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The task of learning a probability distribution from samples is ubiquitous across the natural sciences.
Learning integer lattices
D. Helmbold, R. Sloan, and M. K. Warmuth · 1992
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M. Kearns, Y. Mansour, D. Ron, R. Rubinfeld, R. E. Schapire, and L. Sellie · 1994
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On-line learning with malicious noise and the closure algorithm
P. Auer and N. Cesa-Bianchi · 1994
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The Heisenberg representation of quantum computers
D. Gottesman · 1998
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Efficient noise-tolerant learning from statistical queries
M. Kearns · 1998
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Efficient discrete approximations of quantum gates
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S. Aaronson and D. Gottesman · 2004
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The Solovay-Kitaev algorithm
C. M. Dawson and M. A. Nielsen · 2005
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The learnability of quantum states
S. Aaronson · 2007
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On lattices, learning with errors, random linear codes, and cryptography
O. Regev · 2009
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Foundations of cryptography: volume 2, basic applications
O. Goldreich · 2009
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Classical simulation of commuting quantum computations implies collapse of the polynomial hierarchy
M. J. Bremner, R. Jozsa, and D. J. Shepherd · 2010
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Learning to create is as hard as learning to appreciate
D. Xiao · 2010
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Quantum Computation and Quantum Information: 10th Anniversary Edition
M. A. Nielsen and I. L. Chuang · 2011
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Learning poisson binomial distributions
C. Daskalakis, I. Diakonikolas, and R. A. Servedio · 2012
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Cryptography from learning parity with noise
K. Pietrzak · 2012
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How to construct quantum random functions
M. Zhandry · 2012
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Learning mixtures of structured distributions over discrete domains
S.-O. Chan, I. Diakonikolas, X. Sun, and R. A. Servedio · 2013
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The rank of random binary matrices and distributed storage applications
Paulo J. S. G. Ferreira, Bruno Jesus, Jose Vieira, and Armando J. Pinho · 2013
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Learning from satisfying assignments
A. De, I. Diakonikolas, and R. A. Servedio · 2015
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Estimating outcome probabilities of quantum circuits using quasiprobabilities
H. Pashayan, J. J. Wallman, and S. D. Bartlett · 2015
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Learning structured distributions
I. Diakonikolas · 2016
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Improved classical simulation of quantum circuits dominated by Clifford gates
S. Bravyi and D. Gosset · 2016
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Statistical query lower bounds for robust estimation of high-dimensional Gaussians and Gaussian mixtures
I. Diakonikolas, D. M. Kane, and A. Stewart · 2017
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Quantum machine learning
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd · 2017
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The Born supremacy: Quantum advantage and training of an Ising Born machine
B. Coyle, D. Mills, V. Danos, and E. Kashefi · 2020
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Generation of high-resolution handwritten digits with an ion-trap quantum computer
M. S. Rudolph, N. Bashige Toussaint, A. Katabarwa, S. Johri, B. Peropadre, and A. Perdomo-Ortiz · 2020
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Learnability and complexity of quantum samples
M. Y. Niu, A. M. Dai, L. Li, A. Odena, Z. Zhao, V. Smelyanskyi, H. Neven, and S. Boixo · 2020
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Sample amplification: Increasing dataset size even when learning is impossible
B. Axelrod, S. Garg, V. Sharan, and G. Valiant · 2020
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From estimation of quantum probabilities to simulation of quantum circuits
H. Pashayan, S. D. Bartlett, and D. Gross · 2020
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Learning in Implicit Generative Models
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A General Characterization of the Statistical Query Complexity
V. Feldman · 2017
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Statistical query lower bounds for robust estimation of high-dimensional Gaussians and Gaussian mixtures
I. Diakonikolas, D. M. Kane, and A. Stewart · 2017
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Guest column: A survey of quantum learning theory
S. Arunachalam and R. de Wolf · 2017
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Noisy intermediate-scale quantum (NISQ) algorithms
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A rigorous and robust quantum speed-up in supervised machine learning
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On the quantum versus classical learnability of discrete distributions
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Parametrized quantum policies for reinforcement learning
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Limitations of optimization algorithms on noisy quantum devices
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Efficient learning of non-interacting fermion distributions
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Shtetl-optimized: Yet more mistakes in papers
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Models of quantum complexity growth
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Hadamard-free circuits expose the structure of the clifford group
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Training compute-optimal large language models
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