On the Stochastic Mechanics Foundation of Quantum Mechanics
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Macroscopic Oil Droplets Mimicking Quantum Behavior: How Far Can We Push an Analogy?
Macroscopic oil droplets mimicking quantum behavior: How far can we push an analogy? Louis Vervoort1, Yves Gingras2 1Institut National de Recherche Scientifique (INRS), and 1Minkowski Institute, Montreal, Canada 2Université du Québec à Montréal, Montreal, Canada 29.04.2015, revised 20.10.2015 Abstract. We describe here a series of experimental analogies between fluid mechanics and quantum mechanics recently discovered by a team of physicists. These analogies arise in droplet systems guided by a surface (or pilot) wave. We argue that these experimental facts put ancient theoretical work by Madelung on the analogy between fluid and quantum mechanics into new light. After re-deriving Madelung’s result starting from two basic fluid-mechanical equations (the Navier-Stokes equation and the continuity equation), we discuss the relation with the de Broglie-Bohm theory. This allows to make a direct link with the droplet experiments. It is argued that the fluid-mechanical interpretation of quantum mechanics, if it can be extended to the general N-particle case, would have an advantage over the Bohm interpretation: it could rid Bohm’s theory of its strongly non-local character. 1. Introduction Historically analogies have played an important role in understanding or deriving new scientific results. They are generally employed to make a new phenomenon easier to understand by comparing it to a better known one. Since the beginning of modern science different kinds of analogies have been used in physics as well as in natural history and biology (for a recent historical study, see Gingras and Guay 2011). With the growing mathematization of physics, mathematical (or formal) analogies have become more frequent as a tool for understanding new phenomena; but also for proposing new interpretations and theories for such new discoveries. -
Foundations of Quantum Mechanics (ACM30210) Set 3
Foundations of Quantum Mechanics (ACM30210) Set 3 Dr Lennon O´ N´araigh 20th February 2020 Instructions: This is a graded assignment. • Put your name and student number on your homework. • The assignment is to be handed in on Monday March 9th before 13:00. • Hand in your assignment by putting it into the homework box marked ‘ACM 30210’ • outside the school office (G03, Science North). Model answers will be made available online in due course. • For environmental reasons, please do not place your homework in a ‘polly pocket’ • – use a staple instead. Preliminaries In these exercises, let ψ(x, t) be the wavefunction of for a single-particle system satisfying Schr¨odinger’s equation, ∂ψ ~2 i~ = 2 + U(x) ψ, (1) ∂t −2m∇ where all the symbols have their usual meaning. 1 Quantum Mechanics Madelung Transformations 1. Write ψ in ‘polar-coordinate form’, ψ(x, t) = R(x, t)eiθ(x,t), where R and θ are both functions of space and time. Show that: ∂R ~ + R2 θ =0. (2) ∂t 2mR∇· ∇ Hint: Use the ‘conservation law of probability’ in Chapter 9 of the lecture notes. 2. Define 2 R = ρm/m mR = ρm, (3a) ⇐⇒ and: p 1 θ = S/~ S = θ~, u = S. (3b) ⇐⇒ m∇ Show that Equation (4) can be re-written as: ∂ρm + (uρm)=0. (4) ∂t ∇· 3. Again using the representation ψ = Reiθ, show that S = θ~ satisfies the Quantum Hamilton–Jacobi equation: ∂S 1 ~2 2R + S 2 + U = ∇ . (5) ∂t 2m|∇ | 2m R 4. Starting with Equation (5), deduce: ∂u 1 + u u = UQ, (6) ∂t ·∇ −m∇ where UQ is the Quantum Potential, 2 2 ~ √ρm UQ = U ∇ . -
Galaxy Formation with Ultralight Bosonic Dark Matter
