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Hands-on accelerator physics using MATLAB® / / Volker Ziemann
| Hands-on accelerator physics using MATLAB® / / Volker Ziemann |
| Autore | Ziemann Volker (Associate professor of physics) |
| Pubbl/distr/stampa | Boca Raton, FL : , : CRC Press, Taylor & Francis Group, , [2019] |
| Descrizione fisica | 1 online resource (373 pages) |
| Disciplina | 30.1201514 |
| Soggetto topico |
Quantum theory - Data processing
Particles (Nuclear physics) |
| ISBN |
0-429-95746-7
0-429-95747-5 0-429-49129-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910793430603321 |
Ziemann Volker (Associate professor of physics)
|
||
| Boca Raton, FL : , : CRC Press, Taylor & Francis Group, , [2019] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Hands-on accelerator physics using MATLAB® / / Volker Ziemann
| Hands-on accelerator physics using MATLAB® / / Volker Ziemann |
| Autore | Ziemann Volker (Associate professor of physics) |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boca Raton, FL : , : CRC Press, Taylor & Francis Group, , [2019] |
| Descrizione fisica | 1 online resource (373 pages) |
| Disciplina | 30.1201514 |
| Soggetto topico |
Quantum theory - Data processing
Particles (Nuclear physics) |
| ISBN |
0-429-95746-7
0-429-95747-5 0-429-49129-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- CHAPTER 1: Introduction and History -- CHAPTER 2: Reference System -- 2.1 THE REFERENCE TRAJECTORY -- 2.2 COORDINATE TRANSFORMATIONS -- 2.3 PARTICLES AND THEIR DESCRIPTION -- 2.4 PARTICLE ENSEMBLES, BUNCHES -- CHAPTER 3: Transverse Beam Optics -- 3.1 MAGNETS AND MATRICES -- 3.1.1 Thin quadrupoles -- 3.1.2 Thick quadrupoles -- 3.1.3 Sector dipole -- 3.1.4 Combined function dipole -- 3.1.5 Rectangular dipole -- 3.1.6 Coordinate rotation -- 3.1.7 Solenoid -- 3.1.8 Non-linear elements -- 3.2 PROPAGATING PARTICLES AND BEAMS -- 3.3 TWO-DIMENSIONAL -- 3.3.1 Beam optics in MATLAB -- 3.3.2 Poincarè section and tune -- 3.3.3 FODO cell and beta functions -- 3.3.4 A complementary look at beta functions -- 3.3.5 Beam size and emittance -- 3.4 CHROMATICITY AND DISPERSION -- 3.4.1 Chromaticity -- 3.4.2 Dispersion -- 3.4.3 Emittance generation -- 3.4.4 Momentum compaction factor -- 3.5 FOUR-DIMENSIONAL AND COUPLING -- 3.6 MATCHING -- 3.6.1 Matching the phase advance -- 3.6.2 Match beta functions to a waist -- 3.6.3 Point-to-point focusing -- 3.7 BEAM-OPTICAL SYSTEMS -- 3.7.1 Telescopes -- 3.7.2 Triplets -- 3.7.3 Doublets -- 3.7.4 Achromats -- 3.7.5 Multi-bend achromats -- 3.7.6 TME cell -- 3.7.7 Dispersion suppressor -- 3.7.8 Interaction region -- 3.7.9 Bunch compressors -- CHAPTER 4: Magnets -- 4.1 MAXWELL'S EQUATIONS AND BOUNDARY CONDITIONS -- 4.2 2D-GEOMETRIES AND MULTIPOLES -- 4.3 IRON-DOMINATED MAGNETS -- 4.3.1 Simple analytical methods -- 4.3.2 Using the MATLAB PDE toolbox -- 4.3.3 Quadrupoles -- 4.3.4 Technological aspects -- 4.4 SUPER-CONDUCTING MAGNETS -- 4.4.1 Simple analytical methods -- 4.4.2 PDE toolbox -- 4.5 PERMANENT MAGNETS -- 4.5.1 Multipoles -- 4.5.2 Segmented multipoles -- 4.5.3 Undulators and wigglers -- 4.6 MAGNET MEASUREMENTS.
