In quantum information theory, a product state \(|v\rangle \in \mathbb{C}^{d_1}\otimes\mathbb{C}^{d_2}\) is a quantum state that can be written as an elementary tensor:

\(|v\rangle=|v_1\rangle\otimes|v_2\rangle\text{ with }|v_i\rangle\in\mathbb{C}^{d_i}\ \text{ for } i=1,2,\)

while states that can not be written in this form are called entangled. In this post, we will be investigating unextendible product bases (UPBs), which are sets \(S\subset\mathbb{C}^{d_1}\otimes\mathbb{C}^{d_2}\) of mutually orthogonal product states with the property that no other product state is orthogonal to every member of \(S\).

In this post, we will be looking at how to construct small UPBs. Note that UPBs can more generally be defined on multipartite spaces (i.e., \(\mathbb{C}^{d_1}\otimes\mathbb{C}^{d_2}\otimes\cdots\otimes\mathbb{C}^{d_p}\) for arbitrary \(p\geq 2\)), but for simplicity we stick with the bipartite (i.e., \(p= 2\)) case in this blog post.

Simple Examples

The most trivial unextendible product basis is simply the computational basis:

\(S:=\big\{|0\rangle\otimes|0\rangle,\ldots,|0\rangle\otimes|d_2-1\rangle,\ldots,|d_1-1\rangle\otimes|0\rangle,\ldots,|d_1-1\rangle\otimes|d_2-1\rangle\big\}.\)

However, the above UPB is rather trivial – the unextendibility condition holds vacuously because \(S\) spans the entire Hilbert space, so of course there is no product state (or any state) orthogonal to every member of \(S\).

It is known that when \(\min\{d_1,d_2\}\leq 2\), the only UPBs that exist are trivial in this sense – they consist of a full set of \(d_1d_2\) states. We are more interested in UPBs that contain fewer vectors than the dimension of the Hilbert space (since, for example, these UPBs can be used to construct bound entangled states [1]). One of the first such UPBs to be constructed was called “Pyramid” [1]. To construct this UPB, define \(h:=\tfrac{1}{2}\sqrt{1+\sqrt{5}}\) and \(N:=\tfrac{1}{2}\sqrt{5+\sqrt{5}}\), and let

\(|\phi_j\rangle:=\tfrac{1}{N}[\cos(2\pi j/5),\sin(2\pi j/5),h]\text{ for }0\leq j\leq 4.\)

Then the following set of 5 states in \(\mathbb{C}^3\otimes\mathbb{C}^3\) is a UPB:

\(S_{\textup{pyr}}:=\big\{|v_0\rangle,|v_1\rangle,|v_2\rangle,|v_3\rangle,|v_4\rangle\big\},\)

where \(|v_i\rangle:=|\phi_i\rangle\otimes|\phi_{2i(\text{mod }5)}\rangle\).

It is a straightforward calculation to verify that the members of \(S_{\textup{pyr}}\) are mutually orthogonal (and thus form a product basis). To verify that there is no product state orthogonal to every member of \(S_{\textup{pyr}}\), we first observe that any 3 of the \(|\phi_j\rangle\)’s form a linearly independent set (verification of this claim is slightly tedious, but nonetheless straightforward). Thus there is no state \(|w\rangle\in\mathbb{C}^3\) that is orthogonal to more than 2 of the \(|\phi_j\rangle\)’s. Thus no product state \(|w_1\rangle\otimes|w_2\rangle\in\mathbb{C}^3\otimes\mathbb{C}^3\) is orthogonal to more than 2 + 2 = 4 members of \(S_{\textup{pyr}}\), which verifies unextendibility.

Minimum Size

One interesting question concerning unextendible product bases asks for their minimum cardinality. It was immediately noted that any UPB in \(\mathbb{C}^{d_1}\otimes\mathbb{C}^{d_2}\) must have cardinality at least \(d_1+d_2-1\). To see this, suppose for a contradiction that there existed a UPB \(S\) containing \((d_1-1)+(d_2-1)\) or fewer product states. Then we could construct another product state that is orthogonal to \(d_1-1\) members of \(S\) on \(\mathbb{C}^{d_1}\) and another \(d_2-1\) members of \(S\) on \(\mathbb{C}^{d_2}\), for a total of \((d_1-1)+(d_2-1)\) members of \(S\), which shows that \(S\) is extendible.

Despite being such a simple lower bound, it is also attainable for many values of \(d_1,d_2\) [2] (and very close to attainable in the other cases [3,4]). The goal of this post is to focus on the case when there exists a UPB of cardinality \(d_1+d_2-1\), which is characterized by the following result of Alon and Lovász:

Theorem [2]. There exists a UPB in \(\mathbb{C}^{d_1}\otimes\mathbb{C}^{d_2}\) of (necessarily minimal) size \(d_1+d_2-1\) if and only if \(d_1,d_2\geq 3\) and at least one of \(d_1\) or \(d_2\) is odd.

In spite of the above result that demonstrates the existence of a UPB of the given minimal size in many cases, how to actually construct such a UPB in these cases is not immediately obvious, and is buried throughout the proofs of [2] and its references. The goal of the rest of this post is to make the construction of a minimal UPB in these cases explicit.

