Education

Teaching and learning about math, Maple and MapleSim

These files contain the kinematics and dynamics of the solid using a new technique (ALT + ENTER) to visualize the results online and thus save space in our Maple worksheet. Seen from a relative approach. For engineering students. In Spanish.

Equations_of_movement_for_particle.mw  (Intro)

Kinematics_and_relative_dynamics_of_a_solid.mw

Flat_kinetic_of_a_rigid_body.mw

Lenin Araujo Castillo

Ambassador of Maple

The video shows the curvilinear components of acceleration in polar coordinates, radial and tangential scalar components. Applied to a structure; in a time interval; to finally be graphed and interpreted. For engineering students.

Speed_and_Acceleration_with_Cylindrical_Components.mw

Lenin Araujo Castillo

Ambassador of Maple

As you may have already heard, in order accelerate the growth of our different product lines, Maplesoft decided to spin off our online education products into a separate company. Let me introduce you to DigitalEd.

DigitalEd will focus on online education technology, including online courseware, testing and assessment, and placement testing (i.e. Möbus, Maple T.A., and the Maple T.A. MAA Placement Test Suite). Maplesoft will continue to develop and support Maple, MapleSim, and their related products, for both academic and professional use.  The two companies will maintain close ties, and Maple will continue to supply the mathematical engine to the DigitalEd product line.

For more information about this change, see https://www.maplesoft.com/digitaled/

You can take a look at the new DigitalEd web site here:  www.digitaled.com.

For the past two years the Queen’s College of Guyana Alumni Association (NY) has been hosting its Queen’s College Summer Maths Institute, and Maplesoft has supported this initiative by giving students access to Maple.   With Dr. Terrence Blackman at its helm, the institute aims to sustainably implement a developmentally appropriate and culturally resonant middle school learning environment that engages Guyanese students in a cognitively rich mathematics learning experience.  The experience is intended to place them securely on pathways to STEM (Science, Technology, Engineering and Math) excellence.

The program uses a developmentally appropriate approach that combines significant mathematical content with a setting that encourages a sense of discovery and excitement about math through problem solving and exploration. Program Manager Shindy Johnson, a former student of Queens College, noted that by the end of the first week math sceptics fall in love and gained confidence, and math lovers renew their passion.

As avid Maple users, Dr. Terence Blackman and Cleveland Waddell, one of the main organizers and lecturer,  give the students the opportunity to use Maple. Last year, the students were amazed by Maple's computational power. “It was nothing like they have seen before.  Many students also wrote their first lines of computer code using Maple during the summer camp.  Maple is an invaluable resource for us during the camp,” said Waddell.

Dr. Terence Blackman and Cleveland Waddell

Students receive further enrichment through field trips to broaden their appreciation for education and industry in Guyana.  In addition, Guyanese professionals visit the Institute to share their expertise, career journeys and practical applications of math and other STEM disciplines in their professions.

Field trip to Uitvlugt Sugar Estate

Students who participate ranged from self-professed math lovers to teens who confessed to fearing and even loathing math. By the end of the first week, math lovers had discovered even greater “beauty in the mathematics” and those who quaked at the thought of math were beginning to commit – with confidence – to improving their math grades. This year’s Queen’s College Summer Maths Institute will take place July 26-August 3, 2018 in Georgetown, Guyana.   

Recently I looked through an interesting book D. Wells "The Penquin book of Curious and Interesting Geometry" and came across this result, which I did not know about before: starting with a given quadrilateral , construct a square on each side. Van Aubel's theorem states that the two line segments between the centers of opposite squares are of equal lengths and are at right angles to one another. See the picture below:

                                  

It is interesting that this is true not only for a convex quadrilateral, but for arbitrary one and even self-intersecting one. This post is devoted to proving this result in Maple. The proof was very short and simple. Note that the coordinates of points are given not by lists, but by vectors. This is convenient when using  LinearAlgebra:-DotProduct  and  LinearAlgebra:-Norm  commands.