GALAXY FORMATION WITH ULTRALIGHT BOSONIC DARK MATTER Dissertation zur Erlangung des mathematisch-naturwissenschaftlichen Doktorgrades "Doctor rerum naturalium" der Georg-August-Universit¨atG¨ottingen im Promotionsstudiengang Physik der Georg-August University School of Science (GAUSS) vorgelegt von Jan Veltmaat aus Bietigheim-Bissingen G¨ottingen,2019 Betreuungsausschuss Prof. Dr. Jens Niemeyer, Institut f¨urAstrophysik, Universit¨atG¨ottingen Prof. Dr. Steffen Schumann, Institut f¨urTheoretische Physik, Universit¨atG¨ottingen Mitglieder der Pr¨ufungskommission Referent: Prof. Dr. Jens Niemeyer, Institut f¨urAstrophysik, Universit¨atG¨ottingen Korreferent: Prof. Dr. David Marsh, Institut f¨urAstrophysik, Universit¨atG¨ottingen Weitere Mitglieder der Pr¨ufungskommission: Prof. Dr. Fabian Heidrich-Meisner, Institut f¨urTheoretische Physik, Universit¨atG¨ottingen Prof. Dr. Wolfram Kollatschny, Institut f¨urAstrophysik, Universit¨atG¨ottingen Prof. Dr. Steffen Schumann, Institut f¨urTheoretische Physik, Universit¨atG¨ottingen Dr. Michael Wilczek, Max-Planck-Institut f¨urDynamik und Selbstorganisation, G¨ottingen Tag der m¨undlichen Pr¨ufung:12.12.2019 Abstract Ultralight bosonic particles forming a coherent state are dark matter candidates with distinctive wave-like behaviour on the scale of dwarf galaxies and below. In this thesis, a new simulation technique for ul- tralight bosonic dark matter, also called fuzzy dark matter, is devel- oped and applied in zoom-in simulations of dwarf galaxy halos. When gas and star formation are not included in the simulations, it is found that halos contain solitonic cores in their centers reproducing previous results in the literature. The cores exhibit strong quasi-normal oscil- lations, which are possibly testable by observations. The results are inconclusive regarding the long-term evolution of the core mass. -
A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation
fractal and fractional Article A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation Marylu L. Lagunes, Oscar Castillo * , Fevrier Valdez , Jose Soria and Patricia Melin Tijuana Institute of Technology, Calzada Tecnologico s/n, Fracc. Tomas Aquino, Tijuana 22379, Mexico; [email protected] (M.L.L.); [email protected] (F.V.); [email protected] (J.S.); [email protected] (P.M.) * Correspondence: [email protected] Abstract: Stochastic fractal search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic stochastic diffusion property, an improved dynamic stochastic fractal search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid, and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion. Keywords: fractal search; fuzzy logic; parameter adaptation; CEC 2017 Citation: Lagunes, M.L.; Castillo, O.; Valdez, F.; Soria, J.; Melin, P. A New Approach for Dynamic Stochastic 1. Introduction Fractal Search with Fuzzy Logic for Metaheuristic algorithms are applied to optimization problems due to their charac- Parameter Adaptation. Fractal Fract. teristics that help in searching for the global optimum, while simple heuristics are mostly 2021, 5, 33. -
Introduction to Stochastic Processes - Lecture Notes (With 33 Illustrations)
Introduction to Stochastic Processes - Lecture Notes (with 33 illustrations) Gordan Žitković Department of Mathematics The University of Texas at Austin Contents 1 Probability review 4 1.1 Random variables . 4 1.2 Countable sets . 5 1.3 Discrete random variables . 5 1.4 Expectation . 7 1.5 Events and probability . 8 1.6 Dependence and independence . 9 1.7 Conditional probability . 10 1.8 Examples . 12 2 Mathematica in 15 min 15 2.1 Basic Syntax . 15 2.2 Numerical Approximation . 16 2.3 Expression Manipulation . 16 2.4 Lists and Functions . 17 2.5 Linear Algebra . 19 2.6 Predefined Constants . 20 2.7 Calculus . 20 2.8 Solving Equations . 22 2.9 Graphics . 22 2.10 Probability Distributions and Simulation . 23 2.11 Help Commands . 24 2.12 Common Mistakes . 25 3 Stochastic Processes 26 3.1 The canonical probability space . 27 3.2 Constructing the Random Walk . 28 3.3 Simulation . 29 3.3.1 Random number generation . 29 3.3.2 Simulation of Random Variables . 30 3.4 Monte Carlo Integration . 33 4 The Simple Random Walk 35 4.1 Construction . 35 4.2 The maximum . 36 1 CONTENTS 5 Generating functions 40 5.1 Definition and first properties . 40 5.2 Convolution and moments . 42 5.3 Random sums and Wald’s identity . 44 6 Random walks - advanced methods 48 6.1 Stopping times . 48 6.2 Wald’s identity II . 50 6.3 The distribution of the first hitting time T1 .......................... 52 6.3.1 A recursive formula . 52 6.3.2 Generating-function approach . -
