Tag Archives: non-commutative distributions

A Story of the Determinant of Operator-Valued Semicircular Elements

Operator-valued semicircular elements are the powerhouse of free probability and random matrix theory. In particular, the intrinsic freeness principle typically replaces random matrices by operator-valued semicircular elements and shows that, in many respects, the former are close to the latter. Thus it is important to understand as much as possible about operator-valued semicirculars.

An operator-valued semicircular element – say over B=Mm()B=M_m(\mathbb C) – is of the form

S=a1+i=1naisiwitha,a1,,anMmsa(𝐂),S=a\otimes 1+\sum_{i=1}^n a_i\otimes s_i\qquad\text{with} \qquad a,a_1,\dots,a_n\in M_m^{sa}(\mathbf C),

where s1,,sn s_1,\dots,s_n are free semicirculars. We denote by trm{\rm tr}_m the normalized matrix trace on BB, and by τ\tau the trace on the algebra, where the sis_i live. All information about such an element SS is contained in its mean

(idτ)[S]=a ({\rm id} \otimes \tau)[S]=a

and its covariance

η(b)=:(idτ)[(Sa)b(Sa)]=i=1naibai.\eta(b)=:({\rm id}\otimes \tau)[(S-a)b(S-a)]=\sum_{i=1}^n a_i b a_i.

The covariance η\eta is a completely positive map from BB to BB.

We also know how, in principle, to calculate the distribution μS\mu_S of SS. Its scalar-valued Cauchy transform is

gS(z)=(trmτ)[(z1S)1].g_S(z)=(\tr_m\otimes \tau)[(z1-S)^{-1}].

This can be extracted from the operator-valued Cauchy transform

GS(b)=(idτ)[(bS)1]G_S(b)=({\rm id}\otimes\tau)[(b-S)^{-1}]

simply by

gS(z)=trm[GS(z1)].g_S(z)=\tr_m[G_S(z1)].

And GSG_S is determined by the nice quadratic matrix-valued equation, usually called the Dyson equation,

(ba)GS(b)=1+η(GS(b))GS(b). (b-a)\cdot G_S(b)=1+\eta(G_S(b))\cdot G_S(b).

So everything is determined by mean and covariance. Of course, some quantities are easier, others harder to extract in a meaningful way. For moments of SS, for example, one has the operator-valued free version of the Wick/Isserlis formula. The operator norm of SS, on the other hand, is not so easily accessible. In principle, this is given by the asymptotics of high moments, but that is not very concrete. So the following celebrated formula of Franz Lehner

λmax(a1+i=1naisi)=infbB,b>0λmax(b+a+η(b1))\lambda_{\max} \left(a\otimes 1+\sum_{i=1}^n a_i\otimes s_i\right) = \inf_{b\in B, b \gt 0}\lambda_{\max} \left(b+a+\eta(b^{-1})\right)

comes as a nice surprise, as it reduces the upper spectral edge of the infinite-dimensional SS to a variational problem for the same quantity on the finite-dimensional matrix algebra BB. Here λmax\lambda_{\max} denotes the upper edge of the spectrum; for positive aa, Lehner’s original formulation gives the corresponding operator norm.

A few years ago together with Tobias Mai I was trying to understand the determinant (more precisely, the Fuglede–Kadison determinant) of an operator-valued semicircular element, in order to get some info about the accumulation of mass of the distribution of such a semicircular element close to zero. And we succeeded in finding a nice formula in terms of the covariance; more precisely the determinant was essentially given by the capacity of the covariance map. Capacity here is the quantity introduced by Gurvits in connection with operator scaling; it should not be confused with the various notions of channel capacity in quantum information. Unwinding this notion of capacity one can write our result concretely as

Δ(i=1naisi)=e1/2infbB,b>0Δ(bη(b1))1/2.\Delta(\sum_{i=1}^n a_i\otimes s_i) = e^{-1/2}\cdot \inf_{b\in B, b>0} \Delta\left(b\cdot \eta(b^{-1})\right)^{1/2}.

Δ\Delta is here the Fuglede–Kadison determinant, which reduces in the finite-dimensional situation to

Δ(b)=det(|b|)1/mforbMm().\Delta(b)=\det (\vert b\vert)^{1/m}\qquad\text{for}\qquad b\in M_m(\mathbb C).

As you see, our result is only for mean a=0a=0. But in this case it has a striking similarity with Lehner’s formula:

i=1naisi=infbB,b>0b+η(b1),\| \sum_{i=1}^n a_i\otimes s_i\| = \inf_{b\in B, b>0} \left\|b+\eta(b^{-1})\right\|,

And of course it raises the question: Can we extend our formula also to the case of general mean. We thought a little bit about this way back then, but did not see how to do this.

