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Fredrik
Nov2-09, 07:44 PM
Why is the determinant of a mixed state density matrix always positive?

In the specific case of a 2-dimensional Hilbert space, the density matrix (as well as any other hermitian matrix) can be expressed as

\rho=\frac 1 2 (I+\vec r\cdot\vec \sigma)

so its determinant is

[tex]\det\rho=\frac 1 4(1-\vec r^2)[/itex]

We have |\vec r|=1 if and only if we're dealing with a pure state, so we seem to need the condition \det\rho\geq 0 to see that the set of mixed states is the interior of the sphere rather than the exterior.

Petar Mali
Nov3-09, 05:29 PM
I think that if you have mixed states statistical operator you have

{\hat{\rho}}^2 < \hat{\rho}

and of course for statistical operator Tr\hat{\rho}=1 and \hat{\rho}^{\dagger}=\hat{\rho}

I think that from that three conditions you have det\hat{\rho}>0

Fredrik
Nov3-09, 06:38 PM
Thanks, but now I have two problems instead of one. I don't see why \rho^2<\rho for mixed states. (Do you mean that all expectation values of \rho-\rho^2 are positive?) I also don't see why these results imply that \det\rho>0.

strangerep
Nov3-09, 07:14 PM
The "state operators" in the original post don't necessarily qualify as such...

Summarizing from Ballentine section 2.3 (pp 50-51)...

An acceptable state operator (a) has unit trace, (b) is self-adjoint, and
(c) satisfies \langle u | \rho | u \rangle \ge 0, for all | u \rangle.

Property (b) implies that a state operator's eigenvalues \rho_n are real.

Property (c) implies that a state operator's eigenvalues must satisfy \rho_n \ge 0.

Hence the determinant is real and non-negative, being the product of the eigenvalues.

[EDIT: Removed unnecessary silly mistake herein noted by Fredrik in post #5 below.]

Fredrik
Nov3-09, 08:31 PM
Thank you strangerep. That was surprisingly simple (at least when I only consider finite-dimensional Hilbert spaces, but that's the case I'm interested in for the moment). [Edit: Removed comment about silly mistake in #4 :smile:]

Petar Mali
Nov4-09, 03:27 AM
Just to show c)

\hat{\rho}=\sum^K_{k=1}w_k|\psi_k> <\psi_k|=\sum^K_{k=1}w_k\hat{P}_k

and


\left\langle \psi_n\left|\hat{P}_k\right|\psi_n\right\rangle=|| \hat{P}_k\psi_n||^2