Tensor Product of Pauli Matrices

In summary, the conversation discusses the use of Pauli matrices in two different two-dimensional spaces and how to calculate the matrix elements of the tensor product of these matrices. The formula for the tensor product of two matrices is given and the question raises confusion about using this formula with matrices in different spaces. The solution suggests representing the matrices with respect to the defined basis and taking their tensor product. However, it is acknowledged that the speaker is not an expert in this topic.
  • #1
oshilinawa
1
0

Homework Statement


Suppose that [tex][\sigma_a]_{ij}[/tex] and [tex][\eta_a]_{xy}[/tex] are Pauli matrices in two different two dimensional spaces. In the four dimensional tensor product space, define the basis:
[tex]|1\rangle=|i=1\rangle|x=1\rangle[/tex]
[tex]|2\rangle=|i=1\rangle|x=2\rangle[/tex]
[tex]|3\rangle=|i=2\rangle|x=1\rangle[/tex]
[tex]|4\rangle=|i=2\rangle|x=2\rangle[/tex]
Write out the matrix elements of [tex]\sigma_2\otimes\eta_1[/tex]

Homework Equations


[tex]\sigma_a\sigma_b=\delta_{ab} + i\epsilon_{abc}\sigma_c[/tex]

The Attempt at a Solution


I know that that [tex]\sigma_2\otimes\sigma_1=\begin{bmatrix}0&0&0&-i\\0&0&-i&0\\0&i&0&0\\i&0&0&0\end{bmatrix}[/tex]
And [tex]\langle i,x| \sigma_2\otimes\eta_1|j,y\rangle = \langle i| \sigma_2|j\rangle \langle x| \eta_1|y\rangle[/tex]
I'm just confused about the matrices being in different spaces, how do I use the defined basis to calculate the matrix elements? I suspect I need the formula given as a relevant equation, but how can I use it with matrices in different spaces.
I'm doing self study with Georgi - Lie Algebras.
 
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  • #2
Here's what I'm thinking: We have two matrices A and B that represent linear transformations f and g in two spaces U and V with basis (u, u') and (v, v'), respectively. The tensor product space U otimes V will have as a basis (uv, uv', u'v, u'v') and A otimes B will be the matrix representation of f otimes g with the aforementioned basis.

So represenet sigma_2 with respect to (|i=1>, |i=2>) and then eta_1 with respect to (|x=1>, |x=2>) and take their tensor product.

I'm no expert on this though. Caveat emptor.
 

Related to Tensor Product of Pauli Matrices

1. What is the Tensor Product of Pauli Matrices?

The tensor product of Pauli matrices is a mathematical operation that combines two or more Pauli matrices to form a larger, composite matrix. It is commonly used in quantum mechanics to describe the quantum state of a multi-particle system.

2. How is the Tensor Product of Pauli Matrices calculated?

The tensor product of two matrices A and B is calculated by taking the kronecker product, denoted by A ⊗ B. This involves multiplying each element of matrix A by matrix B and arranging the resulting elements in a larger matrix. For example, the tensor product of two 2x2 Pauli matrices, σx and σy, would result in a 4x4 matrix.

3. What are the properties of the Tensor Product of Pauli Matrices?

The tensor product of Pauli matrices has several important properties. It is distributive, associative, and non-commutative. It also follows the rule (A ⊗ B)(C ⊗ D) = (AC) ⊗ (BD), where A, B, C, and D are matrices. Additionally, the tensor product of two unitary matrices is also unitary.

4. How is the Tensor Product of Pauli Matrices used in quantum computing?

The tensor product of Pauli matrices is used in quantum computing to describe the quantum state of a multi-qubit system. It is also used in quantum gates, which are operations that manipulate the quantum state of a system. The Pauli matrices, along with the identity matrix, form a basis for the space of 2x2 matrices, making them fundamental in quantum computing.

5. Are there any real-world applications of the Tensor Product of Pauli Matrices?

Yes, the tensor product of Pauli matrices has several real-world applications. It is used in quantum information processing, quantum error correction, and quantum cryptography. It also has applications in other fields such as signal processing, image analysis, and machine learning.

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