The code of the proof (the notation of the points on the picture coincide with their names in the code):

restart;
with(LinearAlgebra):
assign(seq(P||i=<x[i],y[i]>, i=1..4)):
P||5:=P||1:
assign(seq(Q||i=(P||i+P||(i+1))/2+<0,1; -1,0>.(P||(i+1)-P||i)/2, i=1..4)):
expand(DotProduct((Q||3-Q||1),(Q||4-Q||2), conjugate=false));
is(Norm(Q||3-Q||1, 2)=Norm(Q||4-Q||2, 2));

The output:

                                                      0
                                                    true

 

VA.mw


 

For Maple 2018.1, there are improvements in pdsolve's ability to solve PDE with boundary and initial conditions. This is work done together with E.S. Cheb-Terrab. The improvements include an extended ability to solve problems involving non-homogeneous PDE and/or non-homogeneous boundary and initial conditions, as well as improved simplification of solutions and better handling of functions such as piecewise in the arguments and in the processing of solutions. This is also an ongoing project, with updates being distributed regularly within the Physics Updates.

Solving more problems involving non-homogeneous PDE and/or non-homogeneous boundary and initial conditions

 

 

Example 1: Pinchover and Rubinstein's exercise 6.17: we have a non-homogenous PDE and boundary and initial conditions that are also non-homogeneous:

pde__1 := diff(u(x, t), t)-(diff(u(x, t), x, x)) = 1+x*cos(t)
iv__1 := (D[1](u))(0, t) = sin(t), (D[1](u))(1, t) = sin(t), u(x, 0) = 1+cos(2*Pi*x)

pdsolve([pde__1, iv__1])

u(x, t) = 1+cos(2*Pi*x)*exp(-4*Pi^2*t)+t+x*sin(t)

(1)

How we solve the problem, step by step:

   

 

Example 2: the PDE is homogeneous but the boundary conditions are not. We solve the problem through the same process, which means we end up solving a nonhomogeneous pde with homogeneous BC as an intermediate step:

pde__2 := diff(u(x, t), t) = 13*(diff(u(x, t), x, x))
iv__2 := (D[1](u))(0, t) = 0, (D[1](u))(1, t) = 1, u(x, 0) = (1/2)*x^2+x

pdsolve([pde__2, iv__2])

u(x, t) = 1/2+Sum(2*(-1+(-1)^n)*cos(n*Pi*x)*exp(-13*Pi^2*n^2*t)/(Pi^2*n^2), n = 1 .. infinity)+13*t+(1/2)*x^2

(12)

How we solve the problem, step by step:

   

 

Example 3: a wave PDE with a source that does not depend on time:

pde__3 := (diff(u(x, t), x, x))*a^2+1 = diff(u(x, t), t, t)
iv__3 := u(0, t) = 0, u(L, t) = 0, u(x, 0) = f(x), (D[2](u))(x, 0) = g(x)

`assuming`([pdsolve([pde__3, iv__3])], [L > 0])

u(x, t) = (1/2)*(2*(Sum(sin(n*Pi*x/L)*(2*L*(Int(sin(n*Pi*x/L)*g(x), x = 0 .. L))*sin(a*Pi*t*n/L)*a-Pi*(Int(sin(n*Pi*x/L)*(-2*f(x)*a^2+L*x-x^2), x = 0 .. L))*cos(a*Pi*t*n/L)*n)/(Pi*n*a^2*L), n = 1 .. infinity))*a^2+L*x-x^2)/a^2

(23)

How we solve the problem, step by step:

   

 

Example 4: Pinchover and Rubinstein's exercise 6.23 - we have a non-homogenous PDE and initial condition:

pde__4 := diff(u(x, t), t)-(diff(u(x, t), x, x)) = g(x, t)
iv__4 := (D[1](u))(0, t) = 0, (D[1](u))(1, t) = 0, u(x, 0) = f(x)

pdsolve([pde__4, iv__4], u(x, t))

u(x, t) = Int(f(tau1), tau1 = 0 .. 1)+Sum(2*(Int(f(tau1)*cos(n*Pi*tau1), tau1 = 0 .. 1))*cos(n*Pi*x)*exp(-Pi^2*n^2*t), n = 1 .. infinity)+Int(Int(g(x, tau1), x = 0 .. 1)+Sum(2*(Int(g(x, tau1)*cos(n1*Pi*x), x = 0 .. 1))*cos(n1*Pi*x)*exp(-Pi^2*n1^2*(t-tau1)), n1 = 1 .. infinity), tau1 = 0 .. t)

