Solutions to problem set ); (, ) (

Similar documents
Non-degenerate Perturbation Theory

Some Different Perspectives on Linear Least Squares

Coherent Potential Approximation

( t) ( t) ( t) ρ ψ ψ. (9.1)

MOLECULAR VIBRATIONS

Lecture 07: Poles and Zeros

7.0 Equality Contraints: Lagrange Multipliers

Stationary states of atoms and molecules

Debabrata Dey and Atanu Lahiri

The Mathematical Appendix

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

Multiple Choice Test. Chapter Adequacy of Models for Regression

ρ < 1 be five real numbers. The

Introduction. Free Electron Fermi Gas. Energy Levels in One Dimension

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

Lecture IV : The Hartree-Fock method

Point Estimation: definition of estimators

STK4011 and STK9011 Autumn 2016

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

TESTS BASED ON MAXIMUM LIKELIHOOD

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

CHAPTER 4 RADICAL EXPRESSIONS

Chapter 4 Multiple Random Variables

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix

Econometric Methods. Review of Estimation

Some results and conjectures about recurrence relations for certain sequences of binomial sums.

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Chapter 9 Jordan Block Matrices

Algorithms Theory, Solution for Assignment 2

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

Numerical Analysis Formulae Booklet

Summary of the lecture in Biostatistics

Mu Sequences/Series Solutions National Convention 2014

The Mathematics of Portfolio Theory

Lecture 3 Probability review (cont d)

x y exp λ'. x exp λ 2. x exp 1.

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

18.413: Error Correcting Codes Lab March 2, Lecture 8

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter

L5 Polynomial / Spline Curves

1 Solution to Problem 6.40

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations.

16 Homework lecture 16

Transforms that are commonly used are separable

Chapter 14 Logistic Regression Models

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

1 Onto functions and bijections Applications to Counting

Functions of Random Variables

On the characteristics of partial differential equations

Applying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Unsupervised Learning and Other Neural Networks

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

ENGI 4421 Propagation of Error Page 8-01

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Class 13,14 June 17, 19, 2015

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

Laboratory I.10 It All Adds Up

CHAPTER VI Statistical Analysis of Experimental Data

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION

Algorithms behind the Correlation Setting Window

Ideal multigrades with trigonometric coefficients

3.1 Introduction to Multinomial Logit and Probit

Training Sample Model: Given n observations, [[( Yi, x i the sample model can be expressed as (1) where, zero and variance σ

MATH 247/Winter Notes on the adjoint and on normal operators.

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

Chapter 5 Properties of a Random Sample

Complex plane of Gauss. (a, b)

Qualifying Exam Statistical Theory Problem Solutions August 2005

1 Lyapunov Stability Theory

Chapter 3. Linear Equations and Matrices

2SLS Estimates ECON In this case, begin with the assumption that E[ i

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections

Wu-Hausman Test: But if X and ε are independent, βˆ. ECON 324 Page 1

Arithmetic Mean and Geometric Mean

Lecture 8 IEEE DCF Performance

Random Variables and Probability Distributions

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

CHAPTER 3 POSTERIOR DISTRIBUTIONS

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

= lim. (x 1 x 2... x n ) 1 n. = log. x i. = M, n

Chapter 3 Sampling For Proportions and Percentages

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Lecture 9: Tolerant Testing

Third handout: On the Gini Index

STA302/1001-Fall 2008 Midterm Test October 21, 2008

MA 524 Homework 6 Solutions

CS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.

Answer key to problem set # 2 ECON 342 J. Marcelo Ochoa Spring, 2009

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

Transcription:

Solutos to proble set.. L = ( yp p ); L = ( p p ); y y L, L = yp p, p p = yp p, + p [, p ] y y y = yp + p = L y Here we use for eaple that yp, p = yp p p yp = yp, p = yp : factors that coute ca be treated as costats. The safe way s to epad all, of course. Here I dd ot eve epad the oetu operators as p =, but used the coutato relato betwee the operators drectly: [ pˆ ] ˆ, =.. ( )( ) [, ] L L L L L L L L L L L L ( ) L + = + = + + = + + = L L + L y y y y y s the prevous ecercse there s o eed to wrte out the operators. It s easer to evaluate the coutators drectly. L, L = [ L, L + L ] = L, L + L, L = L + ( ) L = L + y y y + L, L = [ L, L L ] = L, L L, L = L ( ) L = L y y y 3. Ψ(, t) t t c e Et/ c e Et/ = [ ψ ( ) + ψ ( ) ] = Et/ Et/ ceψ ( e ) + ceψ ( e ) (, ) ( Et/ ( ) Et/ HΨ t = H c e ψ + c e ψ ( ) E / ( / ce H ( )) ce ( H t Et = ψ + ψ ( )) Et = ceψ ( e ) + ceψ ( e ) / Et/