Orthogonality Graphs

The orthogonality graph of a set of \(s\) product states in \(\mathbb{C}^{d_1}\otimes\mathbb{C}^{d_2}\) is graph with coloured edges (there are 2 colours) on \(s\) vertices (one for each product state), such that there is an edge connecting two vertices with the \(i\)th colour if and only if the two corresponding product states are orthogonal on the \(i\)th party.

For example, the orthogonality graph of the Pyramid UPB introduced earlier is illustrated below. Black edges represent states that are orthogonal on the first party, and red dotted edges represent states that are orthogonal on the second party.

If the product states under consideration are mutually orthogonal, then their orthogonality graph is the complete graph \(K_s\). Unextendibility is a bit more difficult to determine, but nonetheless a useful technique for constructing UPBs is to first choose a colouring of the edges of \(K_s\), and then try to construct product states that lead to that colouring.

A Minimal Construction

In the orthogonality graph of the Pyramid UPB, all of the edges that connect a vertex to a neighbouring vertex are coloured black, and all other edges are coloured red. We can construct minimal UPBs by generalizing this graph in a natural way. Suppose without loss of generality that \(d_1\) is odd, and we wish to construct a UPB of size \(s := d_1 + d_2 – 1\). We construct the orthogonality graph by arranging \(s\) vertices in a circle and connecting any vertices that are a distance of \((d_1-1)/2\) or less from each other via a black edge. All other edges are coloured red. For example, in the \(d_1 = d_2 = 3\) case, this gives the orthogonality graph above. In the \(d_1 = 5, d_2 = 4\) case, this gives the orthogonality graph below.

Our goal now is to construct product states that have the given orthogonality graph. This is straightforward to do, since every state must be orthogonal to \(d_1-1\) of the other states on \(\mathbb{C}^{d_1}\) and orthogonal to the \(d_2-1\) other states on \(\mathbb{C}^{d_2}\). Thus, we can just pick \(|v_0\rangle\) arbitrarily, then pick \(|v_1\rangle\) randomly subject to the constraint that it is orthogonal to \(|v_0\rangle\) on the first subsystem, and so on, working our way clockwise around the orthogonality graph, generating each product state randomly subject to the orthogonality conditions.

Furthermore, it can be shown (but will not be shown here – the techniques are similar to those of [4] and are a bit technical) that this procedure leads to a product basis that is in fact unextendible with probability 1. In order to verify unextendibility explicitly, one approach is to check that any subset of \(d_1\) of the product states are linearly independent on \(\mathbb{C}^{d_1}\) and any subset of \(d_2\) of the product states are linearly independent on \(\mathbb{C}^{d_2}\).

References

1. C. H. Bennett, D. P. DiVincenzo, T. Mor, P. W. Shor, J. A. Smolin, and B. M. Terhal. Unextendible product bases and bound entanglement. Phys. Rev. Lett., 82:5385–5388, 1999. E-print: arXiv:quant-ph/9808030
2. N. Alon and L. Lovász. Unextendible product bases. J. Combinatorial Theory, Ser. A, 95:169–179, 2001.
3. K. Feng. Unextendible product bases and 1-factorization of complete graphs. Discrete Appl. Math., 154:942–949, 2006.
4. J. Chen and N. Johnston. The minimum size of unextendible product bases in the bipartite case (and some multipartite cases). Comm. Math. Phys., 333(1):351–365, 2015. E-print: arXiv:1301.1406 [quant-ph]

Let \(\mathcal{H}\) be a finite-dimensional Hilbert space over \(\mathbb{R}\) or \(\mathbb{C}\) (the fields of real and complex numbers, respectively). If we let \(\|\cdot\|\) be a norm on \(\mathcal{H}\) (not necessarily the norm induced by the inner product), then the dual norm of \(\|\cdot\|\) is defined by

\(\displaystyle\|\mathbf{v}\|^\circ := \sup_{\mathbf{w} \in \mathcal{H}}\Big\{ \big| \langle \mathbf{v}, \mathbf{w} \rangle \big| : \|\mathbf{w}\| \leq 1 \Big\}.\)

The double-dual of a norm is equal to itself (i.e., \(\|\cdot\|^{\circ\circ} = \|\cdot\|\)) and the norm induced by the inner product is the unique norm that is its own dual. Similarly, if \(\|\cdot\|_p\) is the vector p-norm, then \(\|\cdot\|_p^\circ = \|\cdot\|_q\), where \(q\) satisfies \(1/p + 1/q = 1\).

In this post, we will demonstrate that \(\|\cdot\|^\circ\) has an equivalent characterization as an infimum, and we use this characterization to provide a simple derivation of several known (but perhaps not well-known) formulas for norms such as the operator norm of matrices.

For certain norms (such as the “separability norms” presented at the end of this post), this ability to write a norm as both an infimum and a supremum is useful because computation of the norm may be difficult. However, having these two different characterizations of a norm allows us to bound it both from above and from below.

The Dual Norm as an Infimum

Theorem 1. Let \(S \subseteq \mathcal{H}\) be a bounded set satisfying \({\rm span}(S) = \mathcal{H}\) and define a norm \(\|\cdot\|\) by

\(\displaystyle\|\mathbf{v}\| := \sup_{\mathbf{w} \in S}\Big\{ \big| \langle \mathbf{v}, \mathbf{w} \rangle \big| \Big\}.\)

Then \(\|\cdot\|^\circ\) is given by

\(\displaystyle\|\mathbf{v}\|^\circ = \inf\Big\{ \sum_i |c_i| : \mathbf{v} = \sum_i c_i \mathbf{v}_i, \mathbf{v}_i \in S \ \forall \, i \Big\},\)

where the infimum is taken over all such decompositions of \(\mathbf{v}\).