(30)

If we now make the functions f and g into specific mappings, we can compare pdsolve's solutions to the general and specific problems:

f := proc (x) options operator, arrow; 3*cos(42*x*Pi) end proc
g := proc (x, t) options operator, arrow; exp(3*t)*cos(17*x*Pi) end proc

 

Here is what pdsolve's solution to the general problem looks like when taking into account the new values of f(x) and g(x,t):

value(simplify(evalindets(u(x, t) = Int(f(tau1), tau1 = 0 .. 1)+Sum(2*(Int(f(tau1)*cos(n*Pi*tau1), tau1 = 0 .. 1))*cos(n*Pi*x)*exp(-Pi^2*n^2*t), n = 1 .. infinity)+Int(Int(g(x, tau1), x = 0 .. 1)+Sum(2*(Int(g(x, tau1)*cos(n1*Pi*x), x = 0 .. 1))*cos(n1*Pi*x)*exp(-Pi^2*n1^2*(t-tau1)), n1 = 1 .. infinity), tau1 = 0 .. t), specfunc(Int), proc (u) options operator, arrow; `PDEtools/int`(op(u), AllSolutions) end proc)))

u(x, t) = 3*cos(42*Pi*x)*exp(-1764*Pi^2*t)+cos(Pi*x)*(65536*cos(Pi*x)^16-278528*cos(Pi*x)^14+487424*cos(Pi*x)^12-452608*cos(Pi*x)^10+239360*cos(Pi*x)^8-71808*cos(Pi*x)^6+11424*cos(Pi*x)^4-816*cos(Pi*x)^2+17)*(exp(289*Pi^2*t+3*t)-1)*exp(-289*Pi^2*t)/(289*Pi^2+3)

(31)

 

Here is pdsolve's solution to the specific problem:

pdsolve([pde__4, iv__4], u(x, t))

u(x, t) = ((867*Pi^2+9)*cos(42*Pi*x)*exp(-1764*Pi^2*t)+cos(17*Pi*x)*(exp(3*t)-exp(-289*Pi^2*t)))/(289*Pi^2+3)

(32)

 

And the two solutions are equal:

simplify(combine((u(x, t) = 3*cos(42*x*Pi)*exp(-1764*Pi^2*t)+cos(x*Pi)*(65536*cos(x*Pi)^16-278528*cos(x*Pi)^14+487424*cos(x*Pi)^12-452608*cos(x*Pi)^10+239360*cos(x*Pi)^8-71808*cos(x*Pi)^6+11424*cos(x*Pi)^4-816*cos(x*Pi)^2+17)*(exp(289*Pi^2*t+3*t)-1)*exp(-289*Pi^2*t)/(289*Pi^2+3))-(u(x, t) = ((867*Pi^2+9)*cos(42*x*Pi)*exp(-1764*Pi^2*t)+cos(17*x*Pi)*(exp(3*t)-exp(-289*Pi^2*t)))/(289*Pi^2+3)), trig))

0 = 0

(33)

f := 'f'; g := 'g'

 

Improved simplification in integrals, piecewise functions, and sums in the solutions returned by pdsolve

 

 

Example 1: exercise 6.21 from Pinchover and Rubinstein is a non-homogeneous heat problem. Its solution used to include unevaluated integrals and sums, but is now returned in a significantly simpler format.

pde__5 := diff(u(x, t), t)-(diff(u(x, t), x, x)) = t*cos(2001*x)
iv__5 := (D[1](u))(0, t) = 0, (D[1](u))(Pi, t) = 0, u(x, 0) = Pi*cos(2*x)

pdsolve([pde__5, iv__5])

u(x, t) = (1/16032024008001)*(4004001*t+exp(-4004001*t)-1)*cos(2001*x)+Pi*cos(2*x)*exp(-4*t)

(34)

pdetest(%, [pde__5, iv__5])

[0, 0, 0, 0]

(35)

 

Example 2: example 6.46 from Pinchover and Rubinstein is a non-homogeneous heat equation with non-homogeneous boundary and initial conditions. Its solution used to involve two separate sums with unevaluated integrals, but is now returned with only one sum and unevaluated integral.

