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Despite years of effort, the quantum machine learning community has only been able to show quantum learning advantages for certain contrived cryptography-inspired datasets in the case of classical data.
How to generate cryptographically strong sequences of pseudorandom bits
Manuel Blum and Silvio Micali · 1984
Earlier work this paper cites.
Cryptographic limitations on learning boolean formulae and finite automata
Michael Kearns and Leslie Valiant · 1994
Earlier work this paper cites.
An introduction to computational learning theory
Michael Kearns and Umesh Vazirani · 1994
Earlier work this paper cites.
No free lunch theorems for optimization
David H Wolpert and William G Macready · 1997
Earlier work this paper cites.
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Peter Shor · 1999
Earlier work this paper cites.
Equivalences and separations between quantum and classical learnability
Rocco Servedio and Steven J Gortler · 2004
Earlier work this paper cites.
Blind quantum computation
Pablo Arrighi and Louis Salvail · 2006
Earlier work this paper cites.
Average-case complexity
Andrej Bogdanov and Luca Trevisan · 2006
Cited alongside, same era.
Quantum algorithm for linear systems of equations
Aram Harrow, Avinatan Hassidim, and Seth Lloyd · 2009
Cited alongside, same era.
Guest column: A survey of quantum learning theory
Srinivasan Arunachalam and Ronald de Wolf · 2017
Cited alongside, same era.
Quantum computational supremacy
Aram W Harrow and Ashley Montanaro · 2017
Cited alongside, same era.
Sequential minimal optimization for quantum-classical hybrid algorithms
Ken Nakanishi, Keisuke Fujii, and Synge Todo · 2020
Cited alongside, same era.
Quantum hardness of learning shallow classical circuits
Srinivasan Arunachalam, Alex Bredariol Grilo, and Aarthi Sundaram · 2021
Cited alongside, same era.
Power of data in quantum machine learning (2020)
HY Huang, M Broughton, M Mohseni, R Babbush, S Boixo, H Neven, and JR McClean · 2021
Later among the works it cites.
Optimal learning of quantum hamiltonians from high-temperature gibbs states
Jeongwan Haah, Robin Kothari, and Ewin Tang · 2021
Later among the works it cites.
A rigorous and robust quantum speed-up in supervised machine learning
Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme · 2021
Later among the works it cites.
Thomas O’Brien, LevC Ioffe, Yuan Su, David Fushman, Hartmut Neven, Ryan Babbush, and Vadim Smelyanskiy · 2021
Later among the works it cites.
On the quantum versus classical learnability of discrete distributions
Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, and Jens Eisert · 2021
Later among the works it cites.
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Encoding-dependent generalization bounds for parametrized quantum circuits
Matthias C Caro, Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert, and Ryan Sweke · 2021
Cited alongside, same era.
Effect of data encoding on the expressive power of variational quantum-machine-learning models
Maria Schuld, Ryan Sweke, and Johannes Jakob Meyer · 2021
Later among the works it cites.
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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