4.6.1 Hall probe -- 4.6.2 Rotating coil -- 4.6.3 Undulator measurements -- CHAPTER 5: Longitudinal Dynamics and Acceleration -- 5.1 PILL-BOX CAVITY -- 5.2 TRANSIT-TIME FACTOR -- 5.3 PHASE STABILITY AND SYNCHROTRON OSCILLATIONS -- 5.4 LARGE-AMPLITUDE OSCILLATIONS -- 5.5 RF GYMNASTICS -- 5.6 ACCELERATION -- 5.7 A SIMPLE WORKED EXAMPLE -- CHAPTER 6: Radio-Frequency Systems -- 6.1 POWER GENERATION AND CONTROL -- 6.2 POWER TRANSPORT: WAVEGUIDES AND TRANSMISSION LINES -- 6.3 COUPLERS AND ANTENNAS -- 6.4 POWER TO THE BEAM: RESONATORS AND CAVITIES -- 6.4.1 Losses and quality factor Q0 of a pill-box cavity -- 6.4.2 General cavity geometry with the PDE toolbox -- 6.4.3 Disk-loaded waveguides -- 6.5 TECHNOLOGICAL ASPECTS -- 6.5.1 Normal-conducting -- 6.5.2 Super-conducting -- 6.6 INTERACTION WITH THE BEAM -- 6.6.1 Beam loading -- 6.6.2 Steady-state operation -- 6.6.3 Pulsed operation and transient beam loading -- 6.6.4 Low-level RF system -- CHAPTER 7: Instrumentation and Diagnostics -- 7.1 ZEROTH MOMENT: CURRENT -- 7.2 FIRST MOMENT: BEAM POSITION AND ARRIVAL TIME -- 7.3 SECOND MOMENT: BEAM SIZE -- 7.4 EMITTANCE AND BETA FUNCTIONS -- 7.5 SPECIALTY DIAGNOSTICS -- 7.5.1 Turn-by-turn position monitor data analysis -- 7.5.2 Beam-beam diagnostics -- 7.5.3 Schottky diagnostics -- CHAPTER 8: Imperfections and Their Correction -- 8.1 SOURCES OF IMPERFECTIONS -- 8.1.1 Misalignment and feed down -- 8.1.2 Tilted components -- 8.1.3 Rolled elements and solenoids -- 8.1.4 Chromatic effects -- 8.1.5 Consequences -- 8.2 IMPERFECTIONS IN BEAM LINES -- 8.2.1 Dipole kicks and orbit errors -- 8.2.2 Quadrupolar errors and beam size -- 8.2.3 Skew-quadrupolar perturbations -- 8.2.4 Filamentation -- 8.3 IMPERFECTIONS IN A RING -- 8.3.1 Misalignment and dipole kicks -- 8.3.2 Gradient imperfections -- 8.3.3 Skew-gradient imperfections -- 8.4 CORRECTION IN BEAM LINES. 8.4.1 Trajectory knobs and bumps -- 8.4.2 Orbit correction -- 8.4.3 Beta matching -- 8.4.4 Dispersion and chromaticity -- 8.5 CORRECTION IN RINGS -- 8.5.1 Orbit correction -- 8.5.2 Dispersion-free steering -- 8.5.3 Tune correction -- 8.5.4 Chromaticity correction -- 8.5.5 Coupling correction -- 8.5.6 Orbit response-matrix based methods -- 8.5.7 Feedback systems -- CHAPTER 9: Targets and Luminosity -- 9.1 EVENT RATE AND LUMINOSITY -- 9.2 ENERGY LOSS AND STRAGGLING -- 9.3 TRANSVERSE SCATTERING, EMITTANCE GROWTH, AND LIFE-TIME -- 9.4 COLLIDING BEAMS -- 9.5 BEAM-BEAM LUMINOSITY -- 9.6 INCOHERENT BEAM-BEAM TUNE SHIFT -- 9.7 COHERENT BEAM-BEAM INTERACTIONS -- 9.8 LINEAR COLLIDERS -- CHAPTER 10: Synchrotron Radiation and Free-Electron Lasers -- 10.1 EFFECT ON THE BEAM -- 10.1.1 Longitudinally -- 10.1.2 Vertically -- 10.1.3 Horizontally -- 10.1.4 Quantum lifetime -- 10.2 CHARACTERISTICS OF THE EMITTED RADIATION -- 10.2.1 Dipole magnets -- 10.2.2 Undulators and wigglers -- 10.3 SMALL-GAIN FREE-ELECTRON LASER -- 10.3.1 Amplifier and oscillator -- 10.4 SELF-AMPLIFIED SPONTANEOUS EMISSION -- 10.5 ACCELERATOR CHALLENGES -- CHAPTER 11: Non-linear Dynamics -- 11.1 A ONE-DIMENSIONAL TOY MODEL -- 11.2 TRACKING AND DYNAMIC APERTURE -- 11.3 HAMILTONIANS AND LIE-MAPS -- 11.3.1 Moving Hamiltonians -- 11.3.2 Concatenating Hamiltonians -- 11.4 IMPLEMENTATION IN MATLAB -- 11.5 TWO-DIMENSIONAL MODEL -- 11.6 KNOBS AND RESONANCE-DRIVING TERMS -- 11.7 NON-RESONANT NORMAL FORMS -- CHAPTER 12: Collective Effects -- 12.1 SPACE CHARGE -- 12.2 INTRABEAM SCATTERING AND TOUSCHEK-EFFECT -- 12.3 WAKE FIELDS, IMPEDANCES, AND LOSS FACTORS -- 12.4 COASTING-BEAM INSTABILITY -- 12.5 SINGLE-BUNCH INSTABILITIES -- 12.6 MULTI-BUNCH INSTABILITIES -- CHAPTER 13: Accelerator Subsystems -- 13.1 CONTROL SYSTEM -- 13.1.1 Sensors, actuators, and interfaces -- 13.1.2 System architecture. 