Quantum machine learning (Record no. 567558)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 02598 a2200229 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250728123659.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250724b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031442254 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3843 |
| Item number | C767q |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Conti, Claudio |
| 245 ## - 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) | |
| Name of publisher | Springer |
| Year of publication | 2024 |
| Place of publication | Switzerland |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xxiii, 378p |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Title | Quantum science and technology |
| 490 ## - SERIES STATEMENT | |
| Series statement | / edited by Raymond Laflamme |
| 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. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Quantum computing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Quantum theory |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| On Display | PK Kelkar Library, IIT Kanpur | PK Kelkar Library, IIT Kanpur | 24/07/2025 | 2 | 9776.55 | 006.3843 C767q | A186929 | 13035.40 | Books |