pde__6 := diff(u(x, t), t)-(diff(u(x, t), x, x)) = exp(-t)*sin(3*x)
iv__6 := u(0, t) = 0, u(Pi, t) = 1, u(x, 0) = phi(x)

pdsolve([pde__6, iv__6], u(x, t))

u(x, t) = (1/8)*(8*(Sum(2*(Int(-(-phi(x)*Pi+x)*sin(n*x), x = 0 .. Pi))*sin(n*x)*exp(-n^2*t)/Pi^2, n = 1 .. infinity))*Pi-Pi*(exp(-9*t)-exp(-t))*sin(3*x)+8*x)/Pi

(36)

pdetest(%, [pde__6, iv__6])

[0, 0, 0, (-phi(x)*Pi^2+Pi*x+2*(Sum((Int(-(-phi(x)*Pi+x)*sin(n*x), x = 0 .. Pi))*sin(n*x), n = 1 .. infinity)))/Pi^2]

(37)

 

More accuracy when returning series solutions that have exceptions for certain values of the summation index or a parameter

 

 

Example 1: the answer to this problem was previously given with n = 0 .. infinity instead of n = 1 .. infinity as it should be:

pde__7 := diff(v(x, t), t, t)-(diff(v(x, t), x, x))

iv__7 := v(0, t) = 0, v(x, 0) = -(exp(2)*x-exp(x+1)-x+exp(1-x))/(exp(2)-1), (D[2](v))(x, 0) = 1+(exp(2)*x-exp(x+1)-x+exp(1-x))/(exp(2)-1), v(1, t) = 0

pdsolve([pde__7, iv__7])

v(x, t) = Sum(-2*sin(n*Pi*x)*((Pi^2*(-1)^n*n^2-Pi^2*n^2+2*(-1)^n-1)*sin(Pi*t*n)-(-1)^n*cos(Pi*t*n)*Pi*n)/(Pi^2*n^2*(Pi^2*n^2+1)), n = 1 .. infinity)

(38)

 

Example 2: the answer to exercise 6.25 from Pinchover and Rubinstein is now given in a much simpler format, with the special limit case for w = 0 calculated separately:

pde__8 := diff(u(x, t), t) = k*(diff(u(x, t), x, x))+cos(w*t)
iv__8 := (D[1](u))(L, t) = 0, (D[1](u))(0, t) = 0, u(x, 0) = x

`assuming`([pdsolve([pde__8, iv__8], u(x, t))], [L > 0])

u(x, t) = piecewise(w = 0, (1/2)*L+Sum(2*L*(-1+(-1)^n)*cos(n*Pi*x/L)*exp(-Pi^2*n^2*k*t/L^2)/(n^2*Pi^2), n = 1 .. infinity)+t, (1/2)*(L*w+2*(Sum(2*L*(-1+(-1)^n)*cos(n*Pi*x/L)*exp(-Pi^2*n^2*k*t/L^2)/(n^2*Pi^2), n = 1 .. infinity))*w+2*sin(w*t))/w)

(39)

 

Improved handling of piecewise, eval/diff in the given problem

 

 

Example 1: this problem, which contains a piecewise function in the initial condition, can now be solved:

pde__9 := diff(f(x, t), t) = diff(f(x, t), x, x)
iv__9 := f(0, t) = 0, f(1, t) = 1, f(x, 0) = piecewise(x = 0, 0, 1)

pdsolve([pde__9, iv__9])

f(x, t) = Sum(2*sin(n*Pi*x)*exp(-Pi^2*n^2*t)/(n*Pi), n = 1 .. infinity)+x

(40)

 

Example 2: this problem, which contains a derivative written using eval/diff, can now be solved:

pde__10 := -(diff(u(x, t), t, t))-(diff(u(x, t), x, x))+u(x, t) = 2*exp(-t)*(x-(1/2)*x^2+(1/2)*t-1)

iv__10 := u(x, 0) = x^2-2*x, u(x, 1) = u(x, 1/2)+((1/2)*x^2-x)*exp(-1)-((3/4)*x^2-(3/2)*x)*exp(-1/2), u(0, t) = 0, eval(diff(u(x, t), x), {x = 1}) = 0

pdsolve([pde__10, iv__10], u(x, t))

u(x, t) = -(1/2)*exp(-t)*x*(x-2)*(t-2)

(41)

 

References:

 

Pinchover, Y. and Rubinstein, J.. An Introduction to Partial Differential Equations. Cambridge UP, 2005.


