiani · 2022
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“Exploiting symmetry in variational quantum machine learning”
Johannes Jakob Meyer, Marian Mularski, Elies Gil-Fuster, Antonio Anna Mele, Francesco Arzani, Alissa Wilms, and Jens Eisert · 2023
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“Equivariant quantum circuits for learning on weighted graphs”
Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, and Vedran Dunjko · 2023
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“Quantum machine learning beyond kernel methods”
Sofiene Jerbi, Lukas J Fiderer, Hendrik Poulsen Nautrup, Jonas M Kübler, Hans J Briegel, and Vedran Dunjko · 2023
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“Approximate unitary t-designs by short random quantum circuits using nearest-neighbor and long-range gates”
Aram W Harrow and Saeed Mehraban · 2023
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