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Total Size:
23.9 MB
Info Hash:
D5595CBC9D5651FC8AF0C076E990FE4E1CEF3653
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Added:
Dec. 27, 2025, 1:30 p.m.
Stats:
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(Last updated: Dec. 27, 2025, 1:33 p.m.)
| File | Size |
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| Yan F. Exploring Complex Networks with Quantum Walks 2025.pdf | 23.9 MB |
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23.9 MB
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2025-12-27
| Uploaded by andryold1 | Size 23.9 MB | Health [ 69 /17 ] | Added 2025-12-27 |
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913.9 MB
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2023-06-02
| Uploaded by somnath2003 | Size 913.9 MB | Health [ 56 /18 ] | Added 2023-06-02 |
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60.5 MB
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2025-08-11
| Uploaded by andryold1 | Size 60.5 MB | Health [ 23 /18 ] | Added 2025-08-11 |
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48.6 MB
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2024-02-01
| Uploaded by FreeCourseWeb | Size 48.6 MB | Health [ 0 /7 ] | Added 2024-02-01 |
NOTE
SOURCE: Yan F. Exploring Complex Networks with Quantum Walks 2025
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COVER
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MEDIAINFO
Textbook in PDF format
This book explores the intersection of quantum computing and network science. It bridges the theoretical foundations of quantum walk algorithms with their applications in the structural exploration and representation learning of complex networks.
Quantum walks, a technology that is pivotal to universal quantum computational models, examines the movement of particles on a graph composed of nodes and links. Quantum superposition enables these particles to traverse these graphs more quickly, while measurement-induced collapse introduces fluctuations, making the identification of critical nodes challenging yet intriguing. At its core, the book explores how quantum walk algorithms can transform the structural and representational analysis of complex networks. It begins by introducing the fundamental concepts of quantum computing and quantum walks, including generalized definitions and the properties of low-dimensional quantum walks. Then, it discusses the implementation of discrete- and continuous-time quantum walks for mining network nodes, links, and subgraphs, as well as their use in network representation learning and graph neural networks
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