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We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process $\mathcal{E}$ over $n$ qubits.
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Density-matrix algorithms for quantum renormalization groups
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Regression shrinkage and selection via the lasso
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Quantum process tomography of a controlled-not gate
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On the fourier tails of bounded functions over the discrete cube
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The complexity of the local hamiltonian problem
Julia Kempe, Alexei Kitaev, and Oded Regev · 2006
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Quantum-process tomography: Resource analysis of different strategies
Masoud Mohseni, Ali T Rezakhani, and Daniel A Lidar · 2008
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Optimizing quantum process tomography with unitary 2-designs
A. J. Scott · 2008
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A variational eigenvalue solver on a photonic quantum processor
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alán Aspuru-Guzik, and Jeremy L O’brien · 2014
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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A quantum approximate optimization algorithm applied to a bounded occurrence constraint problem
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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Beating the Random Assignment on Constraint Satisfaction Problems of Bounded Degree
Boaz Barak, Ankur Moitra, Ryan O’Donnell, Prasad Raghavendra, Oded Regev, David Steurer, Luca Trevisan, Aravindan Vijayaraghavan, David Witmer, and John Wright · 2015
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The complexity of antiferromagnetic interactions and 2d lattices
Stephen Piddock and Ashley Montanaro · 2015
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Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
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Guest column: A survey of quantum learning theory
Srinivasan Arunachalam and Ronald de Wolf · 2017
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Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
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Giuseppe Carleo and Matthias Troyer · 2017
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Machine learning phases of matter
Juan Carrasquilla and Roger G Melko · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography
Robin Blume-Kohout, John King Gamble, Erik Nielsen, Kenneth Rudinger, Jonathan Mizrahi, Kevin Fortier, and Peter Maunz · 2017
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Extremal eigenvalues of local hamiltonians
Aram W Harrow and Ashley Montanaro · 2017
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Modern Quantum Mechanics
J. J. Sakurai and Jim Napolitano · 2017
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Lecture notes on information-theoretic methods for high-dimensional statistics
Variational quantum algorithms
Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al · 2021
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Provably efficient machine learning for quantum many-body problems
Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai, Victor V Albert, and John Preskill · 2021
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Power of data in quantum machine learning
Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R McClean · 2021
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Classical shadows for quantum process tomography on near-term quantum computers
Ryan Levy, Di Luo, and Bryan K Clark · 2021
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Information-theoretic bounds on quantum advantage in machine learning
Hsin-Yuan Huang, Richard Kueng, and John Preskill · 2021
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Yihong Wu · 2017
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Barren plateaus in quantum neural network training landscapes
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
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Classification with quantum neural networks on near term processors
Edward Farhi and Hartmut Neven · 2018
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Active learning machine learns to create new quantum experiments
Alexey A Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, and Hans J Briegel · 2018
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Kai-Min Chung and Han-Hsuan Lin · 2018
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Shadow tomography of quantum states
Scott Aaronson · 2018
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Modelling non-markovian quantum processes with recurrent neural networks
Leonardo Banchi, Edward Grant, Andrea Rocchetto, and Simone Severini · 2018
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Shadow process tomography of quantum channels
Jonathan Kunjummen, Minh C Tran, Daniel Carney, and Jacob M Taylor · 2021
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Improved approximation algorithms for bounded-degree local hamiltonians
Anurag Anshu, David Gosset, Karen J Morenz Korol, and Mehdi Soleimanifar · 2021
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Fermionic partial tomography via classical shadows
Andrew Zhao, Nicholas C Rubin, and Akimasa Miyake · 2021
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Hamiltonian-driven shadow tomography of quantum states
Hong-Ye Hu and Yi-Zhuang You · 2021
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Learning models of quantum systems from experiments
Antonio A Gentile, Brian Flynn, Sebastian Knauer, Nathan Wiebe, Stefano Paesani, Christopher E Granade, John G Rarity, Raffaele Santagati, and Anthony Laing · 2021
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Generalization in quantum machine learning from few training data
Matthias C Caro, Hsin-Yuan Huang, Marco Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, and Patrick J Coles · 2022
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Classical surrogates for quantum learning models
Franz J Schreiber, Jens Eisert, and Johannes Jakob Meyer · 2022
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Out-of-distribution generalization for learning quantum dynamics
Matthias C Caro, Hsin-Yuan Huang, Nicholas Ezzell, Joe Gibbs, Andrew T Sornborger, Lukasz Cincio, Patrick J Coles, and Zoë Holmes · 2022
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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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Dynamical simulation via quantum machine learning with provable generalization
Joe Gibbs, Zoë Holmes, Matthias C Caro, Nicholas Ezzell, Hsin-Yuan Huang, Lukasz Cincio, Andrew T Sornborger, and Patrick J Coles · 2022
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Foundations for learning from noisy quantum experiments
Hsin-Yuan Huang, Steven T Flammia, and John Preskill · 2022
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Quantum talagrand, kkl and friedgut’s theorems and the learnability of quantum boolean functions
Cambyse Rouzé, Melchior Wirth, and Haonan Zhang · 2022
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The randomized measurement toolbox
Andreas Elben, Steven T Flammia, Hsin-Yuan Huang, Richard Kueng, John Preskill, Benoît Vermersch, and Peter Zoller · 2022
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Optimizing strongly interacting fermionic hamiltonians
Matthew B Hastings and Ryan O’Donnell · 2022
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Learning quantum states from their classical shadows
Hsin-Yuan Huang · 2022
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Fermionic tomography and learning
Bryan O’Gorman · 2022
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Matchgate shadows for fermionic quantum simulation
Kianna Wan, William J Huggins, Joonho Lee, and Ryan Babbush · 2022
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Classical shadows with pauli-invariant unitary ensembles
Kaifeng Bu, Dax Enshan Koh, Roy J Garcia, and Arthur Jaffe · 2022
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Exponential separations between learning with and without quantum memory
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, and Jerry Li · 2022
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Predicting gibbs state expectation values with pure thermal shadows
Luuk Coopmans, Yuta Kikuchi, and Marcello Benedetti · 2022
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Learning low-degree functions from a logarithmic number of random queries
Alexandros Eskenazis and Paata Ivanisvili · 2022
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Learning distributions over quantum measurement outcomes
Weiyuan Gong and Scott Aaronson · 2022
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