1 Stochastic Processes and Their Classification
1 1 STOCHASTIC PROCESSES AND THEIR CLASSIFICATION 1.1 DEFINITION AND EXAMPLES Definition 1. Stochastic process or random process is a collection of random variables ordered by an index set. ☛ Example 1. Random variables X0;X1;X2;::: form a stochastic process ordered by the discrete index set f0; 1; 2;::: g: Notation: fXn : n = 0; 1; 2;::: g: ☛ Example 2. Stochastic process fYt : t ¸ 0g: with continuous index set ft : t ¸ 0g: The indices n and t are often referred to as "time", so that Xn is a descrete-time process and Yt is a continuous-time process. Convention: the index set of a stochastic process is always infinite. The range (possible values) of the random variables in a stochastic process is called the state space of the process. We consider both discrete-state and continuous-state processes. Further examples: ☛ Example 3. fXn : n = 0; 1; 2;::: g; where the state space of Xn is f0; 1; 2; 3; 4g representing which of four types of transactions a person submits to an on-line data- base service, and time n corresponds to the number of transactions submitted. ☛ Example 4. fXn : n = 0; 1; 2;::: g; where the state space of Xn is f1; 2g re- presenting whether an electronic component is acceptable or defective, and time n corresponds to the number of components produced. ☛ Example 5. fYt : t ¸ 0g; where the state space of Yt is f0; 1; 2;::: g representing the number of accidents that have occurred at an intersection, and time t corresponds to weeks. ☛ Example 6. fYt : t ¸ 0g; where the state space of Yt is f0; 1; 2; : : : ; sg representing the number of copies of a software product in inventory, and time t corresponds to days. -
The Statistical Interpretation of Entangled States B
The Statistical Interpretation of Entangled States B. C. Sanctuary Department of Chemistry, McGill University 801 Sherbrooke Street W Montreal, PQ, H3A 2K6, Canada Abstract Entangled EPR spin pairs can be treated using the statistical ensemble interpretation of quantum mechanics. As such the singlet state results from an ensemble of spin pairs each with an arbitrary axis of quantization. This axis acts as a quantum mechanical hidden variable. If the spins lose coherence they disentangle into a mixed state. Whether or not the EPR spin pairs retain entanglement or disentangle, however, the statistical ensemble interpretation resolves the EPR paradox and gives a mechanism for quantum “teleportation” without the need for instantaneous action-at-a-distance. Keywords: Statistical ensemble, entanglement, disentanglement, quantum correlations, EPR paradox, Bell’s inequalities, quantum non-locality and locality, coincidence detection 1. Introduction The fundamental questions of quantum mechanics (QM) are rooted in the philosophical interpretation of the wave function1. At the time these were first debated, covering the fifty or so years following the formulation of QM, the arguments were based primarily on gedanken experiments2. Today the situation has changed with numerous experiments now possible that can guide us in our search for the true nature of the microscopic world, and how The Infamous Boundary3 to the macroscopic world is breached. The current view is based upon pivotal experiments, performed by Aspect4 showing that quantum mechanics is correct and Bell’s inequalities5 are violated. From this the non-local nature of QM became firmly entrenched in physics leading to other experiments, notably those demonstrating that non-locally is fundamental to quantum “teleportation”. -
Analysis of Nonlinear Dynamics in a Classical Transmon Circuit
