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[null,"1.","Record Nr.",null,"UNINA9910506399603321"]
[null,null,"Autore",null,"Schuld Maria"]
[null,null,"Titolo",null,"Machine Learning with Quantum Computers / / by Maria Schuld, Francesco Petruccione"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-83098-5"]
[null,null,"Edizione",null,"[2nd ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (321 pages)"]
[null,null,"Collana",null,"Quantum Science and Technology, , 2364-9062"]
[null,null,"Disciplina",null,"004.1"]
[null,null,"Soggetti",null,"Quantum computers"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Mathematics"]
[{"$nulls":4},"Quantum Computing"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Mathematics and Computing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Chapter 1. Introduction -- Chapter 2. Machine Learning -- Chapter 3. Quantum Computing -- Chapter 4. Representing Data on a Quantum Computer -- Chapter 5. Variational Circuits as Machine Learning Models -- Chapter 6. Quantum Models as Kernel Methods -- Chapter 7. Fault-Tolerant Quantum Machine Learning -- Chapter 8. Approaches Based on the Ising Model -- Chapter 9. Potential Quantum Advantages."]
[null,null,null,"Sommario/riassunto","This book offers an introduction into quantum machine learning research, covering approaches that range from \"near-term\" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years."]