@@ -47,7 +47,7 @@ of vector space). We can express the previous equation in terms of its component
assuming as usual some specific choice of basis, by using
the rules of matrix multiplication:
!!! tip "Eigenvalue equation: Eigenvalue and Eigenvector"
!!! info "Eigenvalue equation: Eigenvalue and Eigenvector"
$$
\sum_{j=1}^n A_{ij} v_j = \lambda v_i \, .
$$
...
...
@@ -57,7 +57,7 @@ the rules of matrix multiplication:
!!! warning "Number of solutions"
In general, there will be multiple solutions to the eigenvalue equation $A \vec{v} =\lambda \vec{v}$, each one characterised by an specific eigenvalue and eigenvectors. Note that in some cases one has *degenerate solutions*, whereby a given matrix has two or more eigenvectors that are equal.
!!! info "Characteristic equation:"
!!! tip "Characteristic equation:"
In order to determine the eigenvalues of the matrix $A$, we need to evaluate the solutions of the so-called *characteristic equation*
of the matrix $A$, defined as
$$
...
...
@@ -225,43 +225,35 @@ The set of all the eigenvalues of an operator is called *eigenvalue spectrum* of
##Problems
**1)***Eigenvalues and Eigenvectors*
1.*Eigenvalues and Eigenvectors*
Find the characteristic polynomial and eigenvalues for each of the following matrices,
Find the characteristic polynomial and eigenvalues for each of the following matrices,
**3)** Find the eigenvalues and eigenvectors of the matrices
In one of the problems of the previous section we discussed that an important operator used in quantum computation is the *Hadamard gate*, which is represented by the matrix:
has only two real eigenvalues and find and orthonormal set of three eigenvectors.
In one of the problems of the previous section we discussed that an important operator used in quantum computation is the *Hadamard gate*, which is represented by the matrix: