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Parameterized quantum circuits can be used as quantum neural networks and have the potential to outperform their classical counterparts when trained for addressing learning problems.
Exact and approximate unitary 2-designs and their application to fidelity estimation
Christoph Dankert, Richard Cleve, Joseph Emerson, and Etera Livine · 2009
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Power of data in quantum machine learning (2020)
HY Huang, M Broughton, M Mohseni, R Babbush, S Boixo, H Neven, and JR McClean · 2011
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Symbolic integration with respect to the haar measure on the unitary group
Zbigniew Puchała and Jarosław Adam Miszczak · 2011
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Moments of random matrices and weingarten functions
Yinzheng Gu · 2013
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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An introduction to quantum machine learning
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione · 2015
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Progress towards practical quantum variational algorithms
Dave Wecker, Matthew B Hastings, and Matthias Troyer · 2015
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Deep neural networks as gaussian processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2017
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Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
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Chaos and complexity by design
Daniel A. Roberts and Beni Yoshida · 2017
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Chaos, Complexity, and Random Matrices
Jordan Cotler, Nicholas Hunter-Jones, Junyu Liu, and Beni Yoshida · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Machine learning & artificial intelligence in the quantum domain: a review of recent progress
Vedran Dunjko and Hans J Briegel · 2018
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Classification with quantum neural networks on near term processors
Edward Farhi and Hartmut Neven · 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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Aram Harrow and Saeed Mehraban · 2018
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Spectral form factors and late time quantum chaos
Junyu Liu · 2018
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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 2019
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Quantum convolutional neural networks
Iris Cong, Soonwon Choi, and Mikhail D Lukin · 2019
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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
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Rtni: A symbolic integrator for haar-random tensor networks
Motohisa Fukuda, Robert König, and Ion Nechita · 2019
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Qiskit: An open source framework for quantum computing, 2019
2019
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On the infinite width limit of neural networks with a standard parameterization
Jascha Sohl-Dickstein, Roman Novak, Samuel S Schoenholz, and Jaehoon Lee · 2020
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Feature learning in infinite-width neural networks
Greg Yang and Edward J Hu · 2020
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Connecting ansatz expressibility to gradient magnitudes and barren plateaus, arxiv e-prints
Z Holmes, K Sharma, M Cerezo, and PJ Coles · 2021
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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 · 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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Analyzing the barren plateau phenomenon in training quantum neural networks with the zx-calculus
Chen Zhao and Xiao-Shan Gao · 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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Non-gaussian processes and neural networks at finite widths
Sho Yaida · 2020
Cited alongside, same era.
Power of data in quantum machine learning
Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R McClean · 2020
Cited alongside, same era.
Recurrent quantum neural networks
Johannes Bausch · 2020
Cited alongside, same era.
Training deep quantum neural networks
Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J Osborne, Robert Salzmann, Daniel Scheiermann, and Ramona Wolf · 2020
Cited alongside, same era.
Trainability of dissipative perceptron-based quantum neural networks
Kunal Sharma, Marco Cerezo, Lukasz Cincio, and Patrick J Coles · 2020
Cited alongside, same era.
Learning unitaries by gradient descent
Bobak Toussi Kiani, Seth Lloyd, and Reevu Maity · 2020
Cited alongside, same era.
Exploring entanglement and optimization within the hamiltonian variational ansatz
Roeland Wiersema, Cunlu Zhou, Yvette de Sereville, Juan Felipe Carrasquilla, Yong Baek Kim, and Henry Yuen · 2020
Cited alongside, same era.
Subtleties in the trainability of quantum machine learning models
Supanut Thanasilp, Samson Wang, Nhat A Nghiem, Patrick J Coles, and Marco Cerezo · 2021
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Expressibility of the alternating layered ansatz for quantum computation
Kouhei Nakaji and Naoki Yamamoto · 2021
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An efficient measure for the expressivity of variational quantum algorithms
Yuxuan Du, Zhuozhuo Tu, Xiao Yuan, and Dacheng Tao · 2021
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Generalization in quantum machine learning from few training data
Matthias C Caro, Hsin-Yuan Huang, M Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, and Patrick J Coles · 2021
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Information-theoretic bounds on quantum advantage in machine learning
Hsin-Yuan Huang, Richard Kueng, and John Preskill · 2021
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Representation Learning via Quantum Neural Tangent Kernels
Junyu Liu, Francesco Tacchino, Jennifer R. Glick, Liang Jiang, and Antonio Mezzacapo · 2021
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Norihito Shirai, Kenji Kubo, Kosuke Mitarai, and Keisuke Fujii · 2021
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Theory of overparametrization in quantum neural networks
Martin Larocca, Nathan Ju, Diego García-Martín, Patrick J Coles, and M Cerezo · 2021
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The principles of deep learning theory
Daniel A Roberts, Sho Yaida, and Boris Hanin · 2021
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Representation learning via quantum neural tangent kernels
Junyu Liu, Francesco Tacchino, Jennifer R Glick, Liang Jiang, and Antonio Mezzacapo · 2021
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Neural tangent kernel eigenvalues accurately predict generalization
James B Simon, Madeline Dickens, and Michael R DeWeese · 2021
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Generalization in quantum machine learning: A quantum information standpoint
Leonardo Banchi, Jason Pereira, and Stefano Pirandola · 2021
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Quantum algorithmic measurement
Dorit Aharonov, Jordan Cotler, and Xiao-Liang Qi · 2022
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Is quantum advantage the right goal for quantum machine learning?
Maria Schuld and Nathan Killoran · 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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