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Two main challenges preventing efficient training of variational quantum algorithms and quantum machine learning models are local minima and barren plateaus.
“Input Redundancy for Parameterized Quantum Circuits”
Francisco Javier Gil Vidal and Dirk Oliver Theis · 1901
Earlier work this paper cites.
“An initialization strategy for addressing barren plateaus in parametrized quantum circuits”
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti · 1903
Earlier work this paper cites.
“Learning Unitaries by Gradient Descent” (2020)
Bobak Toussi Kiani, Seth Lloyd, and Reevu Maity · 2001
Earlier work this paper cites.
“Cost function dependent barren plateaus in shallow parametrized quantum circuits”
M. Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J. Coles · 2001
Earlier work this paper cites.
“Trainability of Dissipative Perceptron-Based Quantum Neural Networks”
Kunal Sharma, M. Cerezo, Lukasz Cincio, and Patrick J. Coles · 2005
Earlier work this paper cites.
“Layerwise learning for quantum neural networks”
Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, and Martin Leib · 2006
Earlier work this paper cites.
“Noise-induced barren plateaus in variational quantum algorithms”
Samson Wang, Enrico Fontana, M. Cerezo, Kunal Sharma, Akira Sone, Lukasz Cincio, and Patrick J. Coles · 2007
Earlier work this paper cites.
“The effect of data encoding on the expressive power of variational quantum machine learning models”
Maria Schuld, Ryan Sweke, and Johannes Jakob Meyer · 2008
Earlier work this paper cites.
“Exact and approximate unitary 2-designs and their application to fidelity estimation”
Christoph Dankert, Richard Cleve, Joseph Emerson, and Etera Livine · 2009
Earlier work this paper cites.
“Limitations of optimization algorithms on noisy quantum devices”
Daniel Stilck França and Raul García-Patrón · 2009
Earlier work this paper cites.
“Abrupt transitions in variational quantum circuit training”
Ernesto Campos, Aly Nasrallah, and Jacob Biamonte · 2010
Earlier work this paper cites.
“Entanglement-Induced Barren Plateaus”
Carlos Ortiz Marrero, Mária Kieferová, and Nathan Wiebe · 2010
Earlier work this paper cites.
“Absence of Barren Plateaus in Quantum Convolutional Neural Networks”
Arthur Pesah, M. Cerezo, Samson Wang, Tyler Volkoff, Andrew T. Sornborger, and Patrick J. Coles · 2011
Earlier work this paper cites.
“On barren plateaus and cost function locality in variational quantum algorithms”
A V Uvarov and J D Biamonte · 2011
Earlier work this paper cites.
“Variational quantum algorithms”
M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, and Patrick J. Coles · 2012
Earlier work this paper cites.
“A Quantum Approximate Optimization Algorithm” (2014)
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
Earlier work this paper cites.
“An introduction to quantum machine learning”
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione · 2015
Earlier work this paper cites.
“Deep learning”
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
Earlier work this paper cites.
“Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets”
Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M. Chow, and Jay M. Gambetta · 2017
Earlier work this paper cites.
“Quantum autoencoders for efficient compression of quantum data”
Jonathan Romero, Jonathan P. Olson, and Alan Aspuru-Guzik · 2017
Earlier work this paper cites.
“Finding approximate local minima faster than gradient descent”
Naman Agarwal, Zeyuan Allen-Zhu, Brian Bullins, Elad Hazan, and Tengyu Ma · 2017
Earlier work this paper cites.
“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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“Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz”
Jonathan Romero, Ryan Babbush, Jarrod R. McClean, Cornelius Hempel, Peter J. Love, and Alán Aspuru-Guzik · 2018
Cited alongside, same era.
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii · 2018
Cited alongside, same era.
“Connecting Ansatz Expressibility to Gradient Magnitudes and Barren Plateaus”
Zoë Holmes, Kunal Sharma, M. Cerezo, and Patrick J. Coles · 2022
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“Robust quantum compilation and circuit optimisation via energy minimisation”
Tyson Jones and Simon C Benjamin · 2022
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“Variational quantum eigensolver techniques for simulating carbon monoxide oxidation”
Mariia D. Sapova and Aleksey K. Fedorov · 2022
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“Classical surrogates for quantum learning models” (2022)
Franz J. Schreiber, Jens Eisert, and Johannes Jakob Meyer · 2022
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Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2018
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“Quantum-assisted quantum compiling”
Sumeet Khatri, Ryan LaRose, Alexander Poremba, Lukasz Cincio, Andrew T. Sornborger, and Patrick J. Coles · 2019
Cited alongside, same era.
