# Pauli matrices forming a basis for 2x2 operators

• McLaren Rulez
In summary, Fredrik has discovered a different way to show that the four matrices in a basis for 2x2 matrices are linearly independent. This proof uses a matrix inner product that is equal to 1 if and only if the two vectors are adjacent.
McLaren Rulez
Hi,

We know that the Pauli matrices along with the identity form a basis of 2x2 matrices. Any 2x2 matrix can be expressed as a linear combination of these four matrices. I know of one proof where I take

$a_{0}\sigma_{0}+a_{1}\sigma_{1}+a_{2}\sigma_{2}+a_{3}\sigma_{3}=0$

Here, $\sigma_{0}$ is the identity. We get four simultaneous equations in $a_{i}$ and it is fairly trivial to show that each $a_{i}$ must be zero. This implies that the four matrices are linearly independent and therefore form a basis for 2x2 matrices.

But I recently discovered a different way to show this. This utilizes a matrix inner product defined by $\sigma_{i}.\sigma_{j} = \frac{1}{2}Tr(\sigma_i \sigma_{j}^{\bot})$. Here $A^{\bot}$ is the transpose conjugate and the 1/2 is just to remove the factor of 2 that arises from the matrices being 2x2. The argument goes that this definition gives the inner product equal to 1 if i=j and 0 otherwise meaning that the four matrices we have are orthogonal. Since there are four of them, and the orthogonality is interpreted as linear independence, they form a basis.

My question is, how can I connect these two proofs? The second one seems nice but I cannot see how it is the same, in general, as the first proof. The definition of orthogonality seems arbitrary (it obeys the axioms for inner product, yes, but surely there is more than that) and I cannot really see how showing that matrices that are orthogonal by this definition makes them linearly independent in the way the first proof shows. Thank you very much for your help.

Let $\{x_k\}$ be an orthonormal set in a finite-dimensional vector space V. Suppose that $\sum_k a_k x_k=0$. Then for all i, $$0=\langle x_i,0\rangle=\langle x_i,\sum_k a_k x_k\rangle=\sum_k a_k\langle x_i,x_k\rangle=\sum_k\delta_{ik}=a_i.$$ This means that the set is linearly independent.

It's always easier to use the fact that we're working with an inner product than to use the definition of the specific inner product you've been given.

That's a very nice proof. Thank you Fredrik!

## 1. What are Pauli matrices?

Pauli matrices are a set of 2x2 complex matrices, named after physicist Wolfgang Pauli. They are expressed as σx, σy, and σz, and are used to represent physical quantities such as spin, angular momentum, and energy in quantum mechanics.

## 2. How do Pauli matrices form a basis for 2x2 operators?

Pauli matrices form a basis for 2x2 operators because they are linearly independent and span the space of all 2x2 matrices. This means that any 2x2 matrix can be expressed as a linear combination of the Pauli matrices.

## 3. What is the significance of Pauli matrices forming a basis for 2x2 operators?

The significance of Pauli matrices forming a basis for 2x2 operators is that they provide a powerful tool for analyzing and manipulating quantum systems. They allow physicists to easily represent and manipulate physical quantities, making calculations and predictions about quantum systems much simpler.

## 4. How are Pauli matrices related to the Pauli exclusion principle?

Pauli matrices are not directly related to the Pauli exclusion principle. However, they are named after Wolfgang Pauli, who first proposed the exclusion principle. The matrices were later discovered by Pauli and others to have important applications in quantum mechanics.

## 5. Can Pauli matrices be extended to higher dimensions?

Yes, Pauli matrices can be extended to higher dimensions. In addition to the 2x2 matrices, there are also 4x4 matrices known as Gell-Mann matrices, which are used to represent spin and other physical quantities in quantum mechanics. However, the Pauli matrices themselves only exist in 2x2 dimensions.

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