Is the Hermite Conjugate Needed for Expectation Values of Spin?

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SUMMARY

The discussion focuses on the calculation of expectation values for spin operators, specifically S_x, in quantum mechanics. The expectation value is expressed as \(\langle S_x \rangle = \langle \phi | S_x \phi \rangle = \phi^\dag S_x \phi\), where \(\phi\) represents the quantum state and \(\phi^\dag\) denotes the Hermite conjugate. A key point raised is the distinction between complex conjugation and Hermite conjugation, with the conclusion that Hermite conjugation is indeed necessary for accurate expectation value calculations, as it aligns with the properties of operators in quantum mechanics.

PREREQUISITES
  • Understanding of quantum mechanics principles, particularly spin operators.
  • Familiarity with linear algebra concepts, including inner products and Hermite conjugates.
  • Knowledge of operator theory in quantum mechanics.
  • Basic proficiency in mathematical notation used in quantum physics.
NEXT STEPS
  • Study the properties of Hermitian operators in quantum mechanics.
  • Learn about the role of expectation values in quantum state measurements.
  • Explore the mathematical foundations of inner products in Hilbert spaces.
  • Investigate the implications of complex conjugation versus Hermite conjugation in quantum mechanics.
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Students of quantum mechanics, physicists specializing in quantum theory, and anyone interested in the mathematical foundations of spin operators and expectation values.

Niles
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Homework Statement


Hi all.

The expectation value for S_x (spin in x-direction) is:

[tex] \left\langle {S_x } \right\rangle = \left\langle {\phi |S_x \phi } \right\rangle = \phi ^\dag S_x \phi [/tex]
where \phi is the state and \phi^"sword" is the hermite conjugate.

My question is: I thought that when finding expectation values, you are supposed to complex conjugate the left part of the inner product, not hermite conjugate?
 
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I am fairly certain that by definition,

[tex](S^T)^*=S^{\dag}[/tex]

so in a way it's the same thing (T is the transpose matrix)
 
I get it - thanks :-)
 

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