05390nam 2200649 450 99642632820331620190721112312.00-12-801099-1(CKB)2550000001352487(EBL)1790890(SSID)ssj0001377357(PQKBManifestationID)12603891(PQKBTitleCode)TC0001377357(PQKBWorkID)11318734(PQKB)11007458(MiAaPQ)EBC1790890(EXLCZ)99255000000135248720140920h20142014 uy 0engur|n|---|||||txtrdacontentcrdamediacrrdacarrierQuantum machine learning what quantum computing means to data mining /Peter WittekFirst editionSan Diego, California :Academic Press,2014©20141 online resource (176 p.)Elsevier InsightsDescription based upon print version of record0-12-800953-5 1-322-11434-X Includes bibliographical referencesFront Cover; Quantum Machine Learning: What Quantum Computing Meansto Data Mining; Copyright; Contents; Preface; Notations; Part One Fundamental Concepts; Chapter 1: Introduction; 1.1Learning Theory and Data Mining; 1.2.Why Quantum Computers?; 1.3.A Heterogeneous Model; 1.4.An Overview of Quantum Machine Learning Algorithms; 1.5.Quantum-Like Learning on Classical Computers; Chapter 2: Machine Learning; 2.1.Data-Driven Models; 2.2.Feature Space; 2.3.Supervised and Unsupervised Learning; 2.4.Generalization Performance; 2.5.Model Complexity; 2.6.Ensembles2.7.Data Dependencies and Computational ComplexityChapter 3: Quantum Mechanics; 3.1.States and Superposition; 3.2.Density Matrix Representation and Mixed States; 3.3.Composite Systems and Entanglement; 3.4.Evolution; 3.5.Measurement; 3.6.Uncertainty Relations; 3.7.Tunneling; 3.8.Adiabatic Theorem; 3.9.No-Cloning Theorem; Chapter 4:Quantum Computing; 4.1.Qubits and the Bloch Sphere; 4.2.Quantum Circuits; 4.3.Adiabatic Quantum Computing; 4.4.Quantum Parallelism; 4.5.Grover''s Algorithm; 4.6.Complexity Classes; 4.7.Quantum Information Theory; Part Two Classical Learning AlgorithmsChapter 5:Unsupervised Learning5.1.Principal Component Analysis; 5.2.Manifold Embedding; 5.3.K-Means and K-Medians Clustering; 5.4.Hierarchical Clustering; 5.5.Density-Based Clustering; Chapter 6:Pattern Recognition and Neural Networks; 6.1.The Perceptron; 6.2.Hopfield Networks; 6.3.Feedforward Networks; 6.4.Deep Learning; 6.5.Computational Complexity; Chapter 7:Supervised Learning and Support Vector Machines; 7.1.K-Nearest Neighbors; 7.2.Optimal Margin Classifiers; 7.3.Soft Margins; 7.4.Nonlinearity and Kernel Functions; 7.5.Least-Squares Formulation; 7.6.Generalization Performance7.7.Multiclass Problems7.8.Loss Functions; 7.9.Computational Complexity; Chapter 8:Regression Analysis; 8.1.Linear Least Squares; 8.2.Nonlinear Regression; 8.3.Nonparametric Regression; 8.4.Computational Complexity; Chapter 9:Boosting; 9.1.Weak Classifiers; 9.2.AdaBoost; 9.3.A Family of Convex Boosters; 9.4.Nonconvex Loss Functions; Part Three Quantum Computing and Machine Learning; Chapter 10:Clustering Structure and Quantum Computing; 10.1.Quantum Random Access Memory; 10.2.Calculating Dot Products; 10.3.Quantum Principal Component Analysis; 10.4.Toward Quantum Manifold Embedding10.5.Quantum K-Means10.6.Quantum K-Medians; 10.7.Quantum Hierarchical Clustering; 10.8.Computational Complexity; Chapter 11:Quantum Pattern Recognition; 11.1.Quantum Associative Memory; 11.2.The Quantum Perceptron; 11.3.Quantum Neural Networks; 11.4.Physical Realizations; 11.4.Computational Complexity; Chapter 12:Quantum Classification; 12.1.Nearest Neighbors; 12.2.Support Vector Machines with Grover''s Search; 12.3.Support Vector Machines with Exponential Speedup; 12.4.Computational Complexity; Chapter 13:Quantum Process Tomography and Regression; 13.1.Channel-State Duality13.2.Quantum Process TomographyQuantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine LElsevier insights.Machine learningMathematical modelsData miningData processingQuantum theoryData processingElectronic booksMachine learningMathematical models.Data miningData processing.Quantum theoryData processing.621.3822Wittek Peter1017929MiAaPQMiAaPQMiAaPQBOOK996426328203316Quantum machine learning2390978UNISA01148nam0 22002531i 450 RML023359520231121125659.020121121d1990 ||||0itac50 baitaitz01i xxxe z01n˜La œcostituzione economica a quarant'anni dall'approvazione della carta fondamentaleAtti del convegno Milano, 6-7 maggio 1988scritti di Francesco Saja...[et al.]Milano Giuffr 1990viii,422 p.24 cmIn testa al front. : Università cattolica del S.Cuore di Milano Dipartimento di diritto privato e pubblico dell'economia.ITIT-0120121121IT-FR0098 Biblioteca Area Giuridico EconomicaFR0098 RML0233595Biblioteca Area Giuridico Economica 53IMP 346/90 53VM 0000018595 VM barcode:ECO004826. - Inventario:1541. - Fondo:Sala consultazioneVMA 1994112120121204 53Costituzione economica a quarant'anni dall'approvazione della carta fondamentale62811UNICAS01847nam1 2200397 i 450 CFI016464520231121125442.0884250139520020701d1990 ||||0itac50 baitaitz01i xxxe z01n˜I œsupporti nelle arti pittorichestoria, tecnica, restauroa cura di Corrado MalteseMilanoMursia©19902 v.21 cmIn custodia.001CFI01646492001 ˜1: œSupporti parietali per graffiti e dipinti, mosaici e tarsie, supporti lapidei e vitrei, supporti metallici, gli smalti, supporti lignei[E. I. Basile ... et al.]1001CFI01646532001 ˜2: œIntroduzione ai supporti tessili, le tele per la pittura, gli arazzi, i tappeti, il cuoio, la pergamena, la carta[A. P. Agati ... et al.]2RestauroFIRRMLC002716IArteTecnicaFIRRMLC273842IPitturaRestauroFIRRMLC163429IPitturaSupportiStoriaFIRRMLC185020I702.8BELLE ARTI E ARTI DECORATIVE. TECNICHE, PROCEDURE, APPARECCHI, ATTREZZATURE, MATERIALI19Maltese, CorradoCFIV005336ITIT-0120020701IT-RM028 IT-RM1163 IT-FR0017 Biblioteca Universitaria AlessandrinaRM028 Istituto Centrale Restauro Conservazione Patrimonio Archivistico LibrarioRM1163 Biblioteca umanistica Giorgio ApreaFR0017 CFI0164645Biblioteca umanistica Giorgio Aprea 52SALA BRAGAS.A. 203.1 52SALA BRAGAS.A. 203.22 01 36 52Supporti nelle arti pittoriche1406951UNICAS