Analysis of Nonlinear Dynamics in a Classical Transmon Circuit Sasu Tuohino B. Sc. Thesis Department of Physical Sciences Theoretical Physics University of Oulu 2017 Contents 1 Introduction2 2 Classical network theory4 2.1 From electromagnetic fields to circuit elements.........4 2.2 Generalized flux and charge....................6 2.3 Node variables as degrees of freedom...............7 3 Hamiltonians for electric circuits8 3.1 LC Circuit and DC voltage source................8 3.2 Cooper-Pair Box.......................... 10 3.2.1 Josephson junction.................... 10 3.2.2 Dynamics of the Cooper-pair box............. 11 3.3 Transmon qubit.......................... 12 3.3.1 Cavity resonator...................... 12 3.3.2 Shunt capacitance CB .................. 12 3.3.3 Transmon Lagrangian................... 13 3.3.4 Matrix notation in the Legendre transformation..... 14 3.3.5 Hamiltonian of transmon................. 15 4 Classical dynamics of transmon qubit 16 4.1 Equations of motion for transmon................ 16 4.1.1 Relations with voltages.................. 17 4.1.2 Shunt resistances..................... 17 4.1.3 Linearized Josephson inductance............. 18 4.1.4 Relation with currents................... 18 4.2 Control and read-out signals................... 18 4.2.1 Transmission line model.................. 18 4.2.2 Equations of motion for coupled transmission line.... 20 4.3 Quantum notation......................... 22 5 Numerical solutions for equations of motion 23 5.1 Design parameters of the transmon................ 23 5.2 Resonance shift at nonlinear regime............... 24 6 Conclusions 27 1 Abstract The focus of this thesis is on classical dynamics of a transmon qubit. First we introduce the basic concepts of the classical circuit analysis and use this knowledge to derive the Lagrangians and Hamiltonians of an LC circuit, a Cooper-pair box, and ultimately we derive Hamiltonian for a transmon qubit. -
Ehrenfest Theorem
Ehrenfest theorem Consider two cases, A=r and A=p: d 1 1 p2 1 < r > = < [r,H] > = < [r, + V(r)] > = < [r,p2 ] > dt ih ih 2m 2imh 2 [r,p ] = [r.p]p + p[r,p] = 2ihp d 1 d < p > ∴ < r > = < 2ihp > ⇒ < r > = dt 2imh dt m d 1 1 p2 1 < p > = < [p,H] > = < [p, + V(r)] > = < [p,V(r)] > dt ih ih 2m ih [p,V(r)] ψ = − ih∇V(r)ψ − ()− ihV(r)∇ ψ = −ihψ∇V(r) ⇒ [p,V(r)] = −ih∇V(r) d 1 d ∴ < p > = < −ih∇V(r) > ⇒ < p > = < −∇V(r) > dt ih dt d d Compare this with classical result: m vr = pv and pv = − ∇V dt dt We see the correspondence between expectation of an operator and classical variable. Poisson brackets and commutators Poisson brackets in classical mechanics: ⎛ ∂A ∂B ∂A ∂B ⎞ {A, B} = ⎜ − ⎟ ∑ ⎜ ⎟ j ⎝ ∂q j ∂p j ∂p j ∂q j ⎠ dA ⎛ ∂A ∂A ⎞ ∂A ⎛ ∂A ∂H ∂A ∂H ⎞ ∂A = ⎜ q + p ⎟ + = ⎜ − ⎟ + ∑ ⎜ & j & j ⎟ ∑ ⎜ ⎟ dt j ⎝ ∂q j ∂p j ⎠ ∂t j ⎝ ∂q j ∂q j ∂p j ∂p j ⎠ ∂t dA ∂A ∴ = {A,H}+ Classical equation of motion dt ∂ t Compare this with quantum mechanical result: d 1 ∂A < A > = < [A,H] > + dt ih ∂ t We see the correspondence between classical mechanics and quantum mechanics: 1 [A,H] → {A, H} classical ih Quantum mechanics and classical mechanics 1. Quantum mechanics is more general and cover classical mechanics as a limiting case. 2. Quantum mechanics can be approximated as classical mechanics when the action is much larger than h. -
Monte Carlo Sampling Methods
[1] Monte Carlo Sampling Methods Jasmina L. Vujic Nuclear Engineering Department University of California, Berkeley Email: [email protected] phone: (510) 643-8085 fax: (510) 643-9685 UCBNE, J. Vujic [2] Monte Carlo Monte Carlo is a computational technique based on constructing a random process for a problem and carrying out a NUMERICAL EXPERIMENT by N-fold sampling from a random sequence of numbers with a PRESCRIBED probability distribution. x - random variable N 1 xˆ = ---- x N∑ i i = 1 Xˆ - the estimated or sample mean of x x - the expectation or true mean value of x If a problem can be given a PROBABILISTIC interpretation, then it can be modeled using RANDOM NUMBERS. UCBNE, J. Vujic [3] Monte Carlo • Monte Carlo techniques came from the complicated diffusion problems that were encountered in the early work on atomic energy. • 1772 Compte de Bufon - earliest documented use of random sampling to solve a mathematical problem. • 1786 Laplace suggested that π could be evaluated by random sampling. • Lord Kelvin used random sampling to aid in evaluating time integrals associated with the kinetic theory of gases. • Enrico Fermi was among the first to apply random sampling methods to study neutron moderation in Rome. • 1947 Fermi, John von Neuman, Stan Frankel, Nicholas Metropolis, Stan Ulam and others developed computer-oriented Monte Carlo methods at Los Alamos to trace neutrons through fissionable materials UCBNE, J. Vujic Monte Carlo [4] Monte Carlo methods can be used to solve: a) The problems that are stochastic (probabilistic) by nature: - particle transport, - telephone and other communication systems, - population studies based on the statistics of survival and reproduction. -