Now, in the times of AI hype, it is tempting to come back to this question and ask ChatGPT what it has to say on this. And actually, it has something to say. It proposes the following generalization

Δ(a1+i=1naisi)=e1/2infb>0, 𝒞bη0Δ(b)exp{12a,(𝒞b+η)1(a)2},\Delta\left(a\otimes 1+\sum_{i=1}^n a_i\otimes s_i\right) = e^{-1/2} \inf_{b \gt 0,\ \mathcal C_b-\eta\succeq 0} \Delta(b)\, \exp\left\{ \frac12 \left\langle a, (\mathcal C_b+\eta)^{-1}(a) \right\rangle_2 \right\},

where

𝒞b(x)=bxb,andx,y2=trm(xy)forx,yMm()\mathcal C_b(x)=bxb,\qquad\text{and}\qquad\langle x,y\rangle_2 = \operatorname{tr}_m(x^*y)\qquad \text{for} \qquad x,y\in M_m(\mathbb C)

is the Hilbert-Schmidt inner product.

The condition 𝒞bη0\mathcal C_b-\eta\succeq 0 means positivity as an operator on the Hilbert–Schmidt space Mm()M_m(\mathbb C), i.e.

x,𝒞b(x)2x,η(x)2,or equivalentlytrm(xbxb)trm(xη(x)),for all xMm().\left\langle x,\mathcal C_b(x)\right\rangle_2 \geq \left\langle x,\eta(x)\right\rangle_2,\qquad\text{or equivalently}\qquad \operatorname{tr}_m(x^*bxb) \geq \operatorname{tr}_m\left(x^*\eta(x)\right), \qquad\text{for all }x\in M_m(\mathbb C).

ChatGPT also produces what it claims is a proof of all this — note that even the reduction of this to my formula with Tobias in the case of a=0a=0 is non-trivial — and makes some remarks on the relevance for the Brown measure of an operator-valued circular element. Recall that the Brown measure of an operator TT is encoded by the logarithmic potential

zlogΔ(Tz1).z\mapsto \log \Delta (T-z1).

So understanding determinants of shifted operators is exactly what one needs here.

All this looks somehow reasonable, but not very enlightening; and even if I had a Lean certificate for all this (which I don’t), I would still not be too satisfied.

So let’s talk about all this and hope that, together, we can get a better understanding of what is really going on.

Doing Mathematics with AI — but How?

AI is revolutionizing mathematics, and it will not go away. So somehow we have to arrange ourselves with it and find our way of doing mathematics under these changed conditions.

I am of course also fascinated by asking AI (ChatGPT in my case) to solve all my problems. And sometimes there are indeed answers which seem to be okay and which promise some real progress. But those answers are usually complicated and quite technical, and I don’t really feel like becoming a little helper and argument checker for AI.

I agree with many voices saying that asking the meaningful questions, and understanding and presenting the answers, will remain some of the main tasks for us. But this should happen in a way that we still feel good about going along with it. For me this means that I would like to take from AI mainly some ideas, but then think myself about whether they could be true, what they actually mean, how one could prove them, and what is the best way to present them and convince others.

Of course, like everybody else, I don’t have a final answer to how this should work in practice, or whether this is a route which will help mathematics to survive in anything resembling its present form.

Anyhow, I suppose we just have to try different routes. Here is one experiment I would like to make.

At the moment I am thinking about determinants of operator-valued semicircular elements. Together with Tobias Mai, we derived a few years ago a formula for the determinant of such an element S in terms of its covariance map. This was, however, for the case of vanishing mean of S, and I wondered whether there could be a nice and useful extension to the case of general mean.

Okay, so I asked ChatGPT. And it did what two years ago would have seemed totally out of range, but has now become almost ordinary: it gave me an answer, proved it, and also made some comments on possible implications for questions about the Brown measure of operator-valued circular elements.

It even wrote a manuscript about all this.

I could now just put my name on it, say that the ideas were developed in conversations with AI, and declare that I take full responsibility for the results. But this does not feel right to me. And, more importantly, it does not feel very satisfying.

So here is what I would rather like to do.

I will give some background, explain what ChatGPT proposes as the answer, and then open the problem for public discussion: Is the statement correct? Is it perhaps obvious? How is it related to things that somebody already knows? What is the right proof? And what other interesting questions or conjectures might come out of it?

My dream would be that the answers — and also the questions — are actually self-thought, and not just copies of something another AI conversation produced. Of course everybody could now ask AI about the problem and perhaps produce a paper on it. But this should not be about publishing or priority. It should be about understanding.

Maybe one could think of it as telling a mathematical story together, in a somewhat grassroots way: somebody starts with a question, somebody else recognizes a connection, another person finds an argument, somebody points out that the whole thing is wrong, or suggests the right formulation, and gradually we understand what is really going on. Of course, we can use AI in the background for information, references, or inspiration. But AI should not tell the story.

I have no idea whether this will work.

But let us try.

In my next post I will give the concrete mathematical problem and the beginning of the story.

A dual and a conjugate system for the q-Gaussians, for all q

Update: On Monday, April 4, I will give an online talk on those results at the UC Berkeley Probabilistic Operator Algebra Seminar.

I have just uploaded the joint paper A dual and conjugate system for q-Gaussians for all q with Akihiro Miyagawa to the arXiv. There we report some new results concerning the q-Gaussian operators and von Neumann algebras. The interesting issue is that we can prove quite a few properties in a uniform way for all q in the open interval -1<q<1.