Before proving the result, we make two observations. Firstly, the quantity \(\|\cdot\|\) described by Theorem 1 really is a norm: boundedness of \(S\) ensures that the supremum is finite, and \({\rm span}(S) = \mathcal{H}\) ensures that \(\|\mathbf{v}\| = 0 \implies \mathbf{v} = 0\). Secondly, every norm on \(\mathcal{H}\) can be written in this way: we can always choose \(S\) to be the unit ball of the dual norm \(\|\cdot\|^\circ\). However, there are times when other choices of \(S\) are more useful or enlightening (as we will see in the examples).

Proof of Theorem 1. Begin by noting that if \(\mathbf{w} \in S\) and \(\|\mathbf{v}\| \leq 1\) then \(\big| \langle \mathbf{v}, \mathbf{w} \rangle \big| \leq 1\). It follows that \(\|\mathbf{w}\|^{\circ} \leq 1\) whenever \(\mathbf{w} \in S\). In fact, we now show that \(\|\cdot\|^\circ\) is the largest norm on \(\mathcal{H}\) with this property. To this end, let \(\|\cdot\|_\prime\) be another norm satisfying \(\|\mathbf{w}\|_{\prime}^{\circ} \leq 1\) whenever \(\mathbf{w} \in S\). Then

\(\displaystyle \| \mathbf{v} \| = \sup_{\mathbf{w} \in S} \Big\{ \big| \langle \mathbf{w}, \mathbf{v} \rangle \big| \Big\} \leq \sup_{\mathbf{w}} \Big\{ \big| \langle \mathbf{w}, \mathbf{v} \rangle \big| : \|\mathbf{w}\|_{\prime}^{\circ} \leq 1 \Big\} = \|\mathbf{v}\|_\prime.\)

Thus  \(\| \cdot \| \leq \| \cdot \|_\prime\), so by taking duals we see that \(\| \cdot \|^\circ \geq \| \cdot \|_\prime^\circ\), as desired.

For the remainder of the proof, we denote the infimum in the statement of the theorem by \(\|\cdot\|_{{\rm inf}}\). Our goal now is to show that: (1) \(\|\cdot\|_{{\rm inf}}\) is a norm, (2) \(\|\cdot\|_{{\rm inf}}\) satisfies \(\|\mathbf{w}\|_{{\rm inf}} \leq 1\) whenever \(\mathbf{w} \in S\), and (3) \(\|\cdot\|_{{\rm inf}}\) is the largest norm satisfying property (2). The fact that \(\|\cdot\|_{{\rm inf}} = \|\cdot\|^\circ\) will then follow from the first paragraph of this proof.

To see (1) (i.e., to prove that \(\|\cdot\|_{{\rm inf}}\) is a norm), we only prove the triangle inequality, since positive homogeneity and the fact that \(\|\mathbf{v}\|_{{\rm inf}} = 0\) if and only if \(\mathbf{v} = 0\) are both straightforward (try them yourself!). Fix \(\varepsilon > 0\) and let \(\mathbf{v} = \sum_i c_i \mathbf{v}_i\), \(\mathbf{w} = \sum_i d_i \mathbf{w}_i\) be decompositions of \(\mathbf{v}, \mathbf{w}\) with \(\mathbf{v}_i, \mathbf{w}_i \in S\) for all i, satisfying \(\sum_i |c_i| \leq \|\mathbf{v}\|_{{\rm inf}} + \varepsilon\) and \(\sum_i |d_i| \leq \|\mathbf{w}\|_{{\rm inf}} + \varepsilon\). Then

\(\displaystyle \|\mathbf{v} + \mathbf{w}\|_{{\rm inf}} \leq \sum_i |c_i| + \sum_i |d_i| \leq \|\mathbf{v}\|_{{\rm inf}} + \|\mathbf{w}\|_{{\rm inf}} + 2\varepsilon.\)

Since \(\varepsilon > 0\) was arbitrary, the triangle inequality follows, so \(\|\cdot\|_{{\rm inf}}\) is a norm.

To see (2) (i.e., to prove that \(\|\mathbf{v}\|_{{\rm inf}} \leq 1\) whenever \(\mathbf{v} \in S\)), we simply write \(\mathbf{v}\) in its trivial decomposition \(\mathbf{v} = \mathbf{v}\), which gives the single coefficient \(c_1 = 1\), so \(\|\mathbf{v}\|_{{\rm inf}} \leq \sum_i c_i = c_1 = 1\).

