Is there an impact of a negative solution to Connes' embedding problem on free probability?

There is an exciting new development on Connes’ embedding problem. The recent preprint MIP*=RE by Ji, Natarajan, Vidick, Wright, Yuen claims to have solved the problem to the negative via a negative answer to Tsirelson’s problem via the relation to decision problems on the class MIP* of languages that can be decided by a classical verifier interacting with multiple all powerful quantum provers. I have to say that I don’t really understand what all this is about – but in any case there is quite some excitement about this and there seems to be a good chance that Connes’ problem might have a negative solution. To get some idea about the excitement around this, you might have look on the blogs of Scott Aaronson or of Gil Kalai. At the operator front I have not yet seen much discussion, but it might be that we still have to get over our bafflement.

Anyhow, there is now a realistic chance that there are type II factors which are not embeddable and this raises the question (among many others) what this means for free probability. I was asked this by a couple of people and as I did not have a really satisfying answer I want to think a bit more seriously about this. At the moment my answer is just: Okay, we have our two different approaches to free entropy and a negative solution to Connes embedding problem means that they cannot always agree. This is because we always have for the non-microstates free entropy \chi^* that \chi^*(x_1+\sqrt\epsilon s_n,\dots,x_n+\sqrt\epsilon s_n)>-\infty, if s_1,\dots,s_n are free semicircular variables which are free from x_1,\dots,x_n. The same property for the microstates free entropy \chi, however, would imply that x_1,\dots,x_n have microstates, i.e., the von Neumann algebra generated by x_1,\dots,x_n is embeddable; see these notes of Shlyakhtenko.

But does this mean more then just saying that there are some von Neumann algebras for which we don’t have microstates but for which the non-microstates approach give some more interesting information, or is there more to it? I don’t know, but hopefully I will come back with more thoughts on this soon.

Of course, everybody is invited to share more information or thoughts on this!

Welcome to the Non-Commutative World!

About two weeks ago I posted with Tobias Mai on the archive the preprint “A Note on the Free and Cyclic Differential Calculus”. Here is what we say in the abstract:

In 2000, Voiculescu proved an algebraic characterization of cyclic gradients of noncommutative polynomials. We extend this remarkable result in two different directions: first, we obtain an analogous characterization of free gradients; second, we lift both of these results to Voiculescu’s fundamental framework of multivariable generalized difference quotient rings. For that purpose, we develop the concept of divergence operators, for both free and cyclic gradients, and study the associated (weak) grading and cyclic symmetrization operators, respectively. One the one hand, this puts a new complexion on the initial polynomial case, and on the other hand, it provides a uniform framework within which also other examples – such as a discrete version of the Ito stochastic integral – can be treated.

At the moment I am not in the mood to say more specifically about this preprint (maybe Tobias or I will do so later), but I want to take the opportunity — in particular as the first anniversary of this blog is also coming closer — to put this in a bigger context and mumble a bit about the bigger picture and our dreams … so actually about what this blog should be all about.

Free probability theory has come a long way. Whereas born in the subject of operator algebras, the realization that is also has to say quite a bit about random matrices paved the way to its use in many (and, in particular, also applied) subjects. Hence there are now also papers in statistics, like this one, or in deep learning, like this one or this one, which use tools from free probability for their problems. The last words on how far the use of free probability goes in those subjects are surely not yet spoken but I am looking forward to see more on this.

This is of course all great and nice for our subject, but on the other hand there is also a bigger picture in the background, where I would hope for some more fundamental uses of free probability.

This goes roughly like this. There is the classical world, where we are dealing with numbers and functions and everything commutes; then there is our non-commutative world, where we are dealing with operators and limits of random matrices and where on the basic level nothing commutes. That’s where quite a bit of maximal non-commutative mathematics has been (and is still being) developed from various points of views:

  • free probability deals with a non-commutative notion of independence for non-commuting random variables;
  • there is a version of a non-commutative differential calculus which allows to talk about derivatives in non-commutative variables; my paper with Tobias mentioned above is in this context and tries to formalize and put all this a bit further;
  • free analysis (or free/non-commutative function theory) aims at a non-commutative version of classical complex analysis, i.e., a theory of analytic functions in non-commuting variables;
  • free quantum groups provide the right kind of symmetries for such non-commuting variables.

The nice point is that all those subjects have their own source of motivation but it turns out that there are often relations between them which are non-commutative analogues of classical results.