“Noisy intermediate-scale quantum (NISQ) 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 · 2021
Cited alongside, same era.
“Subtleties in the trainability of quantum machine learning models”
Supanut Thanasilp, Samson Wang, Nhat A. Nghiem, Patrick J. Coles, and M. Cerezo · 2021
Cited alongside, same era.
“Training Variational Quantum Algorithms Is NP-Hard”
Lennart Bittel and Martin Kliesch · 2021
Cited alongside, same era.
“Critical Points in Quantum Generative Models” (2021)
Eric R. Anschuetz · 2021
Cited alongside, same era.
“Exponentially Many Local Minima in Quantum Neural Networks”
Xuchen You and Xiaodi Wu · 2021
Cited alongside, same era.
“Theory of overparametrization in quantum neural networks” (2021)
Martin Larocca, Nathan Ju, Diego García-Martín, Patrick J. Coles, and M. Cerezo · 2021
Cited alongside, same era.
Enrico Fontana, Ivan Rungger, Ross Duncan, and Cristina Cˆırstoiu Cˆırstoiu · 2022
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“Spectral analysis for noise diagnostics and filter-based digital error mitigation” (2022)
Enrico Fontana, Ivan Rungger, Ross Duncan, and Cristina Cˆırstoiu Cˆırstoiu · 2022
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“Trainability barriers and opportunities in quantum generative modeling” (2023)
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp, Oriel Kiss, Sofia Vallecorsa, Michele Grossi, and Zoë Holmes · 2023
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“Efficient variational synthesis of quantum circuits with coherent multi-start optimization”
Nikita A. Nemkov, Evgeniy O. Kiktenko, Ilia A. Luchnikov, and Aleksey K. Fedorov · 2023
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M. Cerezo, Martin Larocca, Diego García-Martín, N. L. Diaz, Paolo Braccia, Enrico Fontana, Manuel S. Rudolph, Pablo Bermejo, Aroosa Ijaz, Supanut Thanasilp, Eric R. Anschuetz, and Zoë Holmes · 2023
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Yabo Wang, Bo Qi, Chris Ferrie, and Daoyi Dong · 2023
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Aram W. Harrow and Saeed Mehraban · 2023
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“Classical simulations of noisy variational quantum circuits” (2023)
Enrico Fontana, Manuel S Rudolph, Ross Duncan, Ivan Rungger, and Cristina Cîrstoiu · 2023
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“A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits” (2023)
Michael Ragone, Bojko N. Bakalov, Frédéric Sauvage, Alexander F. Kemper, Carlos Ortiz Marrero, Martin Larocca, and M. Cerezo · 2023
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Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti, Niraj Kumar, Romina Yalovetzky, Jamie Heredge, Shree Hari Sureshbabu, and Marco Pistoia · 2023
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“Fourier series weight in quantum machine learning” (2023)
Parfait Atchade-Adelomou and Kent Larson · 2023
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“Multi-dimensional Fourier series with quantum circuits” (2023)
Berta Casas and Alba Cervera-Lierta · 2023
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“Classically Approximating Variational Quantum Machine Learning with Random Fourier Features” (2023)
Jonas Landman, Slimane Thabet, Constantin Dalyac, Hela Mhiri, and Elham Kashefi · 2023
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“Fourier expansion in variational quantum algorithms”
Nikita A. Nemkov, Evgeniy O. Kiktenko, and Aleksey K. Fedorov · 2023
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“A Review of Barren Plateaus in Variational Quantum Computing” (2024)
Martin Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob Biamonte, Patrick J. Coles, Lukasz Cincio, Jarrod R. McClean, Zoë Holmes, and M. Cerezo · 2024
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Qiang Miao and Thomas Barthel · 2024
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Phattharaporn Singkanipa and Daniel A Lidar · 2024
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url: https://github.com/idnm/barren_traps
N. Nemkov, E. Kiktenko, and A. Fedorov (2024) · 2024
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