Can Hermitian Matrices be Traceless?

In summary, the conversation discusses the solution to a question involving Hermitian matrices that obey a specific equation. The first question asks to show that the eigenvalues of these matrices are ±1, and the second question asks to prove that the matrices are traceless. The conversation also touches on the idea of being confident in solving math problems and the possibility of a mistake in the solution.
  • #1
SgrA*
16
0
Hello,
Here's a textbook question and my solution, please check if it is correct, I'm slightly doubtful about the second part.

Consider Hermitian matrices [itex]M_1, M_2, M_3,\ and\ M_4[/itex] that obey:
[itex]M_i M_j+M_j M_i = 2 \delta_{ij} I \hspace{10mm} for\ i,\ j\ = 1,\ ... ,4 [/itex]

(1) Show that the eigenvalues of [itex]M_i[/itex] are [itex]±1[/itex].
Solution: When [itex]i = j, \hspace{20mm} M_i M_i = I[/itex].
Since [itex]M_i[/itex] are Hermitian, this equation implies that they are unitary. The eigenvalues of a unitary matrix are complex numbers of unit modulus, since it's also Hermitian, they have to be real. So, the eigenvalues have to be [itex]±1[/itex]. [itex]\hspace{20mm} Q.E.D.[/itex]

(2) Show that [itex]M_i[/itex] are traceless.
Solution: When [itex]i ≠ j, \\
M_i M_j = -M_j M_i\\
\Rightarrow M_i M_j M_i = -M_j M_i M_i\\
\Rightarrow M_i M_j M_i = -M_j I\\
\Rightarrow Tr(M_i M_j M_i) = -Tr(M_j) \\
\Rightarrow Tr(M_j M_i M_i) = -Tr(M_j)\\
\Rightarrow Tr(M_j) = -Tr(M_j)\\
\Rightarrow Tr(M_j) = 0. \hspace{20mm} Q.E.D.[/itex]

(3) Show that [itex]M_i[/itex] cannot be odd-dimensional matrices.
Solution: For some eigenbasis [itex]U, D = U^†M_iU, D[/itex] is a diagonal matrix with [itex] Tr(D) = Tr(M_i)[/itex] and the diagonal elements of [itex]D[/itex] are the eigenvalues, [itex]±1[/itex]. Also, [itex] Tr(D) = Tr(M_i) = 0. [/itex] An odd dimension cannot result in a traceless matrix, hence by argument of parity, [itex]M_i[/itex] are even dimensional matrices. [itex]\hspace{20mm}Q.E.D.[/itex]

Thanks!
 
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  • #2
Yes, I think the three proofs are fine. Nothing suspicious about (2) in particular.
 
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  • #3
Thanks for verifying, I'm still not very confident. :)
 
  • #4
I can't see why, you've spelled out every step in detail, which part gives you doubt ?
 
  • #5
I mean, I got it right, but I'm not confident that I can get math right, yet. Hopefully more problems and I'll feel confident about my solutions. Thanks for asking! :)
 
  • #6
Ah yes I feel that way about physics all the time : )
 
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  • #7
I think there is a mistake on the 2nd question. Between line 4 and 5 you comutate Mi with Mj which you can't do it. You should put a (-) in front so the final result would be 0=0 . There is another way ;) .
 
  • #8
No, it's okay - it's a cyclic permutation, just cycled the "wrong" way (or cycled twice)
 

1. What are traceless Hermitian matrices?

Traceless Hermitian matrices are square matrices where the sum of all elements in the main diagonal (also known as the trace) is equal to zero. They are also known as anti-Hermitian or skew-Hermitian matrices.

2. How are traceless Hermitian matrices different from Hermitian matrices?

Hermitian matrices have the property that they are equal to their conjugate transpose, while traceless Hermitian matrices do not have this property. Additionally, the sum of elements in the main diagonal of a Hermitian matrix is not necessarily equal to zero.

3. What is the significance of traceless Hermitian matrices?

Traceless Hermitian matrices have a variety of applications in physics and engineering, particularly in quantum mechanics and signal processing. They are also used in the study of symmetry in mathematics.

4. How are traceless Hermitian matrices used in quantum mechanics?

In quantum mechanics, traceless Hermitian matrices are used to represent observables, which are physical quantities that can be measured. The eigenvalues of these matrices correspond to the possible outcomes of measurements.

5. Can all matrices be written as a sum of a Hermitian and an anti-Hermitian matrix?

Yes, any square matrix can be written as the sum of a Hermitian and an anti-Hermitian matrix. This is known as the spectral theorem, which states that any matrix can be diagonalized and its eigenvalues can be used to construct these two types of matrices.

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