東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Neural-network simulation of strongl...

Czischek, Stefanie.

FindBook

Google Book

Amazon

博客來

Neural-network simulation of strongly correlated quantum systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neural-network simulation of strongly correlated quantum systems/ by Stefanie Czischek.
作者:	Czischek, Stefanie.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xv, 205 p. :ill., digital ;24 cm.
內容註:	Introduction -- Quantum Mechanics and Spin Systems -- Artiﬁcial Neural Networks -- Discrete Truncated Wigner Approximation -- BM-Based Wave Function Parametrization -- Deep Neural Networks and Phase Reweighting -- Towards Neuromorphic Sampling of Quantum States -- Conclusion.
Contained By:	Springer Nature eBook
標題:	Quantum systems. -
電子資源:	https://doi.org/10.1007/978-3-030-52715-0
ISBN:	9783030527150

Neural-network simulation of strongly correlated quantum systems
Czischek, Stefanie.

Neural-network simulation of strongly correlated quantum systems[electronic resource] /by Stefanie Czischek. - Cham :Springer International Publishing :2020. - xv, 205 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Quantum Mechanics and Spin Systems -- Artiﬁcial Neural Networks -- Discrete Truncated Wigner Approximation -- BM-Based Wave Function Parametrization -- Deep Neural Networks and Phase Reweighting -- Towards Neuromorphic Sampling of Quantum States -- Conclusion.

Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.

ISBN: 9783030527150

Standard No.: 10.1007/978-3-030-52715-0doiSubjects--Topical Terms:

2089209
Quantum systems.

LC Class. No.: QC173.96 / .C95 2020

Dewey Class. No.: 530.12

Neural-network simulation of strongly correlated quantum systems
LDR:02745nmm a2200337 a 4500 001 2256126
003 DE-He213
005 20200827153241.0
006 m d
007 cr nn 008maaau
008 220420s2020 sz s 0 eng d
020 $a 9783030527150 $q (electronic bk.)
020 $a 9783030527143 $q (paper)
024 7 $a 10.1007/978-3-030-52715-0 $2 doi
035 $a 978-3-030-52715-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QC173.96 $b .C95 2020
072 7 $a PHQ $2 bicssc
072 7 $a SCI057000 $2 bisacsh
072 7 $a PHQ $2 thema
082 0 4 $a 530.12 $2 23
090 $a QC173.96 $b .C998 2020
100 1 $a Czischek, Stefanie. $3 3526157
245 1 0 $a Neural-network simulation of strongly correlated quantum systems $h [electronic resource] / $c by Stefanie Czischek.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xv, 205 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
505 0 $a Introduction -- Quantum Mechanics and Spin Systems -- Artiﬁcial Neural Networks -- Discrete Truncated Wigner Approximation -- BM-Based Wave Function Parametrization -- Deep Neural Networks and Phase Reweighting -- Towards Neuromorphic Sampling of Quantum States -- Conclusion.
520 $a Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.
650 0 $a Quantum systems. $3 2089209
650 0 $a Quantum theory. $3 516552
650 0 $a Neural networks (Computer science) $3 532070
650 1 4 $a Quantum Physics. $3 893952
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
650 2 4 $a Condensed Matter Physics. $3 1067080
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer theses. $3 1314442
856 4 0 $u https://doi.org/10.1007/978-3-030-52715-0
950 $a Physics and Astronomy (SpringerNature-11651)