Built on

A short note on learning discrete distributions
Original
Clément L Canonne · 2002
Earlier work this paper cites.
Quantum Boolean functions
Original
Ashley Montanaro and Tobias J Osborne · 2008
Earlier work this paper cites.
Practical characterization of quantum devices without tomography
Marcus P. da Silva, Olivier Landon-Cardinal, and David Poulin · 2011
Earlier work this paper cites.
Learning a local Hamiltonian from local measurements
Eyal Bairey, Itai Arad, and Netanel H. Lindner · 2019
Earlier work this paper cites.
Sample-efficient learning of interacting quantum systems
Anurag Anshu, Srinivasan Arunachalam, Tomotaka Kuwahara, and Mehdi Soleimanifar · 2021
Earlier work this paper cites.
Optimal short-time measurements for hamiltonian learning, 2021
Original
Assaf Zubida, Elad Yitzhaki, Netanel H. Lindner, and Eyal Bairey · 2021
Earlier work this paper cites.
Learning low-degree functions from a logarithmic number of random queries
Alexandros Eskenazis and Paata Ivanisvili · 2022
Earlier work this paper cites.
Optimal learning of quantum hamiltonians from high-temperature gibbs states
Jeongwan Haah, Robin Kothari, and Ewin Tang · 2022
Earlier work this paper cites.
Scalably learning quantum many-body hamiltonians from dynamical data, 2022
Original
Frederik Wilde, Augustine Kshetrimayum, Ingo Roth, Dominik Hangleiter, Ryan Sweke, and Jens Eisert · 2022
Earlier work this paper cites.

Similar

Learning quantum hamiltonians at any temperature in polynomial time, 2023
Original
Ainesh Bakshi, Allen Liu, Ankur Moitra, and Ewin Tang · 2023
Cited alongside, same era.
Learning quantum processes and hamiltonians via the pauli transfer matrix, 2023
Original
Matthias C. Caro · 2023
Cited alongside, same era.
Hamiltonian learning via shadow tomography of pseudo-choi states, 2023
Original
Juan Castaneda and Nathan Wiebe · 2023
Cited alongside, same era.
The advantage of quantum control in many-body hamiltonian learning, 2023
Original
Alicja Dutkiewicz, Thomas E. O’Brien, and Thomas Schuster · 2023
Cited alongside, same era.
Learning to predict arbitrary quantum processes
Hsin-Yuan Huang, Sitan Chen, and John Preskill · 2023

Then

Efficient learning of ground & thermal states within phases of matter, 2023
Original
Emilio Onorati, Cambyse Rouzé, Daniel Stilck França, and James D. Watson · 2023
Later among the works it cites.
Learning quantum many-body systems from a few copies, 2023
Original
Cambyse Rouzé and Daniel Stilck França · 2023
Later among the works it cites.
Noncommutative Bohnenblust–Hille inequalities
Alexander Volberg and Haonan Zhang · 2023
Later among the works it cites.
Robust and efficient hamiltonian learning
Wenjun Yu, Jinzhao Sun, Zeyao Han, and Xiao Yuan · 2023
Later among the works it cites.
Hamiltonian property testing (version 1)
Original
Andreas Bluhm, Matthias C Caro, and Aadil Oufkir · 2024
Closest in time.

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2024

Simple algorithms to test and learn local Hamiltonians

Gutiérrez, Francisco Escudero

Understand

We consider the problems of testing and learning an $n$-qubit $k$-local Hamiltonian from queries to its evolution operator with respect the 2-norm of the Pauli spectrum, or equivalently, the normalized Frobenius norm.

For testing whether a Hamiltonian is $\epsilon_1$-close to $k$-local or $\epsilon_2$-far from $k$-local, we show that $O(1/(\epsilon_2-\epsilon_1)^{8})$ queries suffice.
This solves two questions posed in a recent work by Bluhm, Caro and Oufkir.
For learning up to error $\epsilon$, we show that $\exp(O(k^2+k\log(1/\epsilon)))$ queries suffice.

Built on

A short note on learning discrete distributions
Original
Clément L Canonne · 2002
Earlier work this paper cites.
Quantum Boolean functions
Original
Ashley Montanaro and Tobias J Osborne · 2008
Earlier work this paper cites.
Practical characterization of quantum devices without tomography
Marcus P. da Silva, Olivier Landon-Cardinal, and David Poulin · 2011
Earlier work this paper cites.
Learning a local Hamiltonian from local measurements
Eyal Bairey, Itai Arad, and Netanel H. Lindner · 2019
Earlier work this paper cites.
Sample-efficient learning of interacting quantum systems
Anurag Anshu, Srinivasan Arunachalam, Tomotaka Kuwahara, and Mehdi Soleimanifar · 2021
Earlier work this paper cites.
Optimal short-time measurements for hamiltonian learning, 2021
Original
Assaf Zubida, Elad Yitzhaki, Netanel H. Lindner, and Eyal Bairey · 2021
Earlier work this paper cites.
Learning low-degree functions from a logarithmic number of random queries
Alexandros Eskenazis and Paata Ivanisvili · 2022
Earlier work this paper cites.
Optimal learning of quantum hamiltonians from high-temperature gibbs states
Jeongwan Haah, Robin Kothari, and Ewin Tang · 2022
Earlier work this paper cites.
Scalably learning quantum many-body hamiltonians from dynamical data, 2022
Original
Frederik Wilde, Augustine Kshetrimayum, Ingo Roth, Dominik Hangleiter, Ryan Sweke, and Jens Eisert · 2022
Earlier work this paper cites.

Similar

Learning quantum hamiltonians at any temperature in polynomial time, 2023
Original
Ainesh Bakshi, Allen Liu, Ankur Moitra, and Ewin Tang · 2023
Cited alongside, same era.
Learning quantum processes and hamiltonians via the pauli transfer matrix, 2023
Original
Matthias C. Caro · 2023
Cited alongside, same era.
Hamiltonian learning via shadow tomography of pseudo-choi states, 2023
Original
Juan Castaneda and Nathan Wiebe · 2023
Cited alongside, same era.
The advantage of quantum control in many-body hamiltonian learning, 2023
Original
Alicja Dutkiewicz, Thomas E. O’Brien, and Thomas Schuster · 2023
Cited alongside, same era.
Learning to predict arbitrary quantum processes
Hsin-Yuan Huang, Sitan Chen, and John Preskill · 2023

Then

Efficient learning of ground & thermal states within phases of matter, 2023
Original
Emilio Onorati, Cambyse Rouzé, Daniel Stilck França, and James D. Watson · 2023
Later among the works it cites.
Learning quantum many-body systems from a few copies, 2023
Original
Cambyse Rouzé and Daniel Stilck França · 2023
Later among the works it cites.
Noncommutative Bohnenblust–Hille inequalities
Alexander Volberg and Haonan Zhang · 2023
Later among the works it cites.
Robust and efficient hamiltonian learning
Wenjun Yu, Jinzhao Sun, Zeyao Han, and Xiao Yuan · 2023
Later among the works it cites.
Hamiltonian property testing (version 1)
Original
Andreas Bluhm, Matthias C Caro, and Aadil Oufkir · 2024
Closest in time.