13.1.3 Timing system -- 13.1.4 An example: EPICS -- 13.2 PARTICLE SOURCES -- 13.2.1 Electrons -- 13.2.2 Protons and other ions -- 13.2.3 Highly charged ions -- 13.2.4 Negatively charged ions -- 13.2.5 Radio-frequency quadrupole -- 13.3 INJECTION AND EXTRACTION -- 13.4 BEAM COOLING -- 13.5 VACUUM -- 13.5.1 Vacuum basics -- 13.5.2 Pumps and gauges -- 13.5.3 Vacuum calculations -- 13.6 CRYOGENICS -- 13.7 RADIATION PROTECTION AND SAFETY -- 13.7.1 Units -- 13.7.2 Range of radiation in matter -- 13.7.3 Dose measurements -- 13.7.4 Personnel and machine protection -- 13.8 CONVENTIONAL FACILITIES -- 13.8.1 Electricity -- 13.8.2 Water and cooling -- 13.8.3 Buildings and shielding -- CHAPTER 14: Examples of Accelerators -- 14.1 CERN AND THE LARGE HADRON COLLIDER -- 14.2 EUROPEAN SPALLATION SOURCE -- 14.3 SLAC AND THE LINAC COHERENT LIGHT SOURCE -- 14.4 MAX-IV -- 14.5 TANDEM ACCELERATOR IN UPPSALA -- 14.6 ACCELERATORS FOR MEDICAL APPLICATIONS -- 14.7 INDUSTRIAL ACCELERATORS -- APPENDIX A: The Student Labs -- A.1 BEAM PROFILE OF LASER POINTER -- A.2 EMITTANCE MEASUREMENT WITH A LASER POINTER -- A.3 HALBACH MULTIPOLES AND UNDULATORS -- A.4 MAGNET MEASUREMENTS -- A.5 COOKIE-JAR CAVITY ON A NETWORK ANALYZER -- APPENDIX B: Appendices Available Online -- B.1 LINEAR ALGEBRA -- B.2 MATLAB PRIMER -- B.3 OPENSCAD PRIMER -- B.4 LIGHT OPTICS, RAYS, AND GAUSSIAN -- B.5 MATLAB FUNCTIONS -- Bibliography -- Index. |
| Record Nr. | UNINA-9910973814603321 |
|
Ziemann Volker (Associate professor of physics)
|
||
| Boca Raton, FL : , : CRC Press, Taylor & Francis Group, , [2019] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Hands-On Accelerator Physics Using MATLAB®
| Hands-On Accelerator Physics Using MATLAB® |
| Autore | Ziemann Volker |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Milton : , : Taylor & Francis Group, , 2019 |
| Descrizione fisica | 1 online resource (416 pages) |
| Disciplina | 539.73 |
| Soggetto topico |
Particle accelerators
Particles (Nuclear physics) Quantum theory - Data processing |
| ISBN |
9781003463283
1003463282 9781040314319 1040314317 9781040314395 1040314392 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- CHAPTER 1: Introduction and History -- CHAPTER 2: Reference System -- 2.1 THE REFERENCE TRAJECTORY -- 2.2 COORDINATE TRANSFORMATIONS -- 2.3 PARTICLES AND THEIR DESCRIPTION -- 2.4 PARTICLE ENSEMBLES, BUNCHES -- CHAPTER 3: Transverse Beam Optics -- 3.1 MAGNETS AND MATRICES -- 3.1.1 Thin quadrupoles -- 3.1.2 Thick quadrupoles -- 3.1.3 Sector dipole -- 3.1.4 Combined function dipole -- 3.1.5 Rectangular dipole -- 3.1.6 Coordinate rotation -- 3.1.7 Solenoid -- 3.1.8 Non-linear elements -- 3.2 PROPAGATING PARTICLES AND BEAMS -- 3.3 TWO-DIMENSIONAL -- 3.3.1 Beam optics in MATLAB -- 3.3.2 Poincarè section and tune -- 3.3.3 FODO cell and beta functions -- 3.3.4 A complementary look at beta functions -- 3.3.5 Beam size and emittance -- 3.4 CHROMATICITY AND DISPERSION -- 3.4.1 Chromaticity -- 3.4.2 Dispersion -- 3.4.3 Emittance generation -- 3.4.4 Momentum compaction factor -- 3.5 FOUR-DIMENSIONAL AND COUPLING -- 3.6 MATCHING -- 3.6.1 Matching the phase advance -- 3.6.2 Match beta functions to a waist -- 3.6.3 Point-to-point focusing -- 3.7 BEAM-OPTICAL SYSTEMS -- 3.7.1 Telescopes -- 3.7.2 Triplets -- 3.7.3 Doublets -- 3.7.4 Achromats -- 3.7.5 Multi-bend achromats -- 3.7.6 TME cell -- 3.7.7 Dispersion suppressor -- 3.7.8 Interaction region -- 3.7.9 Bunch compressors -- CHAPTER 4: Magnets -- 4.1 MAXWELL'S EQUATIONS AND BOUNDARY CONDITIONS -- 4.2 2D-GEOMETRIES AND MULTIPOLES -- 4.3 IRON-DOMINATED MAGNETS -- 4.3.1 Simple analytical methods -- 4.3.2 Using the MATLAB PDE toolbox -- 4.3.3 Quadrupoles -- 4.3.4 Technological aspects -- 4.4 SUPER-CONDUCTING MAGNETS -- 4.4.1 Simple analytical methods -- 4.4.2 PDE toolbox -- 4.5 PERMANENT MAGNETS -- 4.5.1 Multipoles -- 4.5.2 Segmented multipoles -- 4.5.3 Undulators and wigglers -- 4.6 MAGNET MEASUREMENTS. 