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Modelling Non-Markovian Quantum Systems Using Tensor Networks (Paperback, 1st ed. 2020) Loot Price: R3,560
Discovery Miles 35 600
Modelling Non-Markovian Quantum Systems Using Tensor Networks (Paperback, 1st ed. 2020): Aidan Strathearn

Modelling Non-Markovian Quantum Systems Using Tensor Networks (Paperback, 1st ed. 2020)

Aidan Strathearn

Series: Springer Theses

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Loot Price R3,560 Discovery Miles 35 600 | Repayment Terms: R334 pm x 12*

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This thesis presents a revolutionary technique for modelling the dynamics of a quantum system that is strongly coupled to its immediate environment. This is a challenging but timely problem. In particular it is relevant for modelling decoherence in devices such as quantum information processors, and how quantum information moves between spatially separated parts of a quantum system. The key feature of this work is a novel way to represent the dynamics of general open quantum systems as tensor networks, a result which has connections with the Feynman operator calculus and process tensor approaches to quantum mechanics. The tensor network methodology developed here has proven to be extremely powerful: For many situations it may be the most efficient way of calculating open quantum dynamics. This work is abounds with new ideas and invention, and is likely to have a very significant impact on future generations of physicists.

General

Imprint: Springer Nature Switzerland AG
Country of origin: Switzerland
Series: Springer Theses
Release date: September 2021
First published: 2020
Authors: Aidan Strathearn
Dimensions: 235 x 155mm (L x W)
Format: Paperback
Pages: 103
Edition: 1st ed. 2020
ISBN-13: 978-3-03-054977-0
Categories: Books > Science & Mathematics > Mathematics > Probability & statistics
Books > Science & Mathematics > Mathematics > Applied mathematics > General
Books > Science & Mathematics > Physics > Quantum physics (quantum mechanics) > General
Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
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LSN: 3-03-054977-1
Barcode: 9783030549770

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