This is the first post in a series about a problem inside RMT QIT that I have been working on for some time now [cn2,bcn]. Since I find it to be very simple and interesting, I will present it in a series of blog notes that should be accessible to a large audience. I will also use this material to prepare the talks I will be giving this summer on this topic ;).

In what follows, all vector spaces shall be assumed to be complex and are fixed constants. For a vector , the symbol denotes its ordered version, i.e. and are the same up to permutation of coordinates and .

**1. Singular values of vectors in a tensor product**

Using the non-canonical isomorphism , one can see any vector

as a matrix

In this way, by using the singular value decomposition of the matrix (keep in mind that we assume ), one can write

where , resp. are orthonormal families in , resp. . The vector is the singular value vector of and we shall always assume that it is ordered . It satisfies the normalization condition

In particular, if is a unit vector, then , where is the probability simplex

and is its ordered version.

In QIT, the decomposition of above is called the Schmidt decomposition and the numbers are called the Schmidt coefficients of the pure state .

**2. The singular value set of a vector subspace**

Consider now a subspace of dimension and define the set

called the singular value subset of the subspace .

Below are some examples of sets , in the case , where the simplex is two-dimensional. In all the four cases, and . In the last two pictures, one of the vectors spanning the subspace has singular values .

**3. Basic properties**

Below is a list of very simple properties of the sets .

**Proposition 1.** The set is a compact subset of the ordered probability simplex having the following properties:

- Local invariance: , for unitary matrices and .
- Monotonicity: if , then .
- If , , then .
- If , then .

**Proof:** The first three statements are trivial. The last one is contained in [cmw], Proposition 6 and follows from a standard result in algebraic geometry about the dimension of the intersection of projective varieties.

**4. So, what is the problem ?**

The question one would like to answer is the following:

How does a

typicallook like ?

In order to address this, I will introduce random subspaces in the ~~next post~~ future. In the next post, I look at the special case of antisymmetric tensors.

**References**

[bcn] S. Belinschi, B. Collins and I. Nechita, Laws of large numbers for eigenvectors and eigenvalues associated to random subspaces in a tensor product, to appear in Invent. Math.

[cn2] B. Collins and I. Nechita, Random quantum channels II: Entanglement of random subspaces, Rényi entropy estimates and additivity problems, Adv. in Math. 226 (2011), 1181–1201.

[cmw] T. Cubitt, A. Montanaro and A. Winter, On the dimension of subspaces with bounded Schmidt rank, J. Math. Phys. 49, 022107 (2008).

## 3 comments

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31 January 2013 at 23:30

teobanicaThat was nice, waiting forward for part two! Btw a typo – which caused me lots of troubles when first reading – when definining you forgot a key “1″ index in the definition. Actually, this ordering seems to play a key role in everything, but, is it really a truly unavoidable trick, or can you do it as well without ordering? (conceptual question coming from a former pure mathematician, of course :)

31 January 2013 at 23:53

ionnechitaThanks Teo, typo corrected. Ordering is convenient, you know the spectrum is a “set with multiplicities”, so it makes sense to order everything to keep track of things. Or, you might just add all the permutations of vectors, but think about all the black ink wasted when printing the papers!

4 February 2013 at 03:04

Random subspaces of a tensor product (II) « Ion Nechita's webpage[...] this short post, I would like to discuss a special case of the construction introduced in the first part of the series, that is compute the set , where is the antisymmetric subspace of the tensor [...]