 

Download What_is_New_after_Maple_2018.mw

Katherina von Bülow

I just feel that if at least one person less experienced than me reads this it will be a worth while post, because it will help them avoid things that eluded me when I was younger.


 

The omitted function definitions are not relevant to the reason for which I decided to post about this. I would like the maple user to simply observe how many variables are involved in the relation's (R) three equalities in the consideration of the output.

 

The reason I believe this is important, is that it is sometimes very easy to believe induction is sufficient proof of the truth value of a relation over the superset of a subset that has been enumerated, much like the example of the coefficients of the
"105^(th) cyclotomic polynomial if one were to inductively reason statements about the coeffiecents of the previous 104 polynomials."

 

 

A[n, k, M] = abs(C[0](n, k, M))/abs(C[1](n, k, M)); B[n, k, M] = abs(C[0](n, k, M))/abs(C[2](n, k, M)); E[n, k, M] = abs(C[1](n, k, M))/abs(C[2](n, k, M))

R

"`&nscr;`(A[n,k,M])=`&nscr;`(B[n,k,M]), `&nscr;`(E[n,k,M])=`&dscr;`(A[n,k,M]),`&dscr;`(B[n,k,M])=`&dscr;`(E[n,k,M])]"

for t to 7 do R(t, 2, 30) end do

[1 = 1, 1 = 1, 1 = 1]

 

[1 = 1, 1 = 1, 1 = 1]

 

[1 = 1, 1 = 1, 1 = 1]

 

[1 = 1, 1 = 1, 1 = 1]

 

[1 = 1, 1 = 1, 1 = 1]

 

[1 = 1, 1 = 1, 1 = 1]

 

[1 = 11^(1/2)*7^(1/2), 11^(1/2)*7^(1/2) = 1, 7 = 7]

(1)


 

Download INDUCTION_IS_NOT_YOUR_FREN.mw

Up to swMATH   Maple is referenced in 4183 articles   in zbMATH   and

up to swMATH Mathematica is referenced in 4654 articles  in zbMATH.

These numbers are sure unexpected to me. I think the ratio of the prices of academic editions MMA/Maple (which approximately equals 2 ) truly reflects their capabilities.

It post can be called a continuation of the theme “Determination of the angles of the manipulator with the help of its mathematical model. Inverse  problem”.
Consider  the use of manipulators as multi-axis CNC machines.
Three-link manipulator with 5 degrees of freedom. In these examples  one of the restrictions on the movement of the manipulator links is that the position of the last link coincides with the normal to the surface along the entire trajectory of the working point movement.
That is, we, as it were, mathematically transform a system with many degrees of freedom to an analog of a lever mechanism with one degree of freedom, so that we can do the necessary work in a convenient to us way.
It seems that this approach is fully applicable directly to multi-axis CNC machines.

(In the texts of the programs, the normalization is carried out with respect to the coordinates of the last point, in order that the lengths of the integration interval coincide with the path length.)
MAN_3_5_for_MP.mw

MAN_3_5_for_MP_TR.mw

Due to the mechanistic process of our students and little creativity in analysis in schools and universities to be professionally trained is that STEM education appears (science, technology, engineering and mathematics) is a new model that is being considered in other countries and with very slow step in our city. In this work the methods with STEM will be visualized but using computational tools provided by Maplesoft which is a company that leads online education for adolescents and adults in the current market. In Spanish.

ECI_UNT_2018.pdf

ECI_UNT_2018.mw

Lenin Araujo Castillo

Ambassador of Maple

Using Maple's native syntax, we can calculate the components of acceleration. That is, the tangent and normal scalar component with its respective units of measure. Now the difficult calculations were in the past because with Maple we solved it and we concentrated on the interpretation of the results for engineering. In spanish.

