Fubini-Study metric of pure states

In summary, you are trying to figure out how to derive the Fubini-Study metric from the arccosine formula. However, you are not getting anywhere because you are not thinking in terms of vectors and angles. You should try and find the angle between 2 unnormalized vectors and then use that angle to derive the Fubini-Study metric.
  • #1
Alex Cros
28
1
Hello PF!

I was reading
https://en.wikipedia.org/wiki/Fubini–Study_metric (qm section like always :wink:)
And can't figure out how to derive:
[tex]\gamma (\psi , \phi) = arccos \sqrt{\frac{<\psi|\phi><\phi|\psi>}{<\psi|\psi><\phi|\phi>}}[/tex]
I started with
[tex]\gamma (\psi , \phi) =|| |\psi> - |\phi>||= \sqrt{(<\psi|-<\phi|)(|\psi>-|\phi>)}=...[/tex]
as it is the distance between the two vectors (no?) but don't seem to get anywhere, help!
NB: I assumed there the vectors are normalized + apologies for the dirac notation format.

Many thanks in advance!
 
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  • #2
Alex Cros said:
I was reading
https://en.wikipedia.org/wiki/Fubini–Study_metric (qm section like always :wink:)
And can't figure out how to derive:
[tex]\gamma (\psi , \phi) = arccos \sqrt{\frac{<\psi|\phi><\phi|\psi>}{<\psi|\psi><\phi|\phi>}}[/tex]
I started with
[tex]\gamma (\psi , \phi) =|| |\psi> - |\phi>||= \sqrt{(<\psi|-<\phi|)(|\psi>-|\phi>)}=...[/tex]
as it is the distance between the two vectors (no?)
That's not what you want. Try thinking in terms of finding the angle between 2 unnormalized vectors. E.g., in a simpler real vector space, what is the dot product of 2 vectors ##u \cdot v## ? Then generalize to a complex vector space.

NB: I assumed there the vectors are normalized
If they're normalized then the denominator of your 1st expression is 1.
 
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Likes Alex Cros
  • #3
strangerep said:
That's not what you want. Try thinking in terms of finding the angle between 2 unnormalized vectors. E.g., in a simpler real vector space, what is the dot product of 2 vectors ##u \cdot v## ? Then generalize to a complex vector space.
I'm confused by the use of: "length between those two points".

If they're normalized then the denominator of your 1st expression is 1.
But why then is it called the distance between those two points?
I think I know what you are saying: take the inner product and solve for the angle, then you get the arccos, but in the article I sent it was referred as the length. Moreover the ultimate aim of doing this is to derive the ds element by [itex]|\psi> →|\psi + d \psi> [/itex] and finding the length between them two.
I'm confused by the: "the length between two points"
 
  • #4
Alex Cros said:
But why then is it called the distance between those two points?
Because angles between vectors in a vector space satisfy the properties of being a metric.
(You should indeed try and prove this assertion. I'll help you if you get stuck.)

I think I know what you are saying: take the inner product and solve for the angle, then you get the arccos, but in the article I sent it was referred as the length. Moreover the ultimate aim of doing this is to derive the ds element by [itex]|\psi> →|\psi + d \psi> [/itex] and finding the length between them two. I'm confused by the: "the length between two points"
It's just an abuse of terminology. The crucial thing is that it defines a metric. When 2 state vectors are maximally "close" to each other it means the angle between them is 0. When 2 state vectors are maximally different from each other, that corresponds to them being orthogonal. I.e., the angle between them is 90deg.

The ##ds## variant is just a special case of the main formula for when the state vectors are only infinitesimally different from each other, hence the angle between them is very small. I find it a bit misleading to call it ##ds## when it's really a small angle, but this usage is quite common.
 
  • #5
strangerep said:
Because angles between vectors in a vector space satisfy the properties of being a metric.
(You should indeed try and prove this assertion. I'll help you if you get stuck.)

It's just an abuse of terminology. The crucial thing is that it defines a metric. When 2 state vectors are maximally "close" to each other it means the angle between them is 0. When 2 state vectors are maximally different from each other, that corresponds to them being orthogonal. I.e., the angle between them is 90deg.

The ##ds## variant is just a special case of the main formula for when the state vectors are only infinitesimally different from each other, hence the angle between them is very small. I find it a bit misleading to call it ##ds## when it's really a small angle, but this usage is quite common.
Okay I see now what you mean now, yeah I agree it's quite confusing the notation. Either way I would be very grateful if you could give me a hand with this derivation by quicking off with the first steps please!
Thanks beforehand!
 