To see (3) (i.e., to prove that \(\|\cdot\|_{{\rm inf}}\) is the largest norm on \(\mathcal{H}\) satisfying condition (2)), begin by letting \(\|\cdot\|_\prime\) be any norm on \(\mathcal{H}\) with the property that \(\|\mathbf{v}\|_{\prime} \leq 1\) for all \(\mathbf{v} \in S\). Then using the triangle inequality for \(\|\cdot\|_\prime\) shows that if \(\mathbf{v} = \sum_i c_i \mathbf{v}_i\) is any decomposition of \(\mathbf{v}\) with \(\mathbf{v}_i \in S\) for all i, then

\(\displaystyle\|\mathbf{v}\|_\prime = \Big\|\sum_i c_i \mathbf{v}_i\Big\|_\prime \leq \sum_i |c_i| \|\mathbf{v}_i\|_\prime = \sum_i |c_i|.\)

Taking the infimum over all such decompositions of \(\mathbf{v}\) shows that \(\|\mathbf{v}\|_\prime \leq \|\mathbf{v}\|_{{\rm inf}}\), which completes the proof.

The remainder of this post is devoted to investigating what Theorem 1 says about certain specific norms.

Injective and Projective Cross Norms

If we let \(\mathcal{H} = \mathcal{H}_1 \otimes \mathcal{H}_2\), where \(\mathcal{H}_1\) and \(\mathcal{H}_2\) are themselves finite-dimensional Hilbert spaces, then one often considers the injective and projective cross norms on \(\mathcal{H}\), defined respectively as follows:

\(\displaystyle \|\mathbf{v}\|_{I} := \sup\Big\{ \big| \langle \mathbf{v}, \mathbf{a} \otimes \mathbf{b} \rangle \big| : \|\mathbf{a}\| = \|\mathbf{b}\| = 1 \Big\} \text{ and}\)

\(\displaystyle \|\mathbf{v}\|_{P} := \inf\Big\{ \sum_i \| \mathbf{a}_i \| \| \mathbf{b}_i \| : \mathbf{v} = \sum_i \mathbf{a}_i \otimes \mathbf{b}_i \Big\},\)

where \(\|\cdot\|\) here refers to the norm induced by the inner product on \(\mathcal{H}_1\) or \(\mathcal{H}_2\). The fact that \(\|\cdot\|_{I}\) and \(\|\cdot\|_{P}\) are duals of each other is simply Theorem 1 in the case when S is the set of product vectors:

\(\displaystyle S = \big\{ \mathbf{a} \otimes \mathbf{b} : \|\mathbf{a}\| = \|\mathbf{b}\| = 1 \big\}.\)

In fact, the typical proof that the injective and projective cross norms are duals of each other is very similar to the proof of Theorem 1 provided above (see [1, Chapter 1]).

Maximum and Taxicab Norms

Use \(n\) to denote the dimension of \(\mathcal{H}\) and let \(\{\mathbf{e}_i\}_{i=1}^n\) be an orthonormal basis of \(\mathcal{H}\). If we let \(S = \{\mathbf{e}_i\}_{i=1}^n\) then the norm \(\|\cdot\|\) in the statement of Theorem 1 is the maximum norm (i.e., the p = ∞ norm):

\(\displaystyle\|\mathbf{v}\|_\infty = \sup_i\Big\{\big|\langle \mathbf{v}, \mathbf{e}_i \rangle \big| \Big\} = \max \big\{ |v_1|,\ldots,|v_n|\big\},\)

where \(v_i = \langle \mathbf{v}, \mathbf{e}_i \rangle\) is the i-th coordinate of \(\mathbf{v}\) in the basis \(\{\mathbf{e}_i\}_{i=1}^n\). The theorem then says that the dual of the maximum norm is

\(\displaystyle \|\mathbf{v}\|_\infty^\circ = \inf \Big\{ \sum_i |c_i| : \mathbf{v} = \sum_i c_i \mathbf{e}_i \Big\} = \sum_{i=1}^n |v_i|,\)

which is the taxicab norm (i.e., the p = 1 norm), as we expect.

Operator and Trace Norm of Matrices

If we let \(\mathcal{H} = M_n\), the space of \(n \times n\) complex matrices with the Hilbert–Schmidt inner product

\(\displaystyle \big\langle A, B \big\rangle := {\rm Tr}(AB^*),\)

then it is well-known that the operator norm and the trace norm are dual to each other:

\(\displaystyle \big\| A \big\|_{op} := \sup_{\mathbf{v}}\Big\{ \big\|A\mathbf{v}\big\| : \|\mathbf{v}\| = 1 \Big\} \text{ and}\)

\(\displaystyle \big\| A \big\|_{op}^\circ = \big\|A\big\|_{tr} := \sup_{U}\Big\{ \big| {\rm Tr}(AU) \big| : U \in M_n \text{ is unitary} \Big\},\)

where \(\|\cdot\|\) is the Euclidean norm on \(\mathbb{C}^n\). If we let \(S\) be the set of unitary matrices in \(M_n\), then Theorem 1 provides the following alternate characterization of the operator norm:

Corollary 1. Let \(A \in M_n\). Then

\(\displaystyle \big\|A\big\|_{op} = \inf\Big\{ \sum_i |c_i| : A = \sum_i c_i U_i \text{ and each } U_i \text{ is unitary} \Big\}.\)

As an application of Corollary 1, we are able to provide the following characterization of unitarily-invariant norms (i.e., norms \(\|\cdot\|_{\prime}\) with the property that \(\big\|UAV\big\|_{\prime} = \big\|A\big\|_{\prime}\) for all unitary matrices \(U, V \in M_n\)):

Corollary 2. Let \(\|\cdot\|_\prime\) be a norm on \(M_n\). Then \(\|\cdot\|_\prime\) is unitarily-invariant if and only if

\(\displaystyle \big\|ABC\big\|_\prime \leq \big\|A\big\|_{op}\big\|B\big\|_{\prime}\big\|C\big\|_{op}\)

for all \(A, B, C \in M_n\).

