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The success of modern deep learning hinges on the ability to train neural networks at scale.
Learning representations by back-propagating errors
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Handwritten digit recognition with a back-propagation network
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Steven R White · 1992
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A fast quantum mechanical algorithm for database search
Lov K Grover · 1996
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Adaptive stochastic approximation by the simultaneous perturbation method
James C Spall · 2000
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Fast quantum algorithm for numerical gradient estimation
Stephen P Jordan · 2005
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Matrix product state representations
David Perez-Garcia, Frank Verstraete, Michael M Wolf, and J Ignacio Cirac · 2006
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Evaluating derivatives: principles and techniques of algorithmic differentiation
Andreas Griewank and Andrea Walther · 2008
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Efficient quantum state tomography
Marcus Cramer, Martin B Plenio, Steven T Flammia, Rolando Somma, David Gross, Stephen D Bartlett, Olivier Landon-Cardinal, David Poulin, and Yi-Kai Liu · 2010
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Quantum techniques for stochastic mechanics
John C Baez and Jacob Biamonte · 2012
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Deep generative stochastic networks trainable by backprop
Yoshua Bengio, Eric Laufer, Guillaume Alain, and Jason Yosinski · 2014
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Tensor network renormalization
Glen Evenbly and Guifre Vidal · 2015
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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The space” just above” bqp
Scott Aaronson, Adam Bouland, Joseph Fitzsimons, and Mitchell Lee · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi · 2017
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Quantum sdp solvers: Large speed-ups, optimality, and applications to quantum learning
Fernando GSL Brandão, Amir Kalev, Tongyang Li, Cedric Yen-Yu Lin, Krysta M Svore, and Xiaodi Wu · 2017
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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
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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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Cost-function-dependent barren plateaus in shallow quantum neural networks, 2020
Marco Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J Coles · 2020
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Predicting many properties of a quantum system from very few measurements
Hsin-Yuan Huang, Richard Kueng, and John Preskill · 2020
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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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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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Improved quantum data analysis
Costin Bădescu and Ryan O’Donnell · 2021
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Nearly optimal quantum algorithm for estimating multiple expectation values
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Online learning of quantum states
Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, and Ashwin Nayak · 2018
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Pseudorandom quantum states
Zhengfeng Ji, Yi-Kai Liu, and Fang Song · 2018
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Quantum circuit learning
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii · 2018
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Wider or deeper: Revisiting the resnet model for visual recognition
Zifeng Wu, Chunhua Shen, and Anton Van Den Hengel · 2019
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Optimizing quantum optimization algorithms via faster quantum gradient computation
András Gilyén, Srinivasan Arunachalam, and Nathan Wiebe · 2019
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Shadow tomography of quantum states
Scott Aaronson · 2019
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Gentle measurement of quantum states and differential privacy
Scott Aaronson and Guy N Rothblum · 2019
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William J Huggins, Kianna Wan, Jarrod McClean, Thomas E O’Brien, Nathan Wiebe, and Ryan Babbush · 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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Focus beyond quadratic speedups for error-corrected quantum advantage
Ryan Babbush, Jarrod R McClean, Michael Newman, Craig Gidney, Sergio Boixo, and Hartmut Neven · 2021
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Simultaneous perturbation stochastic approximation of the quantum fisher information
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Is quantum advantage the right goal for quantum machine learning?
Maria Schuld and Nathan Killoran · 2022
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Exponential separations between learning with and without quantum memory
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, and Jerry Li · 2022
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Quantum advantage in learning from experiments
Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, et al · 2022
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The forward-forward algorithm: Some preliminary investigations
Geoffrey Hinton · 2022
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Gradient estimation with constant scaling for hybrid quantum machine learning
Thomas Hoffmann and Douglas Brown · 2022
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Generalization in quantum machine learning from few training data
Matthias C Caro, Hsin-Yuan Huang, Marco Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, and Patrick J Coles · 2022
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