  • #6
Alex Cros said:
Okay I see now what you mean now, yeah I agree it's quite confusing the notation. Either way I would be very grateful if you could give me a hand with this derivation by quicking off with the first steps please!
Thanks beforehand!
Okay,I think I just proved that (even though its not Fubini-Study metric) [itex] || |\psi>-|\phi> || [/itex] is a metric aswell, as it satisfies [itex] d(x,y)≥0 , d(x,y)=0 ↔ x=y, d(x,y)=d(y,x) & d(x,z)...[/itex]etc. is this the case? or have I made a mistake?
 
  • #7
Alex Cros said:
Okay,I think I just proved that (even though its not Fubini-Study metric) [itex] || |\psi>-|\phi> || [/itex] is a metric aswell, as it satisfies [itex] d(x,y)≥0 , d(x,y)=0 ↔ x=y, d(x,y)=d(y,x) & d(x,z)...[/itex]etc. is this the case? or have I made a mistake?
The final bit just involves the triangle inequality, applied to norms on a vector space.
 
  • #8
Alex Cros said:
Either way I would be very grateful if you could give me a hand with this derivation by quicking off with the first steps please!
Heh, well,... actually, I'll let you "quick off" the first steps, and then I'll help if you get stuck.

Hint: start with the case of a real vector space ([wherein you'll need to know about the (spherical) law of cosines]. When you've done the real case, you can generalize the law of cosines to a complex vector space.
 
  • #9
strangerep said:
Heh, well,... actually, I'll let you "quick off" the first steps, and then I'll help if you get stuck.

Hint: start with the case of a real vector space ([wherein you'll need to know about the (spherical) law of cosines]. When you've done the real case, you can generalize the law of cosines to a complex vector space.
Okay, I first let you know my original attempt to comment on my procedure:
Let [tex] dist(\psi,\phi) = || \psi - \phi || = \sqrt{ < \phi, \phi> + < \psi, \psi> - 2 || < \psi, \phi> ||} [/tex] which is a metric.
now [tex] dl = dist(\psi+d\psi,\phi) = \sqrt{ < \phi, \phi> + < \psi + d\psi, \psi + d\psi> - 2 || < \psi +d\psi, \phi> ||} [/tex]
expanding [tex] dl = \sqrt{ \phi * \phi + \psi * \psi + \psi * d\psi + d\psi* \psi +d\psi*d\psi* - 2||\psi * \phi +d\psi * \phi|| } [/tex]
where [itex]*[/itex] denotes complex conjugate on the left
 
  • #10
Alex Cros said:
Let [tex] dist(\psi,\phi) = || \psi - \phi || = \sqrt{ < \phi, \phi> + < \psi, \psi> - 2 || < \psi, \phi> ||} [/tex] which is a metric. [...]
Are you sure about that last term under the square root? Are you sure it's not ##2 Re\langle\phi,\psi\rangle## ?

In any case, I'm reasonably sure that's not the right point to start from if you want an FS metric.

Do you want to derive the finite form of the FS metric (the one in the Wiki page with an arccos, i.e., ##\gamma(\psi,\phi)=\arccos \sqrt{\cdots}##), or do you just want to start with that finite form and derive the corresponding infinitesimal version ##ds^2 = \cdots## ?
 

1. What is the Fubini-Study metric of pure states?

The Fubini-Study metric of pure states is a mathematical concept used in quantum mechanics to measure the distance between two pure quantum states. It takes into account the complex nature of quantum states and allows for a more accurate comparison between them.

2. How is the Fubini-Study metric calculated?

The Fubini-Study metric is calculated using the Fubini-Study distance formula, which takes into account the inner product of two pure states. This formula involves complex numbers and can be written in terms of matrices.

3. What is the significance of the Fubini-Study metric in quantum mechanics?

The Fubini-Study metric is significant in quantum mechanics because it allows for a more precise measurement of the distance between two quantum states. This is important in understanding the behavior and evolution of quantum systems.

4. Can the Fubini-Study metric be used for mixed states?

No, the Fubini-Study metric is only applicable to pure states. For mixed states, other distance measures, such as the Bures metric, are used.

5. How does the Fubini-Study metric relate to the concept of entanglement?

The Fubini-Study metric can be used to measure the entanglement between two quantum states. It provides a way to quantify the amount of entanglement between two systems and is an important tool in studying entanglement in quantum systems.

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