Inner products on a Hilbert space

In summary, the conversation discusses the definition of inner products on eigenfunctions of hermitian operators, as explained in chapter 3 of the Griffiths textbook. It also explores the application of this idea in finding the probability of measuring a certain spin of a spin 1/2 particle in the x direction. The difference between the integral and sum representations of the inner product is also discussed.
  • #1
Decimal
75
7
Hello,

I am taking a quantum mechanics course using the Griffiths textbook and encountering some confusion on the definition of inner products on eigenfunctions of hermitian operators. In chapter 3 the definition of inner products is explained as follows: $$ \langle f(x)| g(x) \rangle = \int f(x)^{*} g(x)\, dx $$ Should you need to express some function ##f(x)## as a linear combination of functions ## f_n(x)## then the appropriate constants ##c_n(x)## can be found using Fouriers trick: $$c_n(x) = \langle f_n(x)| f(x) \rangle$$ This I understand. In chapter 4 this idea is applied to find the probability of measuring a certain spin of spin 1/2 particle in the x direction. The spinor ##\chi## will need to be expressed in the eigenfunctions of ##\textbf{Sx}##: ##\chi_{x+}## and ##\chi_{x-}##. So to find the appropriate coefficients one can apply fouriers trick again. $$c_+ = \langle \chi_{x+}| \chi\rangle$$ However when this inner product is calculated according to Griffiths: $$c_+ = \langle \chi_{x+}| \chi\rangle = (\chi_{x+})^{\dagger} \chi$$ It seems like the integral from the original definition has disappeared? Why is this? I understand you are working with vectors here instead of scalar valued functions, but does that change the defintion of the inner product on the Hilbert space? This result looks a lot more like the standard definition of the inner product, yet the first vector is also conjugated. Can someone explain this difference to me? Thanks!
 
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  • #2
These belong to a different space. It is finite dimensional, so it is the standard inner product. And it is complex, hence the conjugate.
 
  • #3
Decimal said:
It seems like the integral from the original definition has disappeared?

There is a sum here (as is present when you write out the individual terms of the standard inner product for vectors represented in column form) instead of an integral. The sum and integral are analogous, except that one is for finite dimensional spaces and the other is for an infinite dimensional space.
 

1. What is an inner product on a Hilbert space?

An inner product on a Hilbert space is a mathematical operation that takes two vectors in the space and outputs a scalar, also known as a dot product. It is used to measure the angle between two vectors, as well as their length and direction.

2. How is an inner product different from a regular vector multiplication?

An inner product is a more general form of vector multiplication that is defined for any vector space, not just the traditional three-dimensional space. It also takes into account the direction of the vectors, not just their magnitude.

3. What are the properties of an inner product on a Hilbert space?

The properties of an inner product on a Hilbert space include linearity in the first argument, conjugate symmetry, and positive-definiteness. Linearity means that the inner product of a vector with a linear combination of other vectors is equal to the same linear combination of the inner products of the original vector with each individual vector. Conjugate symmetry means that the inner product of two vectors is equal to the complex conjugate of the inner product of the second vector with the first vector. Positive-definiteness means that the inner product of a vector with itself is always positive, except for the zero vector where it is equal to zero.

4. How is an inner product used in functional analysis?

In functional analysis, inner products are used to define a norm on a vector space. This norm, also known as the length of a vector, is used to measure the size of a vector. Inner products are also used to define the concept of orthogonality, where two vectors are perpendicular to each other and have an inner product of zero. This is useful in solving optimization problems and determining the best approximations of functions.

5. Can an inner product on a Hilbert space be extended to infinite-dimensional spaces?

Yes, an inner product on a Hilbert space can be extended to infinite-dimensional spaces, as long as the space satisfies the axioms of a Hilbert space. This allows for the use of inner products in more complex mathematical models and applications, such as quantum mechanics and signal processing.

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