Proof of Corollary 2. The “if” direction is straightforward: if we let \(A\) and \(C\) be unitary, then

\(\displaystyle \big\|B\big\|_\prime = \big\|A^*ABCC^*\big\|_\prime \leq \big\|ABC\big\|_\prime \leq \big\|B\big\|_{\prime},\)

where we used the fact that \(\big\|A\big\|_{op} = \big\|C\big\|_{op} = 1\). It follows that \(\big\|ABC\big\|_\prime = \big\|B\big\|_\prime\), so \(\|\cdot\|_\prime\) is unitarily-invariant.

To see the “only if” direction, write \(A = \sum_i c_i U_i\) and \(C = \sum_i d_i V_i\) with each \(U_i\) and \(V_i\) unitary. Then

\(\displaystyle \big\|ABC\big\|_\prime = \Big\|\sum_{i,j}c_i d_j U_i B V_j\Big\|_\prime \leq \sum_{i,j} |c_i| |d_j| \big\|U_i B V_j\big\|_\prime = \sum_{i,j} |c_i| |d_j| \big\|B\big\|_\prime.\)

By taking the infimum over all decompositions of \(A\) and \(C\) of the given form and using Corollary 1, the result follows.

An alternate proof of Corollary 2, making use of some results on singular values, can be found in [2, Proposition IV.2.4].

Separability Norms

As our final (and least well-known) example, let \(\mathcal{H} = M_m \otimes M_n\), again with the usual Hilbert–Schmidt inner product. If we let

\(\displaystyle S = \{ \mathbf{a}\mathbf{b}^* \otimes \mathbf{c}\mathbf{d}^* : \|\mathbf{a}\| = \|\mathbf{b}\| = \|\mathbf{c}\| = \|\mathbf{d}\| = 1 \},\)

where \(\|\cdot\|\) is the Euclidean norm on \(\mathbb{C}^m\) or \(\mathbb{C}^n\), then Theorem 1 tells us that the following two norms are dual to each other:

\(\displaystyle \big\|A\big\|_s := \sup\Big\{ \big| (\mathbf{a}^* \otimes \mathbf{c}^*)A(\mathbf{b} \otimes \mathbf{d}) \big| : \|\mathbf{a}\| = \|\mathbf{b}\| = \|\mathbf{c}\| = \|\mathbf{d}\| = 1 \Big\} \text{ and}\)

\(\displaystyle \big\|A\big\|_s^\circ = \inf\Big\{ \sum_i \big\|A_i\big\|_{tr}\big\|B_i\big\|_{tr} : A = \sum_i A_i \otimes B_i \Big\}.\)

There’s actually a little bit of work to be done to show that \(\|\cdot\|_s^\circ\) has the given form, but it’s only a couple lines – consider it an exercise for the interested reader.

Both of these norms come up frequently when dealing with quantum entanglement. The norm \(\|\cdot\|_s^\circ\) was the subject of [3], where it was shown that a quantum state \(\rho\) is entangled if and only if \(\|\rho\|_s^\circ > 1\) (I use the above duality relationship to provide an alternate proof of this fact in [4, Theorem 6.1.5]). On the other hand, the norm \(\|\cdot\|_s\) characterizes positive linear maps of matrices and was the subject of [5, 6].

References

1. J. Diestel, J. H. Fourie, and J. Swart. The Metric Theory of Tensor Products: Grothendieck’s Résumé Revisited. American Mathematical Society, 2008. Chapter 1: pdf
2. R. Bhatia. Matrix Analysis. Springer, 1997.
3. O. Rudolph. A separability criterion for density operators. J. Phys. A: Math. Gen., 33:3951–3955, 2000. E-print: arXiv:quant-ph/0002026
4. N. Johnston. Norms and Cones in the Theory of Quantum Entanglement. PhD thesis, University of Guelph, 2012.
5. N. Johnston and D. W. Kribs. A Family of Norms With Applications in Quantum Information Theory. Journal of Mathematical Physics, 51:082202, 2010.
6. N. Johnston and D. W. Kribs. A Family of Norms With Applications in Quantum Information Theory II. Quantum Information & Computation, 11(1 & 2):104–123, 2011.

Update [March 3, 2015]: I have released a MATLAB package called QETLAB that can compute the completely bounded and diamond norms (and much more) much faster than the code contained in this blog post. I recommend using QETLAB instead of the code below.

In operator theory, the completely bounded norm of a linear map on complex matrices \(\Phi : M_m \rightarrow M_n\) is defined by \(\|\Phi\|_{cb} := \sup_{k \geq 1} \| id_k \otimes \Phi \|\), where \(\|\Phi\|\) is the usual norm on linear maps defined by \(\|\Phi\| := \sup_{X \in M_m} \{ \|\Phi(X)\| : \|X\| \leq 1\}\) and \(\|X\|\) is the operator norm of \(X\) [1]. The completely bounded norm is particularly useful when thinking of \(M_m\) and \(M_n\) as operator spaces.

