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A high-bias, low-variance introduction to Machine Learning for physicists

P. Mehta, M. Bukov, C. Wang, A. Day, C. Richardson, C. Fisher, and D. Schwab. (2018)cite arxiv:1803.08823Comment: Notebooks have been updated. 122 pages, 78 figures, 20 Python notebooks.
DOI: 10.1016/j.physrep.2019.03.001

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Aleksej Bukov

Petar Bukovac

Bernhard Bukovc

Else Marin

Richard Marin

Other publications of authors with the same name

Prethermalization in periodically driven nonreciprocal many-body spin systemsA. McRoberts, H. Zhao, R. Moessner, and M. Bukov. Phys. Rev. Res., 5 (4): 043008 (Nov 4, 2023)Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks.J. Yao, P. Köttering, H. Gundlach, L. Lin, and M. Bukov. CoRR, (2020)Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits.J. Yao, H. Li, M. Bukov, L. Lin, and L. Ying. MSML, volume 190 of Proceedings of Machine Learning Research, page 49-64. PMLR, (2022)Stroboscopic versus nonstroboscopic dynamics in the Floquet realization of the Harper-Hofstadter HamiltonianM. Bukov, and A. Polkovnikov. Phys. Rev. A, 90 (4): 043613 (October 2014)Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks.J. Yao, P. Köttering, H. Gundlach, L. Lin, and M. Bukov. MSML, volume 145 of Proceedings of Machine Learning Research, page 1044-1081. PMLR, (2021)Policy Gradient based Quantum Approximate Optimization Algorithm.J. Yao, M. Bukov, and L. Lin. MSML, volume 107 of Proceedings of Machine Learning Research, page 605-634. PMLR, (2020)Adaptive trotterization for time-dependent hamiltonian quantum dynamics using piecewise conservation lawsH. Zhao, M. Bukov, M. Heyl, and R. Moessner. Phys. Rev. Lett., 133 (1): 010603 (Jul 2, 2024)Making trotterization adaptive and energy-self-correcting for NISQ devices and beyondH. Zhao, M. Bukov, M. Heyl, and R. Moessner. PRX Quantum, 4 (3): 030319 (Aug 9, 2023)A high-bias, low-variance introduction to Machine Learning for physicistsP. Mehta, M. Bukov, C. Wang, A. Day, C. Richardson, C. Fisher, and D. Schwab. (2018)cite arxiv:1803.08823Comment: Notebooks have been updated. 122 pages, 78 figures, 20 Python notebooks.Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations.P. Tashev, S. Petrov, F. Metz, and M. Bukov. CoRR, (2024)

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Disambiguation of "Bukov, Marin"

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A high-bias, low-variance introduction to Machine Learning for physicists

Please choose a person to relate this publication to

Aleksej Bukov

Petar Bukovac

Bernhard Bukovc

Else Marin

Richard Marin

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Bukov, Marin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A high-bias, low-variance introduction to Machine Learning for physicists

Please choose a person to relate this publication to

Aleksej Bukov

Petar Bukovac

Bernhard Bukovc

Else Marin

Richard Marin

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

A high-bias, low-variance introduction to Machine Learning for physicists