SIMULATED INTERPRETATION of QUANTUM MECHANICS Miroslav Súkeník & Jozef Šima
SIMULATED INTERPRETATION OF QUANTUM MECHANICS Miroslav Súkeník & Jozef Šima Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovakia Abstract: The paper deals with simulated interpretation of quantum mechanics. This interpretation is based on possibilities of computer simulation of our Universe. 1: INTRODUCTION Quantum theory and theory of relativity are two fundamental theories elaborated in the 20th century. In spite of the stunning precision of many predictions of quantum mechanics, its interpretation remains still unclear. This ambiguity has not only serious physical but mainly philosophical consequences. The commonest interpretations include the Copenhagen probability interpretation [1], many-words interpretation [2], and de Broglie-Bohm interpretation (theory of pilot wave) [3]. The last mentioned theory takes place in a single space-time, is non - local, and is deterministic. Moreover, Born’s ensemble and Watanabe’s time-symmetric theory being an analogy of Wheeler – Feynman theory should be mentioned. The time-symmetric interpretation was later, in the 60s re- elaborated by Aharonov and it became in the 80s the starting point for so called transactional interpretation of quantum mechanics. More modern interpretations cover a spontaneous collapse of wave function (here, a new non-linear component, causing this collapse is added to Schrödinger equation), decoherence interpretation (wave function is reduced due to an interaction of a quantum- mechanical system with its surroundings) and relational interpretation [4] elaborated by C. Rovelli in 1996. This interpretation treats the state of a quantum system as being observer-dependent, i.e. the state is the relation between the observer and the system. Relational interpretation is able to solve the EPR paradox. -
Lecture 8 Symmetries, Conserved Quantities, and the Labeling of States Angular Momentum
Lecture 8 Symmetries, conserved quantities, and the labeling of states Angular Momentum Today’s Program: 1. Symmetries and conserved quantities – labeling of states 2. Ehrenfest Theorem – the greatest theorem of all times (in Prof. Anikeeva’s opinion) 3. Angular momentum in QM ˆ2 ˆ 4. Finding the eigenfunctions of L and Lz - the spherical harmonics. Questions you will by able to answer by the end of today’s lecture 1. How are constants of motion used to label states? 2. How to use Ehrenfest theorem to determine whether the physical quantity is a constant of motion (i.e. does not change in time)? 3. What is the connection between symmetries and constant of motion? 4. What are the properties of “conservative systems”? 5. What is the dispersion relation for a free particle, why are E and k used as labels? 6. How to derive the orbital angular momentum observable? 7. How to check if a given vector observable is in fact an angular momentum? References: 1. Introductory Quantum and Statistical Mechanics, Hagelstein, Senturia, Orlando. 2. Principles of Quantum Mechanics, Shankar. 3. http://mathworld.wolfram.com/SphericalHarmonic.html 1 Symmetries, conserved quantities and constants of motion – how do we identify and label states (good quantum numbers) The connection between symmetries and conserved quantities: In the previous section we showed that the Hamiltonian function plays a major role in our understanding of quantum mechanics using it we could find both the eigenfunctions of the Hamiltonian and the time evolution of the system. What do we mean by when we say an object is symmetric?�What we mean is that if we take the object perform a particular operation on it and then compare the result to the initial situation they are indistinguishable.�When one speaks of a symmetry it is critical to state symmetric with respect to which operation.