The dual of the completely bounded norm is called the diamond norm, which plays an important role in quantum information theory, as it can be used to measure the distance between quantum channels. The diamond norm of \(\Phi\) is typically denoted \(\|\Phi\|_{\diamond}\). For properties of the completely bounded and diamond norms, see [1,2,3].

A method for efficiently computing the completely bounded and diamond norms via semidefinite programming was recently presented in [4]. The purpose of this post is to provide MATLAB scripts that implement this algorithm and demonstrate its usage.

Download and Install

In order to make use of these scripts to compute the completely bounded or diamond norm, you must download and install two things: the SeDuMi semidefinite program solver and the MATLAB scripts themselves.

1. SeDuMi – Please follow the instructions on the SeDuMi website to download and install it. If possible, you should install SeDuMi 1.1R3, not SeDuMi 1.21 or SeDuMi 1.3, since there is a bug with the newer versions when dealing with complex matrices.
2. CB Norm MATLAB Package – Once SeDuMi is installed, download the CB norm MATLAB scripts, unzip them, and place them in your MATLAB scripts directory. The zip file contains 10 MATLAB scripts.

Usage Examples

The representation of the linear map \(\Phi\) that the CBNorm and DiamondNorm functions take as input is a pair of arrays of its left- and right- generalized Choi-Kraus operators. That is, an array of operators \(\{A_i\}\) and \(\{B_i\}\) such that \(\Phi(X) = \sum_i A_i X B_i\) for all \(X\).

Basic Examples

If we wanted to compute the completely bounded and diamond norms of the map

the MATLAB input and output would be as follows:

>> PhiA(:,:,1) = [1,1;1,0];
>> PhiA(:,:,2) = [1,0;1,2];
>> PhiB(:,:,1) = [1,0;0,1];
>> PhiB(:,:,2) = [1,2;1,1];
>> CBNorm(PhiA,PhiB)

ans =

    7.2684

>> DiamondNorm(PhiA,PhiB)

ans =

    7.4124

So we see that its completely bounded norm is 7.2684 and its diamond norm is 7.4124.

If we instead want to compute the completely bounded or diamond norm of a completely positive map, we only need to provide its Kraus operators – i.e., operators \(\{A_i\}\) such that \(\Phi(X) = \sum_i A_i X A_i^\dagger\) for all \(X\). Furthermore, in this case semidefinite programming isn’t used at all, since [1, Proposition 3.6] tells us that \(\|\Phi\|_{cb} = \|\Phi(I)\|\) and \(\|\Phi\|_{\diamond} = \|\Phi^\dagger(I)\|\), and computing \(\|\Phi(I)\|\) is trivial. The following example demonstrates the usage of these scripts in this case, via a completely positive map \(\Phi : M_3 \rightarrow M_2\) with four (essentially random) Kraus operators:

>> PhiA(:,:,1) = [1 0 0;0 1 1];
>> PhiA(:,:,2) = [-3 0 1;5 1 1];
>> PhiA(:,:,3) = [0 2 0;0 0 0];
>> PhiA(:,:,4) = [1 1 3;0 2 0];
>> CBNorm(PhiA)

ans =

   42.0000

>> DiamondNorm(PhiA)

ans =

   38.7303

Transpose Map

Suppose we want to compute the completely bounded or diamond norm of the transpose map on \(M_n\). A generalized Choi-Kraus representation is given by defining \(A_{ij} = B_{ij} = e_i e_j^\dagger\), where \(\{e_i\}\) is the standard basis of \(\mathbb{C}^n\) (i.e., \(A_{ij}\) and \(B_{ij}\) are the operators with matrix representation in the standard basis with a one in the \((i,j)\)-entry and zeroes elsewhere). It is known that the completely bounded and diamond norms of the n-dimensional transpose map are both equal to n, which can be verified in small dimensions as follows:

>> % 2-dimensional transpose
>> PhiA(:,:,1) = [1 0;0 0];
>> PhiA(:,:,2) = [0 1;0 0];
>> PhiA(:,:,3) = [0 0;1 0];
>> PhiA(:,:,4) = [0 0;0 1];
>> PhiB = PhiA;
>> CBNorm(PhiA,PhiB)

ans =

    2.0000

>> DiamondNorm(PhiA,PhiB)

ans =

    2.0000

>> % 3-dimensional transpose
>> I = eye(3);
>> for i=1:3
for j=1:3
PhiA(:,:,3*(i-1)+j) = I(:,i)*I(j,:);
end
end
>> PhiB = PhiA;
>> CBNorm(PhiA,PhiB)

ans =

    3.0000

>> DiamondNorm(PhiA,PhiB)

ans =

    3.0000

>> PhiA(:,:,1) = [1 0;0 1];
>> PhiA(:,:,2) = [1 1;-1 1]/sqrt(2);
>> PhiB(:,:,1) = [1 0;0 1];
>> PhiB(:,:,2) = -[1 -1;1 1]/sqrt(2);
>> CBNorm(PhiA,PhiB)

ans =

    1.4142

>> DiamondNorm(PhiA,PhiB)

ans =

    1.4142

References

1. V. I. Paulsen. Completely bounded maps and operator algebras. Cambridge University Press, 2003.
2. N. Johnston, D. W. Kribs, and V. I. Paulsen. Computing stabilized norms for quantum operations via the theory of completely bounded maps. Quantum Inf. Comput., 9:16-35, 2009.
3. J. Watrous. Theory of quantum information lecture notes.
4. J. Watrous. Semidefinite programs for completely bounded norms. Theory Comput., 5:217–238, 2009.

