# Matrix in Quantum Mechanics

## Matrix:

A matrix is a rectangular array of real numbers containing m rows and n columns. An mxn matrix contains m rows and n columns and can be expressed as:

The quantities aij are called the entries or components of the matrix. The entry at the ith row and jth column of matrix A is denoted by the corresponding lowercase alphabet with subscripts.

## Square Matrix:

A square matrix is a matrix in which the number of rows is equal to the number of columns, that is m = n. An n x n matrix is known as a square matrix of order n. Square matrices have some unique properties such as symmetry and anti-symmetry. Furthermore, finding determinants and computing eigenvalues are only possible in square matrices.

## Diagonal (Triangular) Matrix:

A square matrix of order n is called a diagonal matrix if

aii = di

aij = 0 if i≠j

For example, consider the 3 x 3 matrix shown below. The elements are a11 = 8, a22 = 10, a33 = 3. All other entries above and below the diagonal line are 0s. Hence, this is a diagonal matrix.

## Symmetric Matrix:

A square matrix is symmetry if it is equal to its transpose. In other words, an n x n matrix A is symmetric if A = AT such a matrix is symmetric about its main diagonal. Some of the important properties of symmetric matrices:

1. The transpose of a symmetric matrix A is also symmetric.

2. The product of a symmetric matrix A and a scalar c that is cA is also symmetric.

3. The inverse of the transpose of a symmetric matrix A is equal to the inverse of the matrix.

(AT)’ = A’

4. If A and B are two symmetric matrices and (A+B)T = AT + BT = A + B then (A + B) is a symmetric matrix.

5. The product of two symmetric matrices is also symmetric if the two matrices commute, that is AB = BA.

## Transpose Matrix:

The transpose of m x n matrix A denoted by AT, is an n x m matrix with entries.

(AT)ij = aji

Thus, the transpose of a matrix can be derived by replacing an ith row of the matrix of A with the ith column of A. The transpose operation is –

## Orthogonal Matrix:

A square matrix is called orthogonal if the transpose of the matrix A is equal to its inverse:

AT = A-1