Calculo_Componentes_Aceleracion_Curvilínea.mw

Uso_de_comandos_y_operadores_para_calculos_de_componentes_de_la_aceleración.mw

Lenin Araujo Castillo

Ambassador of Maple

 

 

Last week, my colleague Erik Postma and I had the pleasure of spending a few hours with a group of bright and motivated high school students at the Math for Real: High School Math Solves Real Problems workshop held at the Fields Institute for Research in Mathematical Sciences in Toronto, and sponsored by the Fields Institute and NSERC PromoScience. The purpose of this three-day workshop was to train students for the International Mathematical Modeling Challenge, also known as IM2C.

The IM2C is hosted by York University and run by the IM2C-Canada committee, consisting of parents and high school teachers, as well as faculty and students in York’s Department of Mathematics and Statistics. In this competition, students working in small teams have five days to solve a mathematical modelling problem in diverse application areas. To support the “Real World” aspect of the contest, students are expected not just to showcase their mathematical creativity and problem-solving skills, but they are also asked to clearly communicate their analyses and conclusions through a written report and visualizations.

The contest allows students to use appropriate software tools to help them with their tasks. Of course I am biased but I can’t help thinking that Maple is the perfect tool for students wanting to do a combination of prototyping, modelling, visualization and document-preparation. The IM2C organizers also thought that the students could benefit from our software, so Erik gave an hour-long introduction to Maple. I was impressed by the students’ enthusiastic remarks and sometimes challenging questions, though admittedly they were partly motivated by the chance to receive as prizes our highly coveted limited-quantity “Math Matters” t-shirts.

The workshop also introduced the students to other software products, taught modelling and writing skills, and had them work on fun practice problems. Over the lunch break, I was struck by the sense of camaraderie at this event, which probably should not have surprised me, as unlike many other competitions involving mathematics, this one is a true team-based activity. Both Erik and I are eager to see what the students will be doing with Maple. Responding to the students’ enthusiasm and interest, Maplesoft has agreed to offer complimentary Maple licenses to all students participating in IM2C. 

As a Corporate Affiliate of the Fields Institute, Maplesoft is pleased to provide training and support to students and researchers that come to Fields for its many events. Developers like myself are encouraged to participate in the institute’s events when possible, and I’ve had the opportunity to attend a number of workshops in the past few years. I encourage you to look at their wide range of activities and to consider visiting the culturally diverse city of Toronto!


Minimum:
1. Maple Tour
2. Maple Quick Start
3. Quick Help
4. Quick Reference Card
5. Math Apps
6. Plotting Guide
...
https://drive.google.com/file/d/1ZAlFQ8_MbuKNsr2PDIyEHmIoogKInBmE/view?usp=sharing

Is that only for my students?

In worksheets:
https://drive.google.com/file/d/1cfA8WKPXSQQxJQR7KbyYHBY6g4OjHaSn/view?usp=sharing

The first update to the Maple 2018 Physics, Differential Equations and Mathematical Functions packages is available. As has been the case since 2013, this update contains fixes, enhancements to existing functionality, and new developments in the three areas. 

The webpage for these updates will continue being the Maplesoft R&D Physics webpage. Starting with Maple 2018, however, this update is also available from the MapleCloud.

To install the update: open Maple and click the Cloud icon (upper-right corner), select "Packages" and search for "Physics Updates". Then, in the corresponding "Actions" column, click the third icon (install pop-up).

NOTE May/1: the "Updates" icon of the MapleCloud toolbar (that opens when you click the upper-right icon within a Maple document / worksheet), now works fine, after having installed the Physics Updates version 32 or higher.

These first updates include:

  • New Physics functionality regarding Tensor Products of Quantum States; and Coherent States.
  • Updates to pdsolve regarding PDE & Boundary Conditions (exact solutions);
  • A change in notation: d_(x), the differential of a coordinate in the Physics package, is now displayed as shown in this Mapleprimes post.


Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

This is about the recent implementation of tensor products of quantum state spaces in the Physics package, in connection with an exchange with the Physics of Information Lab of the University of Waterloo. As usual this development is available to everybody from the Maplesoft R&D Physics webpage. This is the last update for Maple 2017. The updates for Maple 2018, starting with this same material, will begin being distributed through the MapleCloud next week.

