Constructing Unitary Matrices for Rotations in Hilbert Space

In summary, in real linear space, the rotation matrix can be used with Euler angles to rotate any vector. In Hilbert space, the corresponding rotation matrix is known as a unitary operator. To construct such a matrix to rotate a complex vector in Hilbert space, one must find matrices that satisfy the condition U^{-1}=U^{\dagger}. It is not possible to rotate the real and imaginary parts separately. Unitary matrices act similarly to orthogonal matrices in real vector spaces, and can be used to transform a vector into the form (a,0,0,...,0). However, there are more general unitary transformations that are not limited to real matrices.
  • #1
KFC
488
4
In real linear space, we can use the rotation matrix in terms of Euler angle to rotate any vector in that space. I know in hilbert space, the corresponding rotation matrix is so-called unitary operator. I wonder how do I construct such matrix to rotate a complex vector in hilbert space? Can I use the real matrix (for real linear space) to rotate the real and imaginary part separately?
 
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  • #2
Are you mostly interested in the two dimensional complex vector space [itex]\mathbb{C}^2[/itex], or in general Hilbert spaces?
 
  • #3
I am interesting in 3 dimensional complex vector space. But as starting, 2D complex vector space will do.
 
  • #4
ok, anyway, you cannot rotate real and imaginary parts separately. I don't have any ready formulas available now, but you can try to solve (with some small [itex]n[/itex]) what kind of matrices [itex]U\in\mathbb{C}^{n\times n}[/itex] satisfy the condition

[tex]
\sum_{l=1}^n U^*_{lk}U_{lm} = \delta_{kl}
[/tex]

which is equivalent with [itex]U^{-1}=U^{\dagger}[/itex].
 
  • #5
KFC said:
Can I use the real matrix (for real linear space) to rotate the real and imaginary part separately?

Suppose [itex]\boldsymbol{z}\in\mathbb{C}^n[/itex] some (vertical) vector. Then [itex]\boldsymbol{z}=\boldsymbol{x} + i\boldsymbol{y}[/itex] with some [itex]\boldsymbol{x},\boldsymbol{y}\in\mathbb{R}^n[/itex]. If [itex]C\in\mathbb{C}^{n\times n}[/itex] is some complex matrix, you can write it in form [itex]C=A+iB[/itex], where [itex]A,B\in\mathbb{R}^{n\times n}[/itex] are real matrices. Then

[tex]
C\boldsymbol{z} = (A+iB)(\boldsymbol{x}+i\boldsymbol{y}) = (A\boldsymbol{x} - B\boldsymbol{y}) + i(B\boldsymbol{x} + A\boldsymbol{y}),
[/tex]

so you can reduce linear mappings [itex]\mathbb{C}^n\to\mathbb{C}^n[/itex] into linear mappings [itex]\mathbb{R}^n\to\mathbb{R}^n[/itex] like this. It is easy to see that in general acting with unitary matrices on complex vectors will not be the same as acting on the real and imaginary parts with the usual real rotations.
 
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  • #6
Think of it like this: a unitary matrix is to a complex vector space as an orthogonal matrix is to a real vector space (and, if you ever come across it, as a symplectic matrix is to a quaternionic vector space). Most of your intuition for ordinary rotations can be applied to unitary matrices acting on complex vectors. For example, it's always possible to apply a unitary transformation to a vector to get it in the form (a,0,0,...,0), for some real a>0. To see why, first try to see why the corresponding thing is true for rotations of real vectors, and see if you can adapt the argument to the complex case.

Incidentally, a matrix that is both unitary and real is just an orthogonal matrix. Since it's real, it doesn't mix up the real and imaginary parts of a vector, so if you write the vector as [itex]\vec u + i \vec v[/itex], then a real unitary matrix R takes this to [itex] (R\vec u) + i( R \vec v) [/itex], where [itex]\vec u[/itex] and [itex]\vec v[/itex] are ordinary real vectors and R is an ordinary rotation. But these aren't the most general unitary transformations, there are others whose matrix elements aren't all real. For example, the best you could do with a real unitary matrix is rotate your vector into the form (a,b,0,0,...,0) (can you see why this is true? - think about rotating u and v simultaneously with R), so clearly the complex unitary matrices are important.
 

Related to Constructing Unitary Matrices for Rotations in Hilbert Space

1. What is rotation in Hilbert space?

Rotation in Hilbert space refers to the transformation of vectors in a complex vector space, known as a Hilbert space, in which the length and angles between vectors are preserved. This transformation is achieved through the use of a unitary operator, which is a linear transformation that preserves inner products between vectors.

2. How is rotation in Hilbert space different from rotation in Euclidean space?

Rotation in Hilbert space is a more general concept compared to rotation in Euclidean space. In Euclidean space, rotation is restricted to three dimensions and is defined by the rotation of objects around an axis. In Hilbert space, rotation can occur in an infinite number of dimensions and is defined by the transformation of vectors using a unitary operator.

3. What are the applications of rotation in Hilbert space?

Rotation in Hilbert space has various applications in physics, engineering, and mathematics. It is used to study quantum mechanics, signal processing, and image recognition, among others. In quantum mechanics, rotation in Hilbert space is used to describe the properties of particles and their interactions. In signal processing and image recognition, it is used to transform signals and images to extract useful information.

4. Can any vector in a Hilbert space be rotated?

Yes, any vector in a Hilbert space can be rotated using a unitary operator. This is because unitary operators preserve the length and angles between vectors, making them suitable for rotations in Hilbert space. However, not all unitary operators are considered rotations, as some may also reflect or scale vectors.

5. How is rotation in Hilbert space related to the concept of inner product?

Rotation in Hilbert space is closely related to the concept of inner product, as unitary operators preserve inner products between vectors. This means that the inner product between a vector and its rotated version remains the same. Additionally, the angle between two vectors can be calculated using their inner product, making it a useful tool in understanding rotations in Hilbert space.

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