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Books on the topic 'Quantum trajectorie'

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Published: 9 March 2023
Last updated: 10 March 2023

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1

Quantum trajectories. Boca Raton: CRC Press, 2010.

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2

Barchielli, Alberto, and Matteo Gregoratti. Quantum Trajectories and Measurements in Continuous Time. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01298-3.

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3

Quantum optics: Including noise reduction, trapped ions, quantum trajectories, and decoherence. 2nd ed. Berlin: Springer, 2008.

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4

Orszag, Miguel. Quantum optics: Including noise reduction, trapped ions, quantum trajectories, and decoherence. Berlin: Springer, 2000.

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5

Orszag, Miguel. Quantum Optics: Including Noise Reduction, Trapped Ions, Quantum Trajectories, and Decoherence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000.

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6

(Matteo), Gregoratti M., and SpringerLink (Online service), eds. Quantum trajectories and measurements in continuous time: The diffusive case. Berlin: Springer, 2009.

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7

Sanz, Ángel S., and Salvador Miret-Artés. A Trajectory Description of Quantum Processes. II. Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-17974-7.

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8

Sanz, Ángel S., and Salvador Miret-Artés. A Trajectory Description of Quantum Processes. I. Fundamentals. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-18092-7.

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9

Ceresole, A. Tullio Regge: An eclectic genius : from quantum gravity to computer play. Edited by Frè P. editor. Singapore: World Scientific Publishing Co. Pte. Ltd., 2020.

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10

A, Ranfagni, ed. Trajectories and rays: The path-summation in quantum mechanics and optics. Singapore: World Scientific, 1990.

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11

Salvador, Miret-Artés, and SpringerLink (Online service), eds. A Trajectory Description of Quantum Processes. I. Fundamentals: A Bohmian Perspective. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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12

Deterministic explanation of quantum mechanics: Based on a new trajectory-wave ordering interaction. St. Cloud, Minn: North Star Press of St. Cloud, 1994.

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13

1942-, Graaf J. de, ed. Trajectory spaces, generalized functions, and unbounded operators. Berlin: Springer-Verlag, 1985.

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14

service), SpringerLink (Online, ed. Semiclassical Approach to Mesoscopic Systems: Classical Trajectory Correlations and Wave Interference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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15

Chattaraj, Pratim Kumar. Quantum Trajectories. Taylor & Francis Group, 2011.

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16

Chattaraj, Pratim Kumar. Quantum Trajectories. Taylor & Francis Group, 2016.

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17

Chattaraj, Pratim Kumar. Quantum Trajectories. Taylor & Francis Group, 2016.

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18

Chattaraj, Pratim Kumar. Quantum Trajectories. Taylor & Francis Group, 2017.

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19

Quantum Dynamics with Trajectories. New York: Springer-Verlag, 2005. http://dx.doi.org/10.1007/0-387-28145-2.

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20

Quantum Dynamics with Trajectories: Introduction to Quantum Hydrodynamics. Springer New York, 2010.

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21

Nolte, David D. On the Quantum Footpath. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805847.003.0008.

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This chapter shows how the concept of the trajectory of a quantum particle almost vanished in the battle between Werner Heisenberg’s matrix mechanics and Erwin Schrödinger’s wave mechanics. It took Niels Bohr and his complementarity principle of wave-particle duality to cede back some reality to quantum trajectories. However, Schrödinger and Einstein were not convinced and conceived of quantum entanglement to refute the growing acceptance of the Copenhagen Interpretation of quantum physics. Schrödinger’s cat was meant to be an absurdity, but ironically it has become a central paradigm of practical quantum computers. Quantum trajectories took on new meaning when Richard Feynman constructed quantum theory based on the principle of least action, inventing his famous Feynman Diagrams to help explain quantum electrodynamics.
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22

Mompart, Jordi, and Xavier Oriols Pladevall. Applied Bohmian Mechanics: From Nanoscale Systems to Cosmology. Jenny Stanford Publishing, 2019.

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23

Mompart, Jordi, and Xavier Oriols Pladevall. Applied Bohmian Mechanics: From Nanoscale Systems to Cosmology. Jenny Stanford Publishing, 2019.

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24

Orszag, Miguel. Quantum Optics: Including Noise Reduction, Trapped Ions, Quantum Trajectories, and Decoherence. Springer, 2018.

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25

Orszag, Miguel. Quantum Optics: Including Noise Reduction, Trapped Ions, Quantum Trajectories, and Decoherence. Springer, 2016.

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26

Orszag, Miguel. Quantum Optics: Including Noise Reduction, Trapped Ions, Quantum Trajectories, and Decoherence. Springer, 2010.

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27

Orszag, Miguel. Quantum Optics: Including Noise Reduction, Trapped Ions, Quantum Trajectories, and Decoherence. Springer London, Limited, 2016.

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28

Orszag, Miguel. Quantum Optics: Including Noise Reduction, Trapped Ions, Quantum Trajectories, and Decoherence. Springer London, Limited, 2007.

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29

(Contributor), Corey J. Trahan, ed. Quantum Dynamics with Trajectories: Introduction to Quantum Hydrodynamics (Interdisciplinary Applied Mathematics). Springer, 2005.

