Projector in an arbitrary basis, howto compute?

In summary, the conversation discusses the difficulty in using Dirac-notation for measurement via the projection operator on a non-orthonormal basis. The attempt at a solution involves using a projector, but there is confusion about the coefficients and whether or not the projector is actually a projector. The conversation also touches on the concept of general measurements and POVMs, and the difficulty in distinguishing non-orthonormal states through projection. The person is looking for clarification and understanding on the mathematical aspects of this problem.
  • #1
keen23
9
0
Hello!
once again I got trouble with Dirac-notation:

Homework Statement


Given an non-orthonormal basis. Measurement via the projection operator should not give an definit answer, in which state the system was due to the overlap. Geometrical that's clear, but I'm unable to compute that... :(

I tried it for the following states:
[tex] |\psi_0\rangle=|0\rangle[/tex]
[tex]|\psi_1\rangle=\frac{1}{\sqrt{2}}(|0\rangle+|1\rangle)[/tex]

Homework Equations



The Attempt at a Solution


My projector looks the following way (I am not sure about the coefficients):

[tex]P=|0\rangle\langle0|+\frac{1}{2} (|0\rangle+|1\rangle)(\langle 0|+\langle 1|)[/tex]
And now I am confused. When I plug state psi1 on both sides I get a sum of several 0s and 1s I don't know how to interprete.

Any hints? Thank you!
 
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  • #2
What exactly are you trying to do?
 
  • #3
keen23 said:
My projector looks the following way (I am not sure about the coefficients):

[tex]P=|0\rangle\langle0|+\frac{1}{2} (|0\rangle+|1\rangle)(\langle 0|+\langle 1|)[/tex]

Does [itex]P^2 = P[/itex]? If it doesn't (I don't think that it does), then [itex]P[/itex] is not a projector.

Avodyne said:
What exactly are you trying to do?

I, too, would like to know the answer to this.
 
  • #4
At the moment I am reading about general measurements and POVMs.
They say, that you can't definately distinguish two non-orthonormal states by a "normal" measurement, meaning by a projection onto the states (since each has an overlap with the other).
(That's why you need the POVM to do that.)

I just would like to see the math.

Or is that the point, that I can't construct an projection-operator from my non-orthonormal states? But actually you can always project a vector onto its constructing basis vectors, no matter if they are orthogonal or not.

I hope this makes my confusion a bit clearer.
 

1. What is a projector in an arbitrary basis?

A projector in an arbitrary basis refers to a mathematical operation that projects a vector onto a lower-dimensional subspace. It is commonly used in linear algebra and quantum mechanics to simplify complex calculations.

2. What is the purpose of computing a projector in an arbitrary basis?

The purpose of computing a projector in an arbitrary basis is to reduce the dimensionality of a problem and make it easier to solve. It can also help in visualizing and understanding complex systems.

3. How do you compute a projector in an arbitrary basis?

To compute a projector in an arbitrary basis, you need to first define the basis vectors and the vector you want to project. Then, you can use the projection formula, which involves taking the inner product of the vector with each basis vector and multiplying it by the basis vector. Finally, you add up all the projections to get the final projected vector.

4. What are the key properties of a projector in an arbitrary basis?

Some key properties of a projector in an arbitrary basis include: it is idempotent (meaning that projecting a vector twice will give the same result), it is symmetric, and it preserves orthogonality (meaning that the projected vector will be orthogonal to the subspace it is projected onto).

5. How is a projector in an arbitrary basis used in real-life applications?

A projector in an arbitrary basis has various applications in fields such as computer graphics, data compression, and signal processing. It is also used in quantum computing and machine learning algorithms for dimensionality reduction and feature extraction.

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