4.6.1 Hall probe -- 4.6.2 Rotating coil -- 4.6.3 Undulator measurements -- CHAPTER 5: Longitudinal Dynamics and Acceleration -- 5.1 PILL-BOX CAVITY -- 5.2 TRANSIT-TIME FACTOR -- 5.3 PHASE STABILITY AND SYNCHROTRON OSCILLATIONS -- 5.4 LARGE-AMPLITUDE OSCILLATIONS -- 5.5 RF GYMNASTICS -- 5.6 ACCELERATION -- 5.7 A SIMPLE WORKED EXAMPLE -- CHAPTER 6: Radio-Frequency Systems -- 6.1 POWER GENERATION AND CONTROL -- 6.2 POWER TRANSPORT: WAVEGUIDES AND TRANSMISSION LINES -- 6.3 COUPLERS AND ANTENNAS -- 6.4 POWER TO THE BEAM: RESONATORS AND CAVITIES -- 6.4.1 Losses and quality factor Q0 of a pill-box cavity -- 6.4.2 General cavity geometry with the PDE toolbox -- 6.4.3 Disk-loaded waveguides -- 6.5 TECHNOLOGICAL ASPECTS -- 6.5.1 Normal-conducting -- 6.5.2 Super-conducting -- 6.6 INTERACTION WITH THE BEAM -- 6.6.1 Beam loading -- 6.6.2 Steady-state operation -- 6.6.3 Pulsed operation and transient beam loading -- 6.6.4 Low-level RF system -- CHAPTER 7: Instrumentation and Diagnostics -- 7.1 ZEROTH MOMENT: CURRENT -- 7.2 FIRST MOMENT: BEAM POSITION AND ARRIVAL TIME -- 7.3 SECOND MOMENT: BEAM SIZE -- 7.4 EMITTANCE AND BETA FUNCTIONS -- 7.5 SPECIALTY DIAGNOSTICS -- 7.5.1 Turn-by-turn position monitor data analysis -- 7.5.2 Beam-beam diagnostics -- 7.5.3 Schottky diagnostics -- CHAPTER 8: Imperfections and Their Correction -- 8.1 SOURCES OF IMPERFECTIONS -- 8.1.1 Misalignment and feed down -- 8.1.2 Tilted components -- 8.1.3 Rolled elements and solenoids -- 8.1.4 Chromatic effects -- 8.1.5 Consequences -- 8.2 IMPERFECTIONS IN BEAM LINES -- 8.2.1 Dipole kicks and orbit errors -- 8.2.2 Quadrupolar errors and beam size -- 8.2.3 Skew-quadrupolar perturbations -- 8.2.4 Filamentation -- 8.3 IMPERFECTIONS IN A RING -- 8.3.1 Misalignment and dipole kicks -- 8.3.2 Gradient imperfections -- 8.3.3 Skew-gradient imperfections -- 8.4 CORRECTION IN BEAM LINES. 8.4.1 Trajectory knobs and bumps -- 8.4.2 Orbit correction -- 8.4.3 Beta matching -- 8.4.4 Dispersion and chromaticity -- 8.5 CORRECTION IN RINGS -- 8.5.1 Orbit correction -- 8.5.2 Dispersion-free steering -- 8.5.3 Tune correction -- 8.5.4 Chromaticity correction -- 8.5.5 Coupling correction -- 8.5.6 Orbit response-matrix based methods -- 8.5.7 Feedback systems -- CHAPTER 9: Targets and Luminosity -- 9.1 EVENT RATE AND LUMINOSITY -- 9.2 ENERGY LOSS AND STRAGGLING -- 9.3 TRANSVERSE SCATTERING, EMITTANCE GROWTH, AND LIFE-TIME -- 9.4 COLLIDING BEAMS -- 9.5 BEAM-BEAM LUMINOSITY -- 9.6 INCOHERENT BEAM-BEAM TUNE SHIFT -- 9.7 COHERENT BEAM-BEAM INTERACTIONS -- 9.8 LINEAR COLLIDERS -- CHAPTER 10: Synchrotron Radiation and Free-Electron Lasers -- 10.1 EFFECT ON THE BEAM -- 10.1.1 Longitudinally -- 10.1.2 Vertically -- 10.1.3 Horizontally -- 10.1.4 Quantum lifetime -- 10.2 CHARACTERISTICS OF THE EMITTED RADIATION -- 10.2.1 Dipole magnets -- 10.2.2 Undulators and wigglers -- 10.3 SMALL-GAIN FREE-ELECTRON LASER -- 10.3.1 Amplifier and oscillator -- 10.4 SELF-AMPLIFIED SPONTANEOUS EMISSION -- 10.5 ACCELERATOR CHALLENGES -- CHAPTER 11: Non-linear Dynamics -- 11.1 A ONE-DIMENSIONAL TOY MODEL -- 11.2 TRACKING AND DYNAMIC APERTURE -- 11.3 HAMILTONIANS AND LIE-MAPS -- 11.3.1 Moving Hamiltonians -- 11.3.2 Concatenating Hamiltonians -- 11.4 IMPLEMENTATION IN MATLAB -- 11.5 TWO-DIMENSIONAL MODEL -- 11.6 KNOBS AND RESONANCE-DRIVING TERMS -- 11.7 NON-RESONANT NORMAL FORMS -- CHAPTER 12: Collective Effects -- 12.1 SPACE CHARGE -- 12.2 INTRABEAM SCATTERING AND TOUSCHEK-EFFECT -- 12.3 WAKE FIELDS, IMPEDANCES, AND LOSS FACTORS -- 12.4 COASTING-BEAM INSTABILITY -- 12.5 SINGLE-BUNCH INSTABILITIES -- 12.6 MULTI-BUNCH INSTABILITIES -- CHAPTER 13: Accelerator Subsystems -- 13.1 CONTROL SYSTEM -- 13.1.1 Sensors, actuators, and interfaces -- 13.1.2 System architecture. 