Tensor Product of Quantum State Spaces

 

Basic ideas and design

 

 

Suppose A and B are quantum operators and Ket(A, n), et(B, m) are, respectively, their eigenkets. The following works since the introduction of the Physics package in Maple

with(Physics)

Setup(op = {A, B})

`* Partial match of  'op' against keyword 'quantumoperators'`

 

[quantumoperators = {A, B}]

(1)

A*Ket(A, alpha) = A.Ket(A, alpha)

Physics:-`*`(A, Physics:-Ket(A, alpha)) = alpha*Physics:-Ket(A, alpha)

(2)

B*Ket(B, beta) = B.Ket(B, beta)

Physics:-`*`(B, Physics:-Ket(B, beta)) = beta*Physics:-Ket(B, beta)

(3)

where on the left-hand sides the product operator `*` is used as a sort of inert form (it has all the correct mathematical properties but does not perform the contraction) of the dot product operator `.`, used on the right-hand sides.

 

Suppose now that A and B act on different, disjointed, Hilbert spaces.

 

1) To represent that, a new keyword in Setup , is introduced, to indicate which spaces are disjointed, say as in disjointedhilbertspaces = {A, B}.  We want this notation to pop up at some point as {`&Hscr;`[A], `&Hscr;`[B]} where the indexation indicates all the operators acting on that Hilbert space. The disjointedspaces keyword has for synonyms disjointedhilbertspaces and hilbertspaces. The display `&Hscr;`[A] is not yet implemented.

 

NOTE: noncommutative quantum operators acting on disjointed spaces commute between themselves, so after setting  - for instance - disjointedspaces = {A, B}, automatically, A, B become quantum operators satisfying (see comment (ii) on page 156 of ref. [1])

 

"[A,B][-]=0"

 

2) Product of Kets and Bras (KK, BB, KB and BK) where K and B belong to disjointed spaces, are understood as tensor products satisfying, for instance with disjointed spaces A and B (see footnote on page 154 of ref. [1]),

 

`&otimes;`(Ket(A, alpha), Ket(B, beta)) = `&otimes;`(Ket(B, beta), Ket(A, alpha)) 

 

`&otimes;`(Bra(A, alpha), Ket(B, beta)) = `&otimes;`(Ket(B, beta), Bra(A, alpha)) 

 

while of course

Bra(A, alpha)*Ket(A, alpha) <> Bra(A, alpha)*Ket(A, alpha)

 

Details

   

 

3) All the operators of one disjointed space act transparently over operators, Bras and Kets of the other disjointed spaces, for example

 

A*Ket(B, n) = A*Ket(B, n)

and the same for the Dagger of this equation, that is

Bra(B, n)*Dagger(A) = Bra(B, n)*Dagger(A)

 

And this happens automatically. Hence, when we write the left-hand sides and press enter, they are automatically rewritten (returned) as the right-hand sides.

 

Note that for the product of an operator times a Bra or a Ket we are not using the notation that expresses the product with the symbol 5.

 

Regarding the display of Bras and Kets and their tensor products, two enhancements are happening:

 

• 

A new Setup option hideketlabel makes all the labels in Kets and Bras to be hidden when displaying Kets, Bras and Bracket(s), with the indices presented one level up, as if they were a sequence of labels, so that:

 "Ket(A,m,n,l"  

is displayed as

Ket(A, m, n, l)

 

  

This is the notation used more frequently when working in quantum information. This hideketlabel option is already implemented entering Setup(hideketlabel = true)

• 

Tensor products formed with operators, or Bras and Kets, that belong to disjointed spaces (set as such using Setup ), are displayed with the symbol 5 in between, as in Ket(A, n)*Ket(B, n) instead of Ket(A, n)*Ket(B, n), and `&otimes;`(A, B) instead of A*B.

Tensor product notation and the hideketlabel option

   

The implementation of tensor products using `*` and `.`

   

Basic exercising with the new functionality

   

Related functionality already in place before these changes

   

Reference

 

[1] Cohen-Tannoudji, Diue, Laloe, Quantum Mechanics, Chapter 2, section F.

[2] Griffiths Robert B., Hilbert Space Quantum Mechanics, Quantum Computation and Quantum Information Theory Course, Physics Department, Carnegie Mellon University, 2014.

See also

   

 


 

Download TensorProductDesign.mw
 

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

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