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In recent works, much progress has been made with regards to so-called randomized measurement strategies, which include the famous methods of classical shadows and shadow tomography.
A theory of the learnable
Leslie G Valiant · 1984
Earlier work this paper cites.
Exponential separation of quantum and classical one-way communication complexity
Ziv Bar-Yossef, Thathachar S Jayram, and Iordanis Kerenidis · 2004
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Machine learning in a quantum world
Esma Aïmeur, Gilles Brassard, and Sébastien Gambs · 2006
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How to construct quantum random functions
Mark Zhandry · 2012
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The learnability of unknown quantum measurements
Hao-Chung Cheng, Min-Hsiu Hsieh, and Ping-Cheng Yeh · 2015
Earlier work this paper cites.
Shadow tomography of quantum states
Scott Aaronson · 2018
Earlier work this paper cites.
Quantum states cannot be transmitted efficiently classically
Ashley Montanaro · 2019
Earlier work this paper cites.
(pseudo) random quantum states with binary phase
Zvika Brakerski and Omri Shmueli · 2019
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Predicting many properties of a quantum system from very few measurements
Hsin-Yuan Huang, Richard Kueng, and John Preskill · 2020
Cited alongside, same era.
Predicting many properties of a quantum system from very few measurements
Hsin-Yuan Huang, Richard Kueng, and John Preskill · 2020
Cited alongside, same era.
On the quantum versus classical learnability of discrete distributions
Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, and Jens Eisert · 2021
Cited alongside, same era.
A rigorous and robust quantum speed-up in supervised machine learning
Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme · 2021
Cited alongside, same era.
Exponential separations between learning with and without quantum memory
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, and Jerry Li · 2022
Provably efficient machine learning for quantum many-body problems
Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai, Victor V Albert, and John Preskill · 2022
Later among the works it cites.
Exponential separations between learning with and without quantum memory
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, and Jerry Li · 2022
Later among the works it cites.
The randomized measurement toolbox
Andreas Elben, Steven T Flammia, Hsin-Yuan Huang, Richard Kueng, John Preskill, Benoît Vermersch, and Peter Zoller · 2023
Closest in time.
A qubit, a coin, and an advice string walk into a relational problem
Scott Aaronson, Harry Buhrman, and William Kretschmer · 2023
Closest in time.
Learning distributions over quantum measurement outcomes
Weiyuan Gong and Scott Aaronson · 2023
Closest in time.
The power and limitations of learning quantum dynamics incoherently
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Cited alongside, same era.
Sofiene Jerbi, Joe Gibbs, Manuel S Rudolph, Matthias C Caro, Patrick J Coles, Hsin-Yuan Huang, and Zoë Holmes · 2023
Closest in time.