13.1.3 Timing system -- 13.1.4 An example: EPICS -- 13.2 PARTICLE SOURCES -- 13.2.1 Electrons -- 13.2.2 Protons and other ions -- 13.2.3 Highly charged ions -- 13.2.4 Negatively charged ions -- 13.2.5 Radio-frequency quadrupole -- 13.3 INJECTION AND EXTRACTION -- 13.4 BEAM COOLING -- 13.5 VACUUM -- 13.5.1 Vacuum basics -- 13.5.2 Pumps and gauges -- 13.5.3 Vacuum calculations -- 13.6 CRYOGENICS -- 13.7 RADIATION PROTECTION AND SAFETY -- 13.7.1 Units -- 13.7.2 Range of radiation in matter -- 13.7.3 Dose measurements -- 13.7.4 Personnel and machine protection -- 13.8 CONVENTIONAL FACILITIES -- 13.8.1 Electricity -- 13.8.2 Water and cooling -- 13.8.3 Buildings and shielding -- CHAPTER 14: Examples of Accelerators -- 14.1 CERN AND THE LARGE HADRON COLLIDER -- 14.2 EUROPEAN SPALLATION SOURCE -- 14.3 SLAC AND THE LINAC COHERENT LIGHT SOURCE -- 14.4 MAX-IV -- 14.5 TANDEM ACCELERATOR IN UPPSALA -- 14.6 ACCELERATORS FOR MEDICAL APPLICATIONS -- 14.7 INDUSTRIAL ACCELERATORS -- APPENDIX A: The Student Labs -- A.1 BEAM PROFILE OF LASER POINTER -- A.2 EMITTANCE MEASUREMENT WITH A LASER POINTER -- A.3 HALBACH MULTIPOLES AND UNDULATORS -- A.4 MAGNET MEASUREMENTS -- A.5 COOKIE-JAR CAVITY ON A NETWORK ANALYZER -- APPENDIX B: Appendices Available Online -- B.1 LINEAR ALGEBRA -- B.2 MATLAB PRIMER -- B.3 OPENSCAD PRIMER -- B.4 LIGHT OPTICS, RAYS, AND GAUSSIAN -- B.5 MATLAB FUNCTIONS -- Bibliography -- Index. |
| Record Nr. | UNINA-9910978271603321 |
|
Ziemann Volker
|
||
| Milton : , : Taylor & Francis Group, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Introduction to topological quantum matter and quantum computation / / Tudor D. Stanescu
| Introduction to topological quantum matter and quantum computation / / Tudor D. Stanescu |
| Autore | Stanescu Tudor D. <1967-> |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boca Raton, FL : , : CRC Press, Taylor & Francis Group, , [2017] |
| Descrizione fisica | 1 online resource (395 pages) : illustrations |
| Disciplina | 530.1201/514 |
| Soggetto topico |
Quantum theory - Data processing
Topology Quantum computing |
| ISBN |
1-351-72228-X
1-315-18150-9 1-4822-4594-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Quantum theory: some fundamentals -- The geometric phase -- Quantum mechanics and information science -- Symmetry and topology in condensed matter physics -- Topological insulators and superconductors -- Interacting topological phases -- Theories of topological quantum matter -- Majorana zero modes in solid state heterostructures -- Topological phases in cold atom systems -- Topological quantum computation -- Elements of quantum information theory -- Introduction to quantum computation -- Anyons and topological quantum computation. |
| Altri titoli varianti | Introduction to topological quantum matter and quantum computation |
| Record Nr. | UNINA-9910155239103321 |
|
Stanescu Tudor D. <1967->
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| Boca Raton, FL : , : CRC Press, Taylor & Francis Group, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantum machine learning : what quantum computing means to data mining / / Peter Wittek
| Quantum machine learning : what quantum computing means to data mining / / Peter Wittek |
| Autore | Wittek Peter |
| Edizione | [First edition] |
| Pubbl/distr/stampa | San Diego, California : , : Academic Press, , 2014 |
| Descrizione fisica | 1 online resource (176 p.) |
| Disciplina | 621.3822 |
| Collana | Elsevier Insights |
| Soggetto topico |
Machine learning - Mathematical models
Data mining - Data processing Quantum theory - Data processing |
| Soggetto genere / forma | Electronic books |
| ISBN | 0-12-801099-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front 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.Ensembles
2.7.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 Algorithms Chapter 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 Performance 7.7.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 Embedding 10.5.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 Duality 13.2.Quantum Process Tomography |
| Record Nr. | UNISA-996426328203316 |
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Wittek Peter
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| San Diego, California : , : Academic Press, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Quantum mechanics : an introduction to the physical background and mathematical structure / / Gregory L. Naber