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30

Orszag, Miguel. Quantum Optics: Including Noise Reduction, Trapped Ions, Quantum Trajectories, and Decoherence. 2nd ed. Springer, 2007.

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31

Wyatt, Robert E. Quantum Dynamics with Trajectories: Introduction to Quantum Hydrodynamics (Interdisciplinary Applied Mathematics Book 28). Springer, 2006.

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32

Sanz, Ángel S., and Salvador Miret-Artés. A Trajectory Description of Quantum Processes. I. Fundamentals. Springer, 2012.

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33

Barchielli, Alberto, and Matteo Gregoratti. Quantum Trajectories and Measurements in Continuous Time: The Diffusive Case. Springer, 2011.

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34

Eijndhoven, Stephanus van, and Johannes de Graaf. Trajectory Spaces, Generalized Functions and Unbounded Operators. Springer London, Limited, 2006.

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35

Trajectory Spaces, Generalized Functions and Unbounded Operators. Springer Berlin / Heidelberg, 1985.

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36

A Trajectory Description Of Quantum Processes I Fundamentals A Bohemian Perspective. Springer, 2012.

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37

Sanz, Ángel S., and Salvador Miret-Artés. Trajectory Description of Quantum Processes. II. Applications: A Bohmian Perspective. Springer London, Limited, 2013.

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38

A Trajectory Description of Quantum Processes Lecture Notes in Physics Lecture Notes in Physics. Springer, 2012.

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39

Hase, W. Advances in Classical Trajectory Methods, Volume 3: Comparison of Classical and Quantum Dynamics (Advances in Classical Trajectory Methods). JAI Press, 1998.

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40

Mugnai, D., P. Moretti, and M. Cetica. Trajectories and Rays: The Path-Summation in Quantum Mechanics and Optics (World Scientific Lecture Notes in Physics). World Scientific Publishing Company, 1991.

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41

Semiclassical Approach To Mesoscopic Systems Classical Trajectory Correlations And Wave Interference. Springer, 2012.

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42

Waltner, Daniel. Semiclassical Approach to Mesoscopic Systems: Classical Trajectory Correlations and Wave Interference. Springer, 2014.

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43

Nolte, David D. Galileo Unbound. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805847.001.0001.

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Galileo Unbound: A Path Across Life, The Universe and Everything traces the journey that brought us from Galileo’s law of free fall to today’s geneticists measuring evolutionary drift, entangled quantum particles moving among many worlds, and our lives as trajectories traversing a health space with thousands of dimensions. Remarkably, common themes persist that predict the evolution of species as readily as the orbits of planets or the collapse of stars into black holes. This book tells the history of spaces of expanding dimension and increasing abstraction and how they continue today to give new insight into the physics of complex systems. Galileo published the first modern law of motion, the Law of Fall, that was ideal and simple, laying the foundation upon which Newton built the first theory of dynamics. Early in the twentieth century, geometry became the cause of motion rather than the result when Einstein envisioned the fabric of space-time warped by mass and energy, forcing light rays to bend past the Sun. Possibly more radical was Feynman’s dilemma of quantum particles taking all paths at once—setting the stage for the modern fields of quantum field theory and quantum computing. Yet as concepts of motion have evolved, one thing has remained constant, the need to track ever more complex changes and to capture their essence, to find patterns in the chaos as we try to predict and control our world.
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44

Henriksen, Niels Engholm, and Flemming Yssing Hansen. Bimolecular Reactions, Dynamics of Collisions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805014.003.0004.

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This chapter discusses the dynamics of bimolecular collisions within the framework of (quasi-)classical mechanics as well as quantum mechanics. The relation between the cross-section and the reaction probability, which can be calculated theoretically from a (quasi-)classical or quantum mechanical description of the collision, is described in terms of classical trajectories and wave packets, respectively. As an introduction to reactive scattering, classical two-body scattering is described and used to formulate simple models for chemical reactions, based on reasonable assumptions for the reaction probability. Three-body (and many-body) quasi-classical scattering is formulated and the numerical evaluation of the reaction probability is described. The relation between scattering angles and differential cross-sections in various frames is emphasized. The chapter concludes with a brief description of non-adiabatic dynamics, that is, situations beyond the Born–Oppenheimer approximation where more than one electronic state is in play. A discussion of the so-called Landau–Zener model is included.
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45

Nolte, David D. Geometry on my Mind. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805847.003.0005.

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This chapter reviews the history of modern geometry with a focus on the topics that provided the foundation for the new visualization of physics. It begins with Carl Gauss and Bernhard Riemann, who redefined geometry and identified the importance of curvature for physics. Vector spaces, developed by Hermann Grassmann, Giuseppe Peano and David Hilbert, are examples of the kinds of abstract new spaces that are so important for modern physics, such as Hilbert space for quantum mechanics. Fractal geometry developed by Felix Hausdorff later provided the geometric language needed to solve problems in chaos theory. Motion cannot exist without space—trajectories are the tracks of points, mathematical or physical, through it.
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46

Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.
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47

Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.
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