Quantum machine learning : (Record no. 141425)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 01562nam a22002417a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260714154943.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260714s2024 |||||||| |||| 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783031442285 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | PK-LaUMT |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | CON-Q |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Conti, Claudio |
| 245 10 - TITLE STATEMENT | |
| Title | Quantum machine learning : |
| Remainder of title | thinking and exploration in neural network models for quantum science and quantum computing / |
| Statement of responsibility, etc | Claudio Conti |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Cham : |
| Name of publisher, distributor, etc | Springer, |
| Date of publication, distribution, etc | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxiii, 378 p. |
| 490 ## - SERIES STATEMENT | |
| Series statement | Quantum science and technology |
| 500 ## - GENERAL NOTE | |
| General note | Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits' performance. |
| 546 ## - LANGUAGE NOTE | |
| Language note | Eng |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Neural networking |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Quantum computing |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Home library | Current library | Date acquired | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|
| UMT Main Campus | UMT Main Campus | 2026-06-13 | 006.31 CON-Q | 153358 | 2026-07-14 | 2026-07-14 | Books |