Calculating Eigenkets from Matrix w/ Orthonormal Basis

In summary, an eigenket is a vector or state that represents the unique solution to a linear transformation, associated with a particular eigenvalue. To calculate eigenkets from a matrix with an orthonormal basis, one must find the eigenvectors of the matrix and normalize them. An orthonormal basis is important for this process, as it simplifies and ensures the resulting eigenkets are also orthonormal. Eigenkets can only be calculated for square matrices that are diagonalizable. Applications of calculating eigenkets include quantum mechanics, computer graphics, and data analysis.
  • #1
mkbh_10
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How am i supposed to write eigenkets of an operator whose matrix is given to me given that the two ket vectors form an orthonormal basis .
 
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  • #2
Eigenkets is just the "quantum mechanical" name for eigenvectors. So you find the eigenvectors, and then make them orthonormal (e.g. apply the Gram-Schmidt orthonormalisation process)
 
  • #3


To calculate the eigenkets of an operator given its matrix, you can use the following steps:

1. Determine the eigenvalues of the matrix by finding the roots of the characteristic equation det(A-λI)=0, where A is the matrix and λ is the eigenvalue.

2. For each eigenvalue, solve the equation (A-λI)|ψ>=0 to find the corresponding eigenvector |ψ>.

3. Normalize the eigenvectors by dividing each component by the square root of the sum of their squares, to ensure that they form an orthonormal basis.

4. The resulting normalized eigenvectors will be the eigenkets of the operator.

In summary, you can use the given orthonormal basis to find the eigenkets by first finding the eigenvalues and then solving for the corresponding eigenvectors. The normalization step ensures that the eigenvectors form an orthonormal basis, which is essential for performing further calculations and analyses.
 

1. What is an eigenket?

An eigenket is a vector or state that represents the unique solution to a linear transformation, also known as an "eigenstate". It is associated with a particular eigenvalue, which is a scalar that represents the amount of stretching or shrinking that occurs during the transformation.

2. How do you calculate eigenkets from a matrix with an orthonormal basis?

The process of calculating eigenkets from a matrix with an orthonormal basis involves finding the eigenvectors of the matrix, which are the vectors that do not change direction during the transformation. These eigenvectors are then normalized to form the eigenkets. This can be done using various methods, such as the power method or the diagonalization method.

3. Why is an orthonormal basis important for calculating eigenkets?

An orthonormal basis is important for calculating eigenkets because it simplifies the process and ensures that the resulting eigenkets are also orthonormal. This means that the eigenkets are perpendicular to each other and have a length of 1, making them easier to work with and interpret.

4. Can eigenkets be calculated for any matrix?

No, eigenkets can only be calculated for square matrices. Additionally, the matrix must be diagonalizable, which means it can be reduced to a diagonal matrix using a similarity transformation. If these conditions are not met, then eigenkets cannot be calculated.

5. What are some applications of calculating eigenkets from a matrix?

Calculating eigenkets can be useful in various fields, such as quantum mechanics, computer graphics, and data analysis. In quantum mechanics, eigenkets represent the possible states of a quantum system, while in computer graphics, they can be used to transform and manipulate images. In data analysis, eigenkets can help identify patterns and relationships in large datasets.

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