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Structure learning of Hamiltonians from real-time evolution
30 April 2024
Ainesh Bakshi
Allen Liu
Ankur Moitra
Ewin Tang
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Papers citing
"Structure learning of Hamiltonians from real-time evolution"
16 / 16 papers shown
Efficient Hamiltonian, structure and trace distance learning of Gaussian states
Marco Fanizza
Cambyse Rouzé
Daniel Stilck França
309
13
0
05 Nov 2024
A Unified Approach to Learning Ising Models: Beyond Independence and Bounded Width
Symposium on the Theory of Computing (STOC), 2023
Jason Gaitonde
Elchanan Mossel
229
9
0
15 Nov 2023
Learning quantum Hamiltonians at any temperature in polynomial time
Symposium on the Theory of Computing (STOC), 2023
Ainesh Bakshi
Allen Liu
Ankur Moitra
Ewin Tang
212
43
0
03 Oct 2023
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems
International Conference on Machine Learning (ICML), 2023
Ainesh Bakshi
Allen Liu
Ankur Moitra
Morris Yau
225
10
0
13 Jul 2023
A New Approach to Learning Linear Dynamical Systems
Symposium on the Theory of Computing (STOC), 2023
Ainesh Bakshi
Allen Liu
Ankur Moitra
Morris Yau
229
24
0
23 Jan 2023
Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrix
ACM Transactions on Quantum Computing (ACM TQC), 2022
Matthias C. Caro
196
55
0
08 Dec 2022
Quantum Model Learning Agent: characterisation of quantum systems through machine learning
Brian Flynn
Antonio A. Gentile
N. Wiebe
R. Santagati
A. Laing
106
14
0
15 Dec 2021
Sample-efficient learning of quantum many-body systems
Nature Physics (Nat. Phys.), 2020
Anurag Anshu
Srinivasan Arunachalam
Tomotaka Kuwahara
Mehdi Soleimanifar
138
141
0
15 Apr 2020
Predicting Many Properties of a Quantum System from Very Few Measurements
Nature Physics (Nat. Phys.), 2020
Hsin-Yuan Huang
R. Kueng
J. Preskill
287
1,367
0
18 Feb 2020
Learning models of quantum systems from experiments
Nature Physics (Nat. Phys.), 2020
Antonio A. Gentile
Brian Flynn
S. Knauer
N. Wiebe
S. Paesani
C. Granade
J. Rarity
R. Santagati
A. Laing
156
70
0
14 Feb 2020
Learning Linear Dynamical Systems with Semi-Parametric Least Squares
Annual Conference Computational Learning Theory (COLT), 2019
Max Simchowitz
Ross Boczar
Benjamin Recht
175
123
0
02 Feb 2019
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Neural Information Processing Systems (NeurIPS), 2017
Linus Hamilton
Frederic Koehler
Ankur Moitra
188
67
0
31 May 2017
Gradient Descent Learns Linear Dynamical Systems
Moritz Hardt
Tengyu Ma
Benjamin Recht
223
258
0
16 Sep 2016
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
Marc Vuffray
Sidhant Misra
A. Lokhov
Michael Chertkov
312
120
0
24 May 2016
Efficiently learning Ising models on arbitrary graphs
Symposium on the Theory of Computing (STOC), 2014
Guy Bresler
276
212
0
22 Nov 2014
Reconstruction of Markov Random Fields from Samples: Some Easy Observations and Algorithms
Guy Bresler
Elchanan Mossel
Allan Sly
361
163
0
10 Dec 2007
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