ad you see that both evaluate to the sae epresso. Therefore Ψ(, t ) satsfes the t te-depedet Schrödger equato Ψ (, ) = H Ψ (, t). Please ote that t H Ψ(, t) EΨ(, t), ad Ψ(, t ) caot be wrtte as Φ( ) ϕ ( t). Ths s ot a statoary state, uless E = E. Fally Ψ( t, ) does deped o te. Let e ephase statoary states are very specal. There are other ways to satsfy the TDSE. The above proof oly depeds o the fact that t ad H are lear operators (ad H Et/ does ot deped o te). Therefore ay lear cobato of fuctos ψ e satsfes the te-depedet Schrödger equato. It does ot have to be a egestate! 4. d d = Ψ ( t, ) Ψ( t, ) dt dt Ψ( t, ) Ψ( t, ) = [ Ψ(, t) + Ψ( t, ) d dt dt = Ψ t H Ψ t [ (,)( (,)) Ψ(,) t HΨ(,)] t d = [ Ψ(,) t H( Ψ(,)) t Ψ(,) t HΨ(,)] t d = Ψ( t, ) [ H, ] Ψ( td, ) I the secod le we used the cople cojugate of the Schrödger equato, ad the thrd le we used that H s hertea: Χ( )( HΦ( )) d = Φ( ) HΧ( ) d. d d H, = [, ] + V( ), = + 0 = d d P. The rest of the proble s straghtforward ad we fd that the average values quatu echacs satsfy the classcal deftos ad equatos of oto (see als MS 4-7).

5. operator O s Hertea f for ay two fuctos f ( ), g( ) that satsfy the B boudary codtos the tegral g ( )[ Of ( )] d = f( )[ Og ( )] d. The tegrato terval s fro 0..a for the partcle the bo, over a perod 0.. π or π.. π for the partcle o the rg, whle for the Haroc oscllator proble t would eted fro... Cosder the oetu operator p = : B df g p f d g d d g f B B f g ( ) ( ) ( ) = ( )( ) = ( ) ( ) + ( ) d B g( ) B = f( )[ ] d = f ( )[ p g ( )] d B (Where we used partal tegrato). The crucal part s the vashg of g f B ( ) ( ). For all of the eaples lsted above, you ay verfy that ths s true. I geeral the boudary codtos QM are always such that the above ter vashes. Ths s where the caveat that the fuctos should satsfy the boudary codtos coes. lso ote the careful use of brackets to dcate what s to be starred (cople cojugato). I these ecersses I a careful to oly star costats or fuctos. I do ot accept the starrg of operators. You ca oly use the foral defto of Hertcty. B 6. Et/ Et/ a. I ecercse 3 we have show geeral that a fucto ce φ ( ) + ce φ ( ) would satsfy the TDSE. Here we have =, =, whle E = ω ad E = 4ω. The wave fucto gve uder a hece satsfes the TDSE ad equals Ψ(, 0 ) at t=0, whch s all that s requred. b. π t = : Ψ(, t) = [ s( π) + s( π)] ω π t = : Ψ(, t) = [ s( π) + s( π)] ω π t = : Ψ(, t) = [s( π) + s( π)] = Ψ(, 0) ω c.... Use pctures of s( π), s( π) ad add the wth proper factors. The agary part of the wave fucto oly shows up for t = π /ω. Please ote that the wavefucto s ero outsde the bo ( < 0; > ) d.

