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Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework.
Quantum Hamiltonian-ased models and the variational quantum thermalizer algorithm, 2019
G. Verdon, J. Marks, S. Nanda, S. Leichenauer, and J. Hidary · 1910
Earlier work this paper cites.
A theory of the learnable
L. G. Valiant · 1972
Earlier work this paper cites.
How to generate cryptographically strong sequences of pseudorandom bits
M. Blum and S. Micali · 1982
Earlier work this paper cites.
How to construct random functions
O. Goldreich, S. Goldwasser, and S. Micali · 1986
Earlier work this paper cites.
Learnability and the vapnik-chervonenkis dimension
A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth · 1989
Earlier work this paper cites.
Learning DNF under the uniform distribution in quasi-polynomial time
K. Verbeurgt · 1990
Earlier work this paper cites.
Equivalence of models for polynomial learnability
D. Haussler, M. Kearns, N. Littlestone, and M. K. Warmuth · 1991
Earlier work this paper cites.
Cryptographic lower bounds for learnability of boolean functions on the uniform distribution
M. Kharitonov · 1992
Earlier work this paper cites.
On the necessity of occam algorithms
R. Board and L. Pitt · 1992
Earlier work this paper cites.
Cryptographic hardness of distribution-specific learning
M. Kharitonov · 1993
Earlier work this paper cites.
Cryptographic limitations on learning boolean formulae and finite automata
M. Kearns and L. Valiant · 1994
Earlier work this paper cites.
The complexity of exactly learning algebraic concepts
V. Arvind and N. V. Vinodchandran · 1996
Earlier work this paper cites.
Learning DNF over the uniform distribution using a quantum example oracle
N. H. Bshouty and J. C. Jackson · 1998
Earlier work this paper cites.
The decision Diffie-Hellman problem
D. Boneh · 1998
Earlier work this paper cites.
A short note on learning discrete distributions
C. L. Canonne · 2002
Earlier work this paper cites.
Exact quantum fourier transforms and discrete logarithm algorithms
M. Mosca and C. Zalka · 2004
Earlier work this paper cites.
Number-theoretic constructions of efficient pseudo-random functions
M. Naor and O. Reingold · 2004
Earlier work this paper cites.
Equivalences and separations between quantum and classical learnability
R. A. Servedio and S. J. Gortler · 2004
Earlier work this paper cites.
Testing statistical hypotheses
E. L. Lehmann and J. P. Romano · 2007
Earlier work this paper cites.
Foundations of cryptography: volume 1, basic tools
O. Goldreich · 2007
Earlier work this paper cites.
Introduction to Modern Cryptography (Chapman & Hall/Crc Cryptography and Network Security Series)
J. Katz and Y. Lindell · 2007
Cited alongside, same era.
Efficient pseudorandom generators based on the ddh assumption
R. R. Farashahi, B. Schoenmakers, and A. Sidorenko · 2007
Cited alongside, same era.
A clt and tight lower bounds for estimating entropy
G. Valiant and P. Valiant · 2010
Cited alongside, same era.
The computational complexity of linear optics
S. Aaronson and A. Arkhipov · 2011
Cited alongside, same era.
The power of linear estimators
G. Valiant and P. Valiant · 2011
Cited alongside, same era.
How to construct quantum random functions
M. Zhandry · 2012
Cited alongside, same era.
Quantum supremacy in constant-time measurement-based computation: A unified architecture for sampling and verification
J. Miller, S. Sanders, and A. Miyake · 2017
Later among the works it cites.
Machine learning & artificial intelligence in the quantum domain: a review of recent progress
V. Dunjko and H. J. Briegel · 2018
Later among the works it cites.
Supervised learning with quantum computers
M. Schuld and F. Petruccione · 2018
Later among the works it cites.
Differentiable learning of quantum circuit Born machines
J.-G. Liu and L. Wang · 2018
Later among the works it cites.
A quantum machine learning algorithm based on generative models
X. Gao, Z. Zhang, and L. Duan · 2018
Later among the works it cites.
Quantum generative adversarial networks
P.-L. Dallaire-Demers and N. Killoran · 2018
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Cryptography from learning parity with noise
K. Pietrzak · 2012
Cited alongside, same era.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Cited alongside, same era.
Understanding machine learning: From theory to algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
On learning distributions from their samples
S. Kamath, A. Orlitsky, D. Pichapati, and A. T. Suresh · 2015
Cited alongside, same era.
Quantum learning robust against noise
A. W. Cross, G. Smith, and J. A. Smolin · 2015
Cited alongside, same era.
Later among the works it cites.
Quantum generative adversarial learning
S. Lloyd and C. Weedbrook · 2018
Later among the works it cites.
Architectures for quantum simulation showing a quantum speedup
J. Bermejo-Vega, D. Hangleiter, M. Schwarz, R. Raussendorf, and J. Eisert · 2018
Later among the works it cites.
A generative modeling approach for benchmarking and training shallow quantum circuits
M. Benedetti, D. Garcia-Pintos, O. Perdomo, V. Leyton-Ortega, Y. Nam, and A. Perdomo-Ortiz · 2019
Later among the works it cites.
Quantum generative adversarial learning in a superconducting quantum circuit
L. Hu, S.-H. Wu, W. Cai, Y. Ma, X. Mu, Y. Xu, H. Wang, Y. Song, D.-L. Deng, C.-L. Zou, and L. Sun · 2019
Later among the works it cites.
Quantum Wasserstein generative adversarial networks
S. Chakrabarti, H. Yiming, T. Li, S. Feizi, and X. Wu · 2019
Later among the works it cites.
Quantum supremacy using a programmable superconducting processor
F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. S. L. Brandao, D. A. Buell, et al · 2019
Later among the works it cites.
Sample complexity of device-independently certified quantum supremacy
D. Hangleiter, M. Kliesch, J. Eisert, and C. Gogolin · 2019
Later among the works it cites.
Learning-with-errors problem is easy with quantum samples
A. B. Grilo, I. Kerenidis, and T. Zijlstra · 2019
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Verifying commuting quantum computations via fidelity estimation of weighted graph states
M. Hayashi and Y. Takeuchi · 2019
Later among the works it cites.
Statistical limits of supervised quantum learning
C. Ciliberto, A. Rocchetto, A. Rudi, and L. Wossnig · 2020
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Normalizing flows: An introduction and review of current methods
I. Kobyzev, S. Prince, and M. Brubaker · 2020
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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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A Survey on Distribution Testing: Your Data is Big. But is it Blue?
C. L. Canonne · 2020
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A rigorous and robust quantum speed-up in supervised machine learning
Y. Liu, S. Arunachalam, and K. Temme · 2020
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