One of the key concepts in quantum information theory is the difference between separable states and entangled states. A pure quantum state (that is, a unit vector) v ∈ Cⁿ ⊗ Cⁿ is said to be separable if it can be written as v = a ⊗ b for some a,b ∈ Cⁿ; otherwise v is called entangled. In this post we will investigate what operators preserve the set of separable pure states, as well as what operators entangle all separable pure states.

Separable Pure State Preservers and Entangling Gates

In the design of quantum algorithms, entangling gates play a very important role. Entangling gates are unitary operators that are able to generate entanglement. A bit more specifically, a unitary operator U ∈ M_n ⊗ M_n (where M_n is the space of n × n complex matrices) is called an entangling gate if there exists a separable pure state v = a ⊗ b ∈ Cⁿ ⊗ Cⁿ such that Uv is entangled. Conversely, we will say that a unitary operator U preserves separability if Uv is separable whenever v is separable.

In order to answer the question of what unitaries preserve separability, it is instructive to consider some simple examples (this is often a useful way to formulate conjectures regarding preserver problems). For example, it is clear that if U = A ⊗ B for some unitary operators A, B ∈ M_n, then U preserves separability (because U(a ⊗ b) = Aa ⊗ Bb is separable). Another example of a unitary operator that preserves separability is the swap (or flip) operator S defined on separable states by S(a ⊗ b) = b ⊗ a (the action of S on the rest of Cⁿ ⊗ Cⁿ is determined by extending linearly). It turns out that these are essentially the only operators that preserve separability [1,2,3]:

Theorem 1. Let U ∈ M_n ⊗ M_n be a unitary operator. Then U preserves separability (i.e., U is not an entangling gate) if and only if there exist unitary operators A, B ∈ M_n such that either U = A ⊗ B or U = S(A ⊗ B).

As we already saw, the “if” direction of the above result is trivial – the meat and potatoes of the theorem comes from the “only if” direction (as is typically the case with results about linear preservers). Theorem 1 was first proved in [1] essentially by case analysis and checking the action of a separability-preserving unitary on a basis of Cⁿ ⊗ Cⁿ, and was subsequently re-proved using similar techniques (but with different motivations and connections) in [2]. The result was proved in [3] by using the vector-operator isomorphism and the fact that a linear map Φ : M_n → M_n preserves the set of rank-1 operators if and only if there exist A, B ∈ M_n such that either Φ(X) ≡ AXB or Φ(X) ≡ AX^tB [4].

Theorem 1 also follows as a simple corollary of several related results that have recently been proved in [5,6]. A version of Theorem 1 for multipartite systems (i.e., systems that are the tensor product of more than two copies of Cⁿ) can be found in [3] and [7].

Universal Entangling Gates

A universal entangling gate is, as its name suggests, a stronger form of an entangling gate – it is a unitary operator U such that U(a ⊗ b) is entangled for all a, b ∈ Cⁿ (contrast this with entangling gates, which require only that U(a ⊗ b) is entangled for some a, b ∈ Cⁿ). The structure of universal entangling gates is much less well-understood than that of entangling gates, though we can still at least say when they exist.

It is not difficult to convince yourself that universal entangling gates can’t exist in small dimensions. Let’s begin by supposing n = 2. The set of pure states in C² ⊗ C² can be regarded as a 7-dimensional real manifold (7 = 2 × (n × n) – 1, where we subtract one because pure states all have unit length), while the set of separable pure states in C² ⊗ C² can be regarded as a 5-dimensional real manifold (5 = (2 × n – 1) + (2 × n – 1) – 1, where the final one is subtracted because the overall phase of the first system relative to the second system is irrelevant). Thus, if U ∈ M₂ ⊗ M₂ were a universal entangler, it would have to send a 5-dimensional manifold into the 7 – 5 = 2 remaining dimensions of the space, which seems unlikely. Similarly, if n = 3 and U ∈ M₃ ⊗ M₃ were a universal entangler, it would have to send a 9-dimensional manifold into the 17 – 9 = 8 remaining dimensions of the space, which also seems unlikely.

Indeed, this type of argument was made rigorous via methods of algebraic geometry in [8], where the following result was proved:

Theorem 2. There exists a universal entangling gate in M_n ⊗ M_n if and only if n ≥ 4.

Despite knowing when universal entangling gates exist, we still don’t have a characterization of such operators, nor do we even have many explicit examples (does anyone have an explicit example for 3 ⊗ 4 or 4 ⊗ 4 systems?). Similar techniques to those used in the proof of Theorem 2 should also shed light on when universal entangling gates exist in multipartite systems M_n1 ⊗ M_n2 ⊗ … ⊗ M_nk, but to my knowledge this calculation has not been explicitly carried out.