Ψ ωt 4ωt ωt 4ωt ( t, ) Ψ( t, ) = [ e s( π) + e s( π)][ e s( π) + e s( π)] 3ωt 3ωt = [s ( π) + s ( π) + ( e + e )s( π)s( π)] = [s ( π) + s ( π) + cos( 3ω t)s( π)s( π))] You see we have a oscllatory behavor of ths probablty dstrbuto wth agular ω frequecy 3ω, (or ν = 3 ), the dfferece oscllato of the two copoets of the π wave fucto. The wave fucto tself s perodc wth frequecy ω / π, as see uder b. (the slowest coo frequecy). e. To sketch the fucto, t s better to square the real ad agary parts of the wave fuctos as draw uder c, ad add the up pctorally. Ths s what t eas to take Ψ ( t, ) Ψ( t., ) 7. a. ˆ S ( ˆ ˆ = S+ + S ) ˆ ˆ ˆ S ( α + β) = ( S+ + S )( α + β) = (0+ α + β + 0) = [ ( α + β)] ˆ ˆ ˆ S ( α β) = ( S+ + S )( α β) = (0 α + β 0) = [ ( α β)] Ths shows that these fuctos are deed egefuctos of S ˆ wth respectve egevalues are + ad BS ˆ wth egevalue. The fucto α s a egefucto of the Haltoa B = B, whle β s a egefucto wth egevalue B. The wavefucto at te t=0 s hece a superposto of statoary states, ad followg the geeral recpe the wave fucto at te t s gve by Bt Bt Ψ ( t) = ( αe + βe ) = ( α[cos( Bt) s( Bt)] + β[cos( Bt) + s( Bt)] = ( α + β )cos( Bt ) ( α β )s( Bt ) I apulated the result for future coveece. You ay otce that the wavefucto s eplctly wrtte ter of the orthooral egefuctos of the S ˆ operator (see ht part b). Both the se ad cose oscllate as Bt. The perod s defed as the te t takes π for ths phase to chage by π, hece T =. B

b. The possble values that ca be easured for S ˆ are always oe of the egevalues: + or. The probabltes deped o the wavefucto Ψ () t P( + ) = ( α + β) Ψ ( t) = cos( Bt) P( + ) = ( α β) Ψ ( t) = s( Bt) = s( Bt), ad they are gve by Hece: TIME: P(+/) P(-/) t=0 0 t=t/4 =π /B 0 t= T/ =π /B 0 t=t/8=π /4B / / You see that the probabltes deped o the te of easureet. Ths s true because the wave fucto s ot a statoary state, whle the operator S ˆ does ot coute wth the Haltoa, ad so these operators do ot have a coplete set of coo egefuctos. If I would chage the proble such that you would easure S ˆ, the the possble outcoes would also be + or, but the probabltes would be Bt P( + ) = α Ψ ( t) = e = Bt P( ) = β Ψ ( t) = e = Ths s a geeral result. You ca prove that f the operator you easure, ad the Haltoa coute, the results of your easureet are depedet of te.

Proble 8: S&O.: a. b. Oe = e O j j j e Oe = e e O = e e O = δ O = O k k j j k j j kj j k j j j Oa = b Oe a j j j k k k j j k k k k j k k j j j e b e Oe a = e e b = δ b Oa = = b S&O.3: S&O.4 ( B) j = ( B) j = ( jkbk) = jkbk = kj Bk = Bk kj = ( B ) j k k k k a. ( ) ( ) Tr( B) = B = B = B = B = Tr( B) j j j j, j, j j jj b. c. d. e. B ( B) = B ( ) B= B B= B = ( B) B= U U = = = UBU UU UU ( B) ( ) B B = B B B = ( ) = ( ) ( ) = ( ) = ( ) = f. Show by eplct ultplcato that 0 = = 0

S&O.6: 6. Iterchagg equal rows does ot chage atr, but does chage sg deterat, hece = 0 7. 8. 9. = = = = = = = = = U OU = U OU = OU U = OUU = O = O S&O.8: Tr U OU = Tr U OU = Tr OU U = Tr OUU = Tr O = Tr O ( ) ( ( )) (( ) ) ( ) ( ) ( ) S&O.0,.. a+ b 0 = 0 a b S&O.3 f( a+ b) 0 f( ) = 0 f( a b) f( a+ b) f( a+ b) = f( a b) f( a b) f( a+ b) + f( a b) f( a+ b) f( a b) = f( a+ b) f( a b) f( a+ b) f( a b) S&O.4 ε ε ( ) δ ( ) = l ( ) l [ (0) '(0) ''(0)...] ε 0 = ε ε 0 ε + + + ε ε da a d a a a d 3 ε ε 3 ε ε = l[ { a(0)[ ] ε + a'(0)[ ] ε + a''(0)[ 6 ] ε +...} = a(0) + 0 + l[ +...] = a(0) ε 0 ε ε 0 6ε S&O.5: Bra-ket otato: O ˆ = k ko k j O ˆ = j ko = jko = δ O = O k k jk k j k k k

S&O.7 e: ψ( ) Ojψ j( ') = Oj j ' = O j j ' = O ' = O ', j, j, j The other probles are straghtforward substtuto.