Kronecker sum of more than two matrices?

In summary, the conversation discusses the definition of Kronecker sums and their application in physics when considering ensembles of non-interacting systems. The total Hamiltonian of the ensemble is written as a sum over Kronecker products of unity matrix and individual Hamiltonians. The conversation also explores the associativity of Kronecker sums and provides a proof for it.
  • #1
Earthland
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0

Homework Statement


The question arises from this quote from wikipedia's article about kronecker product:

Kronecker sums appear naturally in physics when considering ensembles of non-interacting systems. Let Hi be the Hamiltonian of the i-th such system. Then the total Hamiltonian of the ensemble is
c2e0b6679eb0d88a8ec2d35a7d1a448f.png


I have to write this Htot as a ordinary sum over kronecker products of unity matrix and Hi-s.

Homework Equations



Kronecker sum for two matrices is defined as

3f1676452a4f1311f5d7a165e319b184.png


If A is n × n, B is m × m and Ik denotes the k × k identity matrix.

The Attempt at a Solution



Well, as I undesratnd, now instead of A and B we have simply Hi and there should be sum kind of sum over i. But the Kronecker sum is defined only for a pair of matrices and it isn't commutative, so the order is important. I tried something like this, for three H-s:

upload_2015-2-27_12-59-47.png


But it doesn't look very elegant and I have no idea if this could be true. Any advice?
 

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  • #2
The definition is as follows

c2e0b6679eb0d88a8ec2d35a7d1a448f.png


[tex] H_{\text{Tot}} =\left( H_1 \otimes \mathbb{I} \otimes \mathbb{I} \otimes ... \right)+ \left(\mathbb{I} \otimes H_2 \otimes \mathbb{I} \otimes ... \right) + \left(\mathbb{I} \otimes \mathbb{I} \otimes H_3 \otimes ... \right) + ...[/tex]
 
  • #3
Are you sure? Thank you!
 
  • #4
Proof:
Kronecker sum is associative.

In other words.
The Kronecker sum of two matrices is, as you wrote,
[tex] X=A\oplus B = A\otimes\mathbb{I}_B + \mathbb{I}_A\otimes{B} [/tex]

Now, since the sum ##A\oplus B## is a matrix, ##X##, the Kronecker sum
[tex] Y= X\oplus C = X\otimes\mathbb{I}_C + \mathbb{I}_X\otimes C = (A\otimes\mathbb{I}_B + \mathbb{I}_A\otimes{B})\otimes\mathbb{I}_C + \mathbb{I}_X\otimes{C} [/tex]
Of course ##\mathbb{I}_X=\mathbb{I}_A\otimes\mathbb{I}_B##, which gives
[tex] Y= A\otimes\mathbb{I}_B\otimes\mathbb{I}_C + \mathbb{I}_A\otimes B\otimes\mathbb{I}_C + \mathbb{I}_A\otimes\mathbb{I}_B\otimes{C} [/tex]

##Z= Y\oplus D = ## Keep going... :)
 
  • #5
Thank you very much!
 

FAQ: Kronecker sum of more than two matrices?

What is the Kronecker sum of more than two matrices?

The Kronecker sum of more than two matrices is a mathematical operation that combines two or more matrices together. It is also known as the direct sum or tensor sum and is denoted by ⊕.

How is the Kronecker sum of more than two matrices calculated?

The Kronecker sum of more than two matrices is calculated by taking the direct sum of each pair of matrices. For example, if we have three matrices A, B, and C, the Kronecker sum would be A⊕B⊕C.

What are the properties of the Kronecker sum of more than two matrices?

Some of the key properties of the Kronecker sum of more than two matrices include distributivity, associativity, and commutativity. It is also a bilinear operation, meaning that scalar multiplication and addition can be distributed over the direct sum.

What are the applications of the Kronecker sum of more than two matrices?

The Kronecker sum of more than two matrices has various applications in linear algebra, signal processing, and quantum mechanics. It is used to represent the tensor product of quantum states and to generate new matrices from existing ones.

Are there any limitations or challenges when working with the Kronecker sum of more than two matrices?

One of the main limitations of the Kronecker sum of more than two matrices is that it can produce very large matrices, which can be computationally expensive to work with. Additionally, the Kronecker sum is not always unique, and there can be multiple ways to represent the same matrix.

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