M
Marin Bukov
Researcher at University of California, Berkeley
Publications - 57
Citations - 4223
Marin Bukov is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Floquet theory & Quantum. The author has an hindex of 22, co-authored 42 publications receiving 2717 citations. Previous affiliations of Marin Bukov include Sofia University & Boston University.
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Universal high-frequency behavior of periodically driven systems: from dynamical stabilization to Floquet engineering
TL;DR: In this article, a general overview of the high-frequency regime in periodically driven systems and three distinct classes of driving protocols in which the infinite-frequency Floquet Hamiltonian is not equal to the time-averaged Hamiltonian are identified.
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A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta,Marin Bukov,Ching-Hao Wang,Alexandre G. R. Day,Charles C. Richardson,Charles K. Fisher,David J. Schwab +6 more
TL;DR: The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning.
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Reinforcement Learning in Different Phases of Quantum Control
Marin Bukov,Alexandre G. R. Day,Dries Sels,Dries Sels,Phillip Weinberg,Anatoli Polkovnikov,Pankaj Mehta +6 more
TL;DR: This work implements cutting-edge Reinforcement Learning techniques and shows that their performance is comparable to optimal control methods in the task of finding short, high-fidelity driving protocol from an initial to a target state in non-integrable many-body quantum systems of interacting qubits.
Journal ArticleDOI
A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta,Marin Bukov,Ching-Hao Wang,Alexandre G. R. Day,Charles C. Richardson,Charles K. Fisher,David J. Schwab +6 more
TL;DR: In this paper, the authors provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists and emphasize the many natural connections between ML and statistical physics.
Journal ArticleDOI
QuSpin: a Python Package for Dynamics and Exact Diagonalisation of Quantum Many Body Systems part I: spin chains
Phillip Weinberg,Marin Bukov +1 more
TL;DR: QuSpin this paper is an open-source Python package for exact diagonalization and quantum dynamics of spin-photon chains, supporting the use of various symmetries in 1-dimensional and (imaginary) time evolution for chains up to 32 sites in length.