| Quantum mechanics : an introduction to the physical background and mathematical structure / / Gregory L. Naber |
| Autore | Naber Gregory L. <1948-> |
| Pubbl/distr/stampa | Berlin : , : Walter de Gruyter GmbH, , [2021] |
| Descrizione fisica | 1 online resource (570 pages) |
| Disciplina | 530.12 |
| Collana | De Gruyter Textbook |
| Soggetto topico |
Quantum theory - Mathematics
Quantum theory - Data processing |
| Soggetto non controllato |
Feynman integral
Harmonic oscillator, Heisenberg algebra Quantum mechanics Supersymmetry |
| ISBN | 3-11-075194-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Preface -- Contents -- 1 The classical harmonic oscillator -- 2 Lagrangian and Hamiltonian mechanics -- 3 The formalism of quantum mechanics: motivation -- 4 Physical background -- 5 Synopsis of self-adjoint operators, spectral theory and Stone’s theorem -- 6 The postulates of quantum mechanics -- 7 Canonical quantization -- 8 Path integral quantization -- 9 Sketches of some rigorous results -- 10 Fermionic and supersymmetric harmonic oscillators -- A Gaussian integrals -- B Morse lemma -- C Stationary phase approximation -- D Tangent and cotangent bundles -- E Poisson and wave equations -- F Carathéodory procedure -- G Schwartz space, Fourier transform, distributions and Sobolev spaces -- H Stieltjes integrals -- I Unitary representations and Schur’s lemma -- J Semigroups of operators -- K Hilbert space tensor products -- Bibliography -- Index |
| Record Nr. | UNINA-9910554230103321 |
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Naber Gregory L. <1948->
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| Berlin : , : Walter de Gruyter GmbH, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Quantum-Inspired Approaches for Intelligent Data Processing
| Quantum-Inspired Approaches for Intelligent Data Processing |
| Autore | Jadhav Dipti |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2026 |
| Descrizione fisica | 1 online resource (313 pages) |
| Disciplina | 004.0151 |
| Soggetto topico |
Quantum theory - Data processing
Quantum computing Soft computing |
| ISBN |
1-394-33644-6
1-394-33643-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Soft Computing for Intelligent Data Processing -- 1.1 Introduction -- 1.1.1 Limitations of Traditional Computing -- 1.1.2 The Philosophy of Soft Computing -- 1.1.3 Core Components of Soft Computing -- 1.1.4 Data Processing and Its Importance -- 1.1.5 Advantages of Soft Computing for Intelligent Data Processing -- 1.2 Literature Review -- 1.3 Proposed Methodology -- 1.3.1 Fuzzy-Neural Hybrid Systems -- 1.3.2 Evolutionary Fuzzy Systems -- 1.3.3 Neuro-Evolutionary Learning -- 1.3.4 Deep Learning with Soft Computing Integration -- 1.4 Results and Discussions -- 1.5 Conclusion -- References -- Chapter 2 Foundations of Quantum Computing: Overview, Foundation and Scope -- 2.1 Overview of Quantum Computing -- 2.1.1 Classical vs. Quantum Systems in Computing Techniques for Data Processing -- 2.1.2 Superposition and Entanglement in Quantum Computing for Enhanced Performance -- 2.1.2.1 Qubits and Quantum States -- 2.1.2.2 Superposition and Entanglement -- 2.1.2.3 Quantum Gates and Circuits -- 2.1.3 The Probabilistic Nature of Quantum Computing -- 2.1.4 Quantum Measurement and Observables in Computing Environment -- 2.2 Quantum Algorithms: Unleashing Quantum Power for Data Processing -- 2.2.1 Implementation of Shor's Algorithm for Integer Factorization -- 2.2.2 Implementation of Grover's Algorithm for Unstructured Search -- 2.2.3 Quantum Approximation and Optimization Algorithms in the Present Scenario -- 2.3 Advantages and Challenges of Quantum Computing -- 2.3.1 Quantum Supremacy in Computing Technology -- 2.3.2 Challenges and Limitations in Quantum Computing -- 2.3.3 Quantum Error Correction Techniques -- 2.3.3.1 Errors in Quantum Systems-Sources of Errors -- 2.3.3.2 Quantum Error Correction Code (QECC) -- 2.3.3.3 Surface Code.
