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Density matrix theory and applications
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ISBN: 0306406845 Year: 1981 Publisher: New York (N.Y.) : Plenum press,

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Density matrix theory and applications
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ISBN: 9783642205606 Year: 2012 Publisher: Heidelberg ; New York, NY : Springer,

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Reduced density matrices in quantum chemistry
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ISBN: 012205850X Year: 1976 Publisher: New York (N.Y.) : Academic press,

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Many-electron densities and reduced density matrices
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ISBN: 0306464543 Year: 2000 Publisher: New York (N.Y.) : Kluwer academic/Plenum,

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Lectures on light : nonlinear and quantum optics using the density matrix.
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ISBN: 9780199574872 Year: 2010 Publisher: Oxford Oxford university press

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This book bridges a gap between introductory optics texts and the vanguard of optical science, where light is used as a tool to probe the properties of new materials. A single mathematical tool is introduced that enables readers to understand laser tweezers, laser cooling, optical magnetism, squeezed light, and many other advanced topics.


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Density functional theory
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ISBN: 3540127216 0387127216 9783540387039 354038703X Year: 1983 Publisher: Berlin ; New York : Springer-Verlag,

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Reduced density matrices : Coulson's challenge
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ISBN: 354067148X 9783540671480 Year: 2000 Volume: 72 Publisher: Berlin : Springer,

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2D NMR: density matrix and product operator treatment
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ISBN: 013013368X 9780130133687 Year: 1993 Publisher: Englewood Cliffs (N.J.): Prentice Hall,

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Density matrices and density functionals : proceedings of the A. John Coleman symposium
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ISBN: 9027724776 Year: 1987 Publisher: Dordrecht Reidel

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Density matrix and tensor network renormalization
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ISBN: 9781009398671 1009398679 1009398717 9781009398701 Year: 2023 Publisher: Cambridge ; New York, NY Cambridge University Press

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Renormalization group theory of tensor network states provides a powerful tool for studying quantum many-body problems and a new paradigm for understanding entangled structures of complex systems. In recent decades the theory has rapidly evolved into a universal framework and language employed by researchers in fields ranging from condensed matter theory to machine learning. This book presents a pedagogical and comprehensive introduction to this field for the first time. After an introductory survey on the major advances in tensor network algorithms and their applications, it introduces step-by-step the tensor network representations of quantum states and the tensor-network renormalization group methods developed over the past three decades. Basic statistical and condensed matter physics models are used to demonstrate how the tensor network renormalization works. An accessible primer for scientists and engineers, this book would also be ideal as a reference text for a graduate course in this area.

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