What are eigenvalues and eigenstates in quantum mechanics?

In summary, the conversation discusses the concept of eigenvalues and eigenvectors in the context of quantum mechanics. These are specific energy levels and states that are allowed for a quantum system, such as the hydrogen atom. They are determined by mathematical equations and can explain phenomena such as line spectra. Other physical quantities, such as position, momentum, and angular momentum, also have their own set of eigenstates, which contribute to the uncertainty principle.
  • #1
saba sha
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hope u all are doing good
please anybody explain in ddetail about eign value and eign levels
regards
 
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  • #2
In order to really understand what eigenvalues are, one has - of course - to study some math; more precisely linear algebra. I first encountered that in my first year at the University.

Nevertheless: I guess you are familiar with the notion of quantum systems having only a set of certain "allowed" energies. A well-known example is the hydrogen atom, whose lowest energy level is at -13.6 eV, and the next one is at exactly 1/4 that value, -3.4 eV, and so on according to the formula E=-13.6/n^2 eV, where n is any whole number. If you measure the energy of a hydrogen atom you get one of those values and none other. (This also explains the line spectra of gases.)

So in mathematical jargon, we call those energies the "eigenvalues of the Hamiltonian" (or eigenenergies). The word comes from the mathematics we use when solving the equations. I have not heard the word "eigenlevel", but that would be the state that the system is in when it has a definite energy; I would rather say "eigenstate". Think of it as the different orbits that the electron can be in.

Much of the wonders of quantum mechanics comes from the fact that all measurable physical quantities have a set of eigenstates: position, momentum, angular momentum, spin, etc. So if you measure the angular momentum of a particle you are bound to receive one out of a certain set of values, and we can calculate which ones are allowed (given a certain system, e.g. a hydrogen atom or a quantum dot or a neutron star or the Universe ;) ). And the eigenstates of all those quantities are not the same; this actually gives rise to Heisenberg's uncertainty relation.
 

FAQ: What are eigenvalues and eigenstates in quantum mechanics?

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are concepts in linear algebra that are used to describe the behavior of a linear transformation or a matrix. Eigenvalues are scalars that represent how much a vector is scaled when it is multiplied by a transformation or matrix. Eigenvectors are the corresponding vectors that remain in the same direction after the transformation or matrix multiplication.

How are eigenvalues and eigenvectors calculated?

Eigenvalues and eigenvectors can be calculated through a process called diagonalization. This involves finding the roots of the characteristic polynomial of a matrix and solving for the corresponding eigenvectors. Alternatively, they can also be calculated using numerical methods such as the power method or the QR algorithm.

What is the significance of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors have many applications in mathematics, science, and engineering. They are commonly used in data analysis, image processing, and differential equations. In physics, they are used to describe the behavior of quantum systems and in chemistry, they are used to analyze molecular orbitals.

What are the properties of eigenvalues and eigenvectors?

Some important properties of eigenvalues and eigenvectors include orthogonality, where eigenvectors corresponding to different eigenvalues are orthogonal to each other, and completeness, where a set of eigenvectors can be used to form a basis for a vector space. Eigenvectors also have the property of being linearly independent.

How do eigenvalues and eigenvectors relate to matrix diagonalization?

Eigenvalues and eigenvectors play a crucial role in matrix diagonalization. A diagonal matrix is a matrix where the only non-zero elements are on the main diagonal. Through diagonalization, a matrix can be written as a product of its eigenvectors and a diagonal matrix of its eigenvalues. This process is useful for simplifying calculations and solving systems of linear equations.

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