2.3.3.4 Threshold Theorem -- 2.4 Quantum Computing Technologies: Building the Quantum Toolbox -- 2.4.1 The Significance of Superconducting Qubits in Quantum Computing -- 2.4.2 Physical Implementation of Trapped Ions and Quantum Dots in Quantum Computing -- 2.4.3 Topological Quantum Computing Strategy for Effective Solutions -- 2.4.3.1 Braiding of Anyons and Fault Tolerance -- 2.4.3.2 Topological Quantum Gates -- 2.5 Scope of Quantum Computing: Security, Optimization, and Machine Learning -- 2.5.1 Key Distribution and Secure Communication in Quantum Cryptography -- 2.5.2 Securing IoT Devices Using Encryption and Blockchain -- 2.5.3 Solving Combinatorial Optimization with Quantum Speedup -- 2.5.3.1 Quantum Approximate Optimization Algorithm (QAOA) for Combinatorial Problems -- 2.5.3.2 Quantum Annealing for Optimization -- 2.5.4 Quantum-Enhanced Machine Learning: Optimizing Energy Consumption with Quantum Algorithms -- 2.5.4.1 Key Concepts and Benefits in QML -- 2.5.4.2 Quantum Support Vector Machines (QSVM) -- 2.5.4.3 Quantum Neural Networks (QNN) -- 2.5.4.4 Quantum Reinforcement Learning (QRL) -- 2.6 The Future of Quantum Computing -- 2.6.1 Quantum Computing and Industry Applications -- 2.6.2 Quantum Cloud Computing -- 2.6.3 Quantum Computing's Role in National Security -- 2.6.4 Looking Ahead: Challenges and Opportunities -- Bibliography -- Chapter 3 Integration of Quantum Computing with Soft Computing for Data Processing -- 3.1 Introduction to Quantum Computing and Soft Computing -- 3.1.1 Comparative Analysis -- 3.2 Interrelation Between Quantum Computing and Soft Computing -- 3.2.1 Quantum Computing Advantage of Speed and Scalability Vs Soft Computing Advantages of 'Soft' and Approximations -- 3.3 Mathematical Analysis of the Interrelation between Quantum Computing and Soft Computing -- 3.3.1 Representing Quantum States and Qubits. 3.3.2 Quantum-Soft Computing Hybrid Model -- 3.3.3 Quantum Probability and Fuzzy Membership Interrelation -- 3.3.4 Quantum-Soft Superposition for Approximation -- 3.3.5 Optimization Using Quantum-Soft Algorithms -- 3.3.6 Hybrid Error Minimization -- 3.4 Quantum-Inspired Algorithms for Enhanced Data Processing -- 3.4.1 Quantum Genetic Algorithms (QGAs) -- 3.4.2 Quantum Neural Networks (QNNs) -- 3.4.3 Quantum Particle Swarm Optimization (QPSO) and Its Role in Large-Scale Optimization -- 3.4.4 Quantum Particle Swarm Optimization (QPSO) -- 3.4.5 Advantages of Quantum-Inspired Algorithms in Data Processing and Optimization -- 3.4.6 Quantum Computing in Big Data Analytics -- 3.4.7 Parallel Data Processing in Modern Quantum Computing -- 3.5 Trade-Offs Between Computational Error and Processing Speed -- 3.6 Data Mining, Control Systems, and Pattern Recognition -- 3.6.1 Data Mining -- 3.6.2 Control Systems -- 3.6.3 Pattern Recognition -- 3.7 Challenges and Limitations of Classical Soft Computing in Large Datasets -- 3.7.1 Challenges Related to Size in Soft Computing Techniques -- 3.8 Quantum Computing Platforms for Soft Computing Integration -- 3.8.1 Overview of Quantum Development Platforms -- 3.9 Case Studies of Quantum and Soft Computing Integration in Industry -- 3.9.1 Security and Privacy in Quantum-Enhanced Soft Computing -- 3.10 Introduction to Quantum Cryptography and Data Privacy -- 3.11 Quantum Algorithms for Privacy Preservation in Computation and Communication -- 3.12 Future Prospects and Emerging Research Gaps -- 3.12.1 Demand for Physical Quantum Algorithms and Well-Defined Theoretical Models -- 3.13 Security and Privacy Challenges in Quantum-Enhanced Soft Computing -- 3.14 Potential for Quantum-Inspired Tools in Artificial Intelligence and Big Data Analytics -- 3.15 Impact of Quantum and Soft Computing Integration on Data Processing. 