Helps on understanding different representation transformations

This is known as the Floquet picture. However, this transformation may not always be necessary or useful depending on the specific problem you are trying to solve.
  • #1
luxiaolei
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Hi,all, I m an undergrades and I am suffering on understanding the different representation transformations, namely from schrodinger picture to interaction picture tupically, my lecturer didn't state which representation he was using and I m so confused, any helps would be great.

Shall I bring one example.

So I have an Hamiltonian for a 4 level ladder system, which consists a time independent and time dependent part(a perturbation from lasers),3 lasers which has frequency wa,wb,wc

H = H0+HI(t)

So, as far as I understood, this is not in schrodinger's picture. But confused it is in Heisenberg's or interaction pictures. So does the state vectors of above Hamiltonian, in which picture?

And if make a transformation in the way that

U= exp(iw1t)|1><1|+exp(i(w1+wa)t)|2><2|+exp(i(w1+wa+wb)t)|3><3|+exp(i(w1+wa+wb+wc)t)|4><4|

New state vectors = U*old state vectors
New Hamiltonian = U*H*U(dagger) - ihU*dU(dagger)/dt

So the new Hamiltonian becomes time independent , and state vectors becomes time dependent. So are they all now in the interaction pictures? Don't know why people do this transformation and what is the advantage?

Looks like to me this transformation gives a time independent Hamiltonian which is easier to work with, but what if I do want to keep the third laser time dependent, how should I construct this transformation?

I know, it's a lot questions, and I apologies for the lengthy newbie questions, and thanks so much in advance:)
 
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  • #2
The transformation you have done is indeed a representation transformation from the Schrodinger picture to the interaction picture. In the Schrodinger picture, the state vectors are time-independent and the Hamiltonian is time-dependent; in the interaction picture, the state vectors are time-dependent and the Hamiltonian is time-independent.The advantage of the transformation is that it allows us to treat the time dependence of the Hamiltonian more easily. This is because in the interaction picture, the Hamiltonian is time-independent so we don't need to consider how it changes with time.If you want to keep the third laser time dependent, then you will need to use a different transformation. One way to do this would be to use a transformation that involves the exponential of the sum of the three frequencies of the lasers (wa, wb, wc). This will give you a time-dependent Hamiltonian but the state vectors will still be time-independent.
 

1. What is the purpose of understanding different representation transformations?

Understanding different representation transformations is important for scientists because it allows them to accurately analyze and communicate their data. By knowing how to represent data in different formats, scientists can gain a deeper understanding of the patterns and relationships within their data.

2. What are some common types of representation transformations?

Some common types of representation transformations include scaling, rotation, translation, and shearing. Scaling involves changing the size of an object or data points. Rotation involves rotating an object or data points around a fixed point. Translation involves moving an object or data points horizontally or vertically. Shearing involves skewing an object or data points along one axis.

3. How do representation transformations affect data analysis?

Representation transformations can greatly impact data analysis by revealing patterns and relationships that may not be apparent in the original data. They can also help to simplify complex data and make it easier to interpret and communicate. Additionally, representation transformations can be used to correct for distortions or biases in the data.

4. What are some tools or techniques for understanding different representation transformations?

One common tool for understanding representation transformations is through the use of graphs and charts. These visual representations can help to illustrate the effects of different transformations on data. Another technique is to experiment with different transformations on a small set of data to see how they affect the overall representation.

5. How can understanding different representation transformations benefit different fields of science?

Understanding different representation transformations can benefit a wide range of scientific fields, such as physics, biology, and economics. In physics, for example, representation transformations can help to visualize and analyze complex data from experiments or simulations. In biology, they can aid in understanding the relationships between different variables in a study. In economics, representation transformations can help to interpret market trends and make informed decisions.

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