Understanding Bras and Kets as Vectors and Tensors

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Discussion Overview

The discussion centers on understanding bras and kets within the framework of vectors and tensors, particularly in relation to coordinate bases. Participants explore the mathematical structure of kets and bras in Hilbert spaces, their representation as vectors, and the implications of these representations in both finite and infinite dimensions.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Some participants propose that kets can be viewed as vectors with upper indices and bras as vectors with lower indices, but the transition to a tensorial framework remains unclear.
  • It is suggested that kets are elements of a vector space, with the basis consisting of eigenvectors of Hermitian operators, though the term "coordinate basis" may not be applicable.
  • Participants discuss the nature of the dual space and the distinction between finite and infinite-dimensional Hilbert spaces, noting that the dual space is isomorphic to the primal space in finite dimensions but not in infinite dimensions.
  • One participant questions whether kets are always unit vectors and discusses the normalization of kets when multiplied by complex numbers.
  • There is a discussion about the Hermitian inner product in finite dimensions, with references to the metric tensor and the definition of inner products in real vector spaces.
  • Some participants express confusion over the notation and representation of bras and kets, with differing opinions on how to properly denote them.
  • One participant suggests that tensors can be defined as objects that transform as products of bras and kets.
  • Another participant emphasizes that the ket representation as a column vector and the bra as a row vector is not universally applicable, as kets are more general and do not assume a specific basis.

Areas of Agreement / Disagreement

Participants express a mix of agreement and disagreement on various aspects of the discussion. While there is some consensus on the basic definitions of bras and kets, significant uncertainty and differing interpretations exist regarding their representation, the implications of dual spaces, and the application of inner products.

Contextual Notes

Limitations include the potential confusion surrounding the application of coordinate bases, the distinction between finite and infinite-dimensional spaces, and the varying interpretations of the inner product in Hilbert spaces. Some mathematical steps and definitions remain unresolved or unclear.

  • #151
Phrak said:
mrandersdk-

If |00>,|01>,|10> and |11> (1=up,0=down)

are linear independent vectors, then <01|01> = 0,

rather than <01|01> = <0|0><1|1>, as you suggest.
How do you figure?
 
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  • #152
Hans de Vries said:
You may have an argument in that I implicitly assume that in R\otimes R one is a row vector and the other is a column vector, so an nx1 vector times a 1xn vector is an nxn matrix, but I wouldn't even know how to express a transpose operation at higher ranks without people loosing track of the otherwise very
simple math.
Regards, Hans

Transposition is more of a notational device, than anything, to keep track of where the rows and columns are.

In higher ranks, you can use labels to keep track rows, columns, depth..., and use a modified Einstein summation to multiply matrices.

Y = M^{T} \Rightarrow Y_{cr} = M_{rc}

(M_{abc...z} N_{abc...z})_{(fg)} = \stackrel{\Sum (M_{abc...z} N_{abc...z})}{f,g=i, i=1...n}, f
__________________________
Any mistakes I blame on LaTex
 
  • #153
Hans de Vries said:
You may have an argument in that I implicitly assume that in R\otimes R one is a row vector and the other is a column vector, so an nx1 vector times a 1xn vector is an nxn matrix, but I wouldn't even know how to express a transpose operation at higher ranks without people loosing track of the otherwise very
simple math.

Regards, Hans

Transposition is more of a notational device, than anything, to keep track of where the rows and columns are. Which elements combine with which elements between two tensors is unchange by

In higher ranks, you can use labels to keep track of rows, columns, depth...etc, and use a modified Einstein summation to multiply matrices.

Y = M^{T} \Rightarrow Y_{cr} = M_{rc}

(M_{abc...f} N_{c\: d\: e...z})_{(dp)} \equiv \sum_{d_i , p_i \ i=1...n} (M_{abc...f} N_{c\: d\: e...z})}\ , \ \ \ \ d \neq p

L_{abc_{m}e_{m}f_{m}c_{n}e_{n}f_{n}ghi...o,qrs...z} = (M_{abcef} N_{efg...z})

______________________________________________________________________
Any mistakes now, in the past, or ever, I blame on LaTex, whether I'm using it or not.
 
  • #154
Phrak said:
mrandersdk-

If |00>,|01>,|10> and |11> (1=up,0=down)

are linear independent vectors, then <01|01> = 0,

rather than <01|01> = <0|0><1|1>, as you suggest.

no, |01&gt;^\dagger = &lt;01|
 
  • #155
mrandersdk, Hurkl-

I posted:
If |00>,|01>,|10> and |11> (1=up,0=down)

are linear independent vectors, then <01|01> = 0,

rather than <01|01> = <0|0><1|1>, as you suggest.

Hurkyl said:
How do you figure?

:eek: I figure, I misread <01|01> as <01|10> :redface:

(I wouldn't mind if someone deleted my extra and partially edited post, #152.)
 

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