3.15.1 Benefits and Potential of Quantum-Soft Computing Synergy -- 3.16 Outlook on Future Applications in AI, Optimization, and Big Data -- References -- Chapter 4 Quantum-Soft Fusion: Transforming the Future of Data Handling -- 4.1 Introduction -- 4.2 Literature Work -- 4.3 Proposed Work -- 4.4 Results -- 4.5 Conclusion and Future Scope -- References -- Chapter 5 Quantum-Inspired Soft Computing for Intelligent IoT Big Data Processing -- 5.1 Introduction to Quantum-Inspired Soft Computing and IoT Big Data -- 5.2 Quantum-Inspired Genetic Algorithms (QIGAs) -- 5.2.1 Mathematical Model for Quantum Principles -- 5.2.1.1 Quantum-Inspired Selection -- 5.2.1.2 Quantum-Inspired Crossover -- 5.2.1.3 Quantum-Inspired Mutation -- 5.2.1.4 Fitness Evaluation -- 5.3 Quantum-Inspired Particle Swarm Optimization (QIPSO) Algorithm -- 5.4 Quantum Annealing Algorithm -- 5.5 Quantum-Inspired Artificial Neural Networks (QIA-NN) -- 5.5.1 Mathematical Model of Quantum Inspired Artificial Neural Networks -- 5.6 Performance Evaluation of Quantum Inspired Soft Computing Techniques -- 5.7 Role of QI Soft Computing Techniques for IoT Big Data Processing -- 5.7.1 Benefits of Quantum-Inspired Soft Computing for Big Data -- References -- Chapter 6 Quantum-Inspired Optimization Techniques for IoT-Driven Big Data Analysis -- 6.1 Overview of Internet of Things (IoT) and Big Data -- 6.2 Challenges in Handling Big Data in IoT -- 6.3 The Role of Optimization in IoT Data Analysis -- 6.4 Quantum-Inspired Optimization Techniques -- 6.4.1 Key Principles of Quantum Mechanics in QIO -- 6.4.2 Popular Quantum-Inspired Algorithms -- 6.5 Quantum-Inspired Optimization Algorithms for IoT -- 6.5.1 Basics of Quantum-Inspired Algorithms -- 6.5.2 Quantum Particle Swarm Optimization (QPSO) -- 6.5.3 Quantum-Inspired Evolutionary Algorithm (QIEA) -- 6.5.4 Quantum Annealing Inspired Optimization (QAIO). 6.6 Performance Evaluation of Quantum-Inspired Optimization Techniques -- 6.7 Quantum-Inspired Optimization Techniques for Big Data Analysis -- 6.7.1 Applications of Quantum-Inspired Optimization Technique in Big-Data Analytics -- 6.8 Summary -- Bibliography -- Chapter 7 Quantum-Inspired Soft Computing for Intelligent Data Processing in Real-Life Scenarios -- 7.1 Introduction -- 7.2 Fundamentals of Quantum-Inspired Soft Computing -- 7.3 Key Concepts: Superposition, Entanglement, and Interference -- 7.4 Soft Computing Techniques: Fuzzy Logic, Genetic Algorithms, and Neural Networks -- 7.5 Quantum-Inspired Algorithms for Intelligent Data Processing -- 7.6 Quantum-Inspired Neural Networks -- 7.7 Hybrid Quantum Approaches in Soft Computing -- 7.8 Applications of Quantum-Inspired Soft Computing in Real-Life Scenarios -- 7.8.1 Healthcare Data Processing -- 7.8.2 Financial Data Analytics -- 7.8.3 Traffic Management and Smart Cities -- 7.9 IoT and Edge Computing in Industry 4.0 -- 7.10 Energy Management in Smart Grids -- 7.11 Fraud Detection in E-Commerce -- 7.12 Challenges and Limitations of Quantum-Inspired Soft Computing -- 7.12.1 Computational Complexity and Scalability -- 7.12.2 Data Noise and Uncertainty -- 7.12.3 Hardware and Algorithmic Limitations -- 7.13 Ethical and Social Implications in Data Handling -- 7.13.1 Impact on Data Privacy and Security -- 7.13.2 Ethical Use of AI and Quantum Technologies in Decision-Making -- 7.13.3 Addressing Bias and Fairness -- 7.14 Future Trends in Quantum-Inspired Soft Computing -- 7.15 Case Studies and Practical Implementations -- 7.16 Conclusion -- References -- Chapter 8 Market Trends in Quantum-Inspired Soft Computing for Intelligent Data Processing -- 8.1 Introduction -- 8.2 Understanding Quantum-Inspired Soft Computing regarding Quantum-Inspired Soft Computing -- 8.2.1 Overview and Essential Ideas. 8.2.2 Fundamental Elements. |
| Record Nr. | UNINA-9911054512803321 |
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Jadhav Dipti
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| Newark : , : John Wiley & Sons, Incorporated, , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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