The canonical commutation and anti-commutation relations are fundamental relations describing bosons and fermions, respectively. In 1991, Marek Bożejko and I considered an interpolation between those bosonic and fermionic relations, depending on a parameter q with -1\le q \le 1 (where q=1 corresponds to the bosonic case and q=-1 to the fermionic case): a_ia_j^*-q a_j^* a_i=\delta_{ij} 1. These relations can be represented by creation and annihilation operators on a q-deformed Fock space. (Showing that the q-deformed inner product which makes a_i and a_i^* adjoints of each other is indeed an inner product, i.e. positive, was one of the main results in my paper with Marek.) In the paper with Akihiro we consider only the case where the number d of indices is finite.

Since then studying the q-Gaussians A_i=a_i+a_i^* has attracted quite some interest. Especially, the q-Gaussian von Neumann algebras, i.e., the von Neumann algebras generated by the A_i, have been studied for many years. One of the basic questions is whether and how those algebras depend on q. The extreme cases q=1 (bosonic) and q=-1 (fermionic) are easy to understand and they are in any case different from the other q in the open interval -1<q<1. The central case q=0 is generated by free semicircular elements and free probability tools give then easily that this case is isomorphic to the free group factor.

So the main question is whether the q-Gaussian algebras are, for -1<q<1, isomorphic to the free group factor. Over the years it has been shown that these algebras share many properties with the free group factors. For instance, for all -1<q<1 the q-Gaussian algebras are II1-factors, non-injective, prime, and have strong solidity. A partial answer to the isomorphism problem was achieved in the breakthrough paper by Guionnet and Shlyakhtenko, who proved that the q-Gaussian algebras are isomorphic to the free group factors for small |q| (where the size of the interval depends on d and goes to zero for d\to\infty). However, it is still open whether this is true for all -1<q<1.

In our new paper, we compute a dual system and from this also a conjugate system for q-Gaussians. These notions were introduced by Voiculescu in the context of free entropy and have turned out to carry important information about distributional properties of the considered operators and to have many implications for the generated von Neumann algebras.

Our approach starts from finding a concrete formula for dual systems; those are operators whose commutators with q-Gaussians are exactly the orthogonal projection onto the vacuum vector. If we also normalize such dual operators by requiring that they vanish on the vacuum vector, then the commutator relation gives a recursion, which can be solved in terms of a precise combinatorial formula involving partitions and their number of crossings, where the latter has, however, to be counted in a specific, and different from the usual, way. The main work consists then in showing that the dual operators given in this way have indeed the vacuum vector in the domain of their adjoints. The action of the adjoints of the dual operators on the vacuum gives then, by general results going back to Voiculescu and Shlyakhtenko, the conjugate variables.

One should note that whereas the action of the dual operators on elements in the m-particle space is given by finite sums, going over to the adjoint results, even for their action on the vacuum, necessarily in non-finite sums, i.e., power series expansions. Thus it is crucial to control the convergence of such series in order to get the existence of the conjugate variables. There have been results before on the existence of conjugate variables for the q-Gaussians, by Dabrowski, but those relied on power series expansions which involved coefficients of the form q^m for elements in the m-particle space and thus guaranteed convergence only for small q. In contrast, the precise combinatorial formulas in our work lead to power series expansions which involve coefficients of the form q^{m(m-1)/2}. This quadratic form of the exponent is in the end responsible for the fact that our power series expansions converge for all q in the interval (-1,1).

The existence of conjugate systems for all q with -1<q<1 has then, by previous general results, many consequences for all such q (some of them had been known only for the restricted interval of q, some of them for all q, by other methods). We can actually improve on the existence of the conjugate system and show that it satisfies a stronger condition, known as Lipschitz property. This implies then, by general results of Dabrowski, the maximality of the micro-states free entropy dimension of the q-Gaussian operators in the whole interval (-1,1).

Unfortunately, we are not able to use our results for adding anything to the isomorphism problem. However, the fact that the free entropy dimension is maximal for all q in the whole interval is another strong indication that they might all be isomorphic to the free group factor.

Lecture Notes on Non-Commutative Distributions

Waiting has come to an end … finally the pdf edition of the Lecture Notes on Non-Commutative Distributions has arrived. As a bonus for loyal followers I have added, compared to the actual content of the lecture series, two small sections at the end on what our machinery has to say about Connes embedding problem and the q-Gaussian distribution. Though, don’t expect too much there …

The saga ends …

I have now finished my class on random matrices. The last lecture motivated the notion of (asymptotic) freeness from the point of view of looking on independent GUE random matrices. So you might think that there should now be continuations on free probability and alike coming soon. But actually this part of the story was already written and recorded and if you don’t want to spoil the tension you should watch the series not in its historical but in its logical order:

  1. Random Matrices (videos, homepage of class)
  2. Free Probability Theory (videos, homepage of class)
  3. Non-commutative Distributions (and Operator-Valued free Probability Theory) (videos, homepage of class)

More information, in particular the underlying script (sometimes in a handwritten version, sometimes in a more polished texed version), can be found on the corresponding home page of the lecture series.

May freeness be with you …