References:

1. M. Marcus and B. N. Moyls, Transformations on tensor product spaces. Pacific Journal of Mathematics 9, 1215–1221 (1959).
2. F. Hulpke, U. V. Poulsen, A. Sanpera, A. Sen De, U. Sen, and M. Lewenstein, Unitarity as preservation of entropy and entanglement in quantum systems. Foundations of Physics 36, 477–499 (2006). E-print: arXiv:quant-ph/0407118
3. N. Johnston, Characterizing Operations Preserving Separability Measures via Linear Preserver Problems. To appear in Linear and Multilinear Algebra (2011). E-print: arXiv:1008.3633 [quant-ph]
4. L. Beasley, Linear operators on matrices: the invariance of rank k matrices. Linear Algebra and its Applications 107, 161–167 (1988).
5. E. Alfsen and F. Shultz, Unique decompositions, faces, and automorphisms of separable states. Journal of Mathematical Physics 51, 052201 (2010). E-print: arXiv:0906.1761 [math.OA]
6. S. Friedland, C.-K. Li, Y.-T. Poon, and N.-S. Sze, The automorphism group of separable states in quantum information theory. Journal of Mathematical Physics 52, 042203 (2011). E-print: arXiv:1012.4221 [quant-ph]
7. R. Westwick, Transformations on tensor spaces. Pacific Journal of Mathematics 23, 613–620 (1967).
8. J. Chen, R. Duan, Z. Ji, M. Ying, J. Yu, Existence of Universal Entangler. Journal of Mathematical Physics 49, 012103 (2008). E-print: arXiv:0704.1473 [quant-ph]

A few months ago, I spent two posts describing the Choi-Jamiolkowski isomorphism between linear operators from M_n to M_m (often referred to as “superoperators“) and linear operators living in the space M_n ⊗ M_m. However, there is another isomorphism between superoperators and regular operators — one that I’m not sure of any name for but which has just as many interesting properties.

Recall from Section 1 of this post that any superoperator Φ can be written as

for some operators {A_i} and {B_i}. The isomorphism that I am going to focus on in this post is the one given by associating Φ with the operator

The main reason that M_Φ can be so useful is that it retains the operator structure of Φ. In particular, if you define vec(X) to be the vectorization of the operator X, then

In other words, if you treat X as a vector, then M_Φ is the operator describing the action of Φ on X. From this it becomes simple to compute some basic quantities describing Φ. For example, the induced Frobenius norm,

is equal to the standard operator norm of M_Φ. If n = m then we can define the eigenvalues {λ} and the eigenmatrices {V} of Φ in the obvious way via

Then the eigenvalues of Φ are exactly the eigenvalues of M_Φ, and the corresponding eigenvectors of M_Φ are the vectorizations of the eigenmatrices of Φ. It is similarly easy to check whether Φ is invertible (by checking whether or not det(M_Φ) = 0), find the inverse if it exists, or find the nullspace (and a pseudoinverse) if it doesn’t.

Finally, here’s a question for the interested reader to think about: why is the transpose required on the B_i operators for this isomorphism to make sense? That is, why can we not define an isomorphism between Φ and the operator

Recently, a family of vector norms [1,2] have been introduced in quantum information theory that are useful for helping classify entanglement of quantum states. In particular, the Schmidt vector k-norm of a vector v ∈ Cⁿ ⊗ Cⁿ, for an integer 1 ≤ k ≤ n, is defined by

In the above definition, SR(w) refers to the Schmidt rank of the vector w and so these norms are in some ways like a measure of entanglement for pure state vectors. One of the results of [2] shows how to compute these norms efficiently, so with that in mind we can perform all sorts of fun numerical analysis on them. Analytic results are provided in the paper, so I’ll provide more hand-wavey stuff and pictures here. In particular, let’s look at what the distributions of the Schmidt vector norms look like.

Figure 1: The distribution of the Schmidt 1 and 2 vector norms in (3 ⊗ 3)-dimensional space

Figure 1 shows the distributions of the Schmidt 1 and 2 norms of unit vectors distributed according to the Haar measure in C³ ⊗ C³, based on 5×10⁵ vectors generated randomly via MATLAB. Note that the Schmidt 3-norm just equals the standard Euclidean norm so it always equals 1 and is thus not shown. Figures 2 and 3 show similar distributions in C⁴ ⊗ C⁴ and C⁵ ⊗ C⁵.

Figure 2: The distribution of the Schmidt 1, 2, and 3 vector norms in (4 ⊗ 4)-dimensional space

Figure 3: The distribution of the Schmidt 1, 2, 3, and 4 vector norms in (5 ⊗ 5)-dimensional space

The following table shows various basic statistics about the above distributions. I suppose the natural next step is to ask whether or not we can analytically determine the distribution of the Schmidt vector norms. Since these norms are essentially just the singular values of an operator that is associated with the vector, it seems like this might even already be a (partially) solved problem, since many results are known about the distribution of the singular values of random matrices. The difficulty comes in trying to interpret the Haar measure (or any other natural measure on pure states, such as the Hilbert-Schmidt measure) on the associated operators.

References:

1. D. Chruscinski, A. Kossakowski, G. Sarbicki, Spectral conditions for entanglement witnesses vs. bound entanglement, Phys. Rev A 80, 042314 (2009). arXiv:0908.1846v2 [quant-ph]
2. N. Johnston and D. W. Kribs, A Family of Norms With Applications in Quantum Information Theory. Journal of Mathematical Physics 51, 082202 (2010). arXiv:0909.3907 [quant-ph]