What is the Tensor Product and its Properties in Different States?

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In summary, a tensor is a mathematical/geometrical object with certain transformation properties under change of coordinates. The tensor product of two tensors of different ranks will result in a new tensor with an increased rank. For matrices, the tensor product is defined as multiplying each element of one matrix with the other matrix. The Pauli matrices can also be used in the tensor product, resulting in a 4x4 matrix with specific block elements depending on the given matrices.
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Hluf
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Dear All,

I need some explanations of properties of tensor and the tensor product on different states;
σ1ijσij2=_____________
Thank you.
 
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A tensor is a mathematical/geometrical object which has certain transformation properties under change of coordinates.
http://en.wikipedia.org/wiki/Tensor

You can have a tensor of rank n covariant and rank m contravariant
and a tensor of rank l covariant and rank k contravariant
Then their product will be a rank (n+l) covariant and (m+k) contravariant.
[itex] T^{i_{1}i_{2}...i_{m}}_{j_{1}j_{2}...j_{n}} T^{r_{1}r_{2}...r_{k}}_{w_{1}w_{2}...w_{l}}= R^{i_{1}i_{2}...i_{m}r_{1}r_{2}...r_{k}}_{j_{1}j_{2}...j_{n}w_{1}w_{2}...w_{l}}[/itex]

If you are talking about the tensor product for example between two matrices, the idea is almost the same...
In that case you define:
for [itex] A \in K^{p \times q} [/itex] and [itex] B \in K^{r \times s} [/itex]
[itex] A \times B \in K^{pr \times qs}[/itex]
So a matrix let's say 3x2 multiplied by tensor product with another 4x5 will give as a result a matrix 12x10.

in element notation, the product is defined as:
[itex] ( A \times B)_{(ik)(jl)}= A_{ij}B_{kl}[/itex]
or in matrix form you write for EACH element the matrix B and multiply it in the first line with A[11], A[12],...,A[1q]
the second line A[21],...A[2q] etc... (see attachment)So if what you ask for are the pauli matrices [itex]\sigma^{1,2}[/itex] then the result will be a 4x4 matrix, with upper left 2x2 block the [itex]\sigma_{11}^{1} \sigma^{2}[/itex], the upper right 2x2 block [itex]\sigma_{12}^{1} \sigma^{2}[/itex], the lower left 2x2 block [itex]\sigma_{21}^{1} \sigma^{2}[/itex], and the lower right 2x2 block [itex]\sigma_{22}^{1} \sigma^{2}[/itex].
In this case though [itex]\sigma^{1}[/itex] has zero diagonal elements, so the upper left and lower right 2x2 blocks are zero, the other off diagonal blocks are the [itex]\sigma^{2}[/itex] matrices. If i did it correctly...
 

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Related to What is the Tensor Product and its Properties in Different States?

1. What is a tensor?

A tensor is a mathematical object that represents a geometric quantity, such as a vector or a matrix, in a coordinate-independent way. It is used to describe the relationships between different quantities in a multi-dimensional space.

2. What is the difference between a tensor and a tensor product?

A tensor is a single mathematical object, while a tensor product is the result of multiplying two or more tensors together. A tensor product can also be thought of as a way to combine different tensors to create a new, more complex tensor.

3. How is a tensor represented mathematically?

Tensors are represented using indices and components. The number of indices and their positions indicate the rank of the tensor, while the components represent the magnitude of the tensor in each direction. For example, a vector can be represented as a tensor with one index, while a matrix can be represented as a tensor with two indices.

4. What are some common applications of tensors?

Tensors have a wide range of applications in various fields, including physics, engineering, and computer science. They are used to model physical phenomena, such as stress and strain in materials, as well as to perform data analysis and machine learning tasks.

5. How are tensor products useful in linear algebra?

Tensor products are useful in linear algebra because they provide a way to combine multiple linear transformations into a single transformation. This allows for more efficient and concise mathematical calculations, particularly in higher dimensions. Tensor products are also useful in understanding the relationships between different vector spaces.

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