So, again this is all great and nice, BUT apart from the commutative and our maximal non-commutative world there is actually the, maybe most important, quantum world. This is of course also non-commutative, but only up to some point. There operators don’t commute in general, but commutativity is replaced by some other relations, like the canonical commutation relations, and there are actually still a lot of operators which commute (for example, measurements which are at space-like positions are usually modeled by commuting operators). Because of this commutativity, basic concepts of free probability do not have a direct application there.

Here is a bit more concretely what I mean with that. In free probability we have free analogues of such basic concepts as entropy or Fisher information. There are a lot of nice statements and uses of those concepts and via random matrices they can also be seen as arising as a kind of large N limit of the corresponding classical concepts. However, in the classical world those concepts have usually also a kind of operational meaning by being the answer to fundamental questions. For example, the classical Shannon entropy is the answer to the question how much information one can transmit over classical channels. Now there are quantum channels and one can ask how much information one can transmit over them; again there are answers in terms of an entropy, but this is unfortunately not free entropy, but von Neumann entropy, a more commutative non-commutative cousin of classical entropy. There are just too many tensor products showing up in the quantum world which prevent a direct use of basic free probability concepts. But still, I am dreaming of finding some day operational meanings of free entropy and similar quantities.

Anyhow, I hope to continue to explain in this blog more of the concrete results and problems which we have in free probability and related subjects; but I just wanted to point out that there are also some bigger dreams in the background.

Class on “Random Matrices”, Winter Term 2019/20

Our winter term has just started, running from mid October 2019 to mid February 2020, with a two-week break around Christmas. This term I am giving an introduction to random matrices. Again, the lectures will be recorded and put online. The lectures can be found on our video platform; more info on the lectures are also on the website of the class.

The lectures will follow roughly the material from the same class of summer term 2018, for which there exist also texed lecture notes. There will be a few reorganizations and shifts in the material, so there might emerge also a new version of the lectures notes sometimes in the future …

Update on videos of lectures and talks

Videos of talks at Fields Institute

The videos of my three talks in the distinguished lecture series at the Fields Institute are now online and can be found here.

In particular, the first talk, entitled Dan-Virgil Voiculescu: visionary operator algebraist and creator of free probability theory, was a talk for a public audience and gives not only a bit of information on Dan Voiculescu, but also a very high level idea of what free probability is all about. And it also has some movie references …

Videos of lecture series “Non-commutative distributions”

The course on “Non-commutative distributions” has now finished; all 20 lectures are online and can be found on our video platform. My hand-written notes for the classes can be found here.

Focus Program on Applications of Noncommutative Functions; Featuring a Celebration Banquet for Dan Voiculescu’s 70th Birthday

Today started the Focus Program on Applications of Noncommutative Functions at the Fields Institute in Toronto. There will be two workshops: this week on the “Developments and Technical Aspects of Free Noncommutative Functions” and next week on “Applications to Random Matrices and Free Probability of Free Noncommutative Functions”. Both workshops look interesting to me; unfortunately I will miss most of the first one as I will fly only on Wednesday to Toronto.

I will give a series of three talks on the relation between free probability and random matrices. The first talk will be quite general and is also intended for a public academic audience. Its main purpose is to celebrate the 70th birthday of Dan Voiculescu by giving an idea of Dan’s achievements and of free probability theory. The talk and the banquet will be on the very day of Dan’s birthday.

SYK Model and q-Brownian Motion

Recently I became aware of the so-called Sachdev-Ye-Kitaev (SYK) model, which has attracted quite some interest in the last couple of years in physics, as a kind of toy model for quantum holography. What attracted my attention was the fact that in some limit there appears a q-deformation of the Gauss distribution – the same one which also showed up in my old papers with Marek Bozejko and Burkhard K├╝mmerer on non-commutative versions of Brownian motions, see here and here. Whereas in the SYK context there is usually only one limit distribution, in our non-commutative probability context we usually have the multivariate situation with several random variables (corresponding to the increments of the process). Thus I wanted to see whether one can also extend the calculations in the SYK model to a multivariate setting. This is done together with Miguel Pluma in our paper The SYK Model and the q-Brownian Motion. It turns out that one gets indeed q-Gaussian variables corresponding to orthogonal vectors for independent SYK models.

It is not clear to me whether such independent copies of SYK models have any physical relevance. However, there have recently been some papers by Berkooz and collaborators, here and here, where they calculated the 2-point and the 4-point function for the large N double scaled SYK model, by using also essentially the combinatorics of such multivariate extensions.

Those calculations are quite technical and not easy, and it seems to be unclear whether one can get a final analytic result. This seems to be related to our problems of doing any useful analytic calculations with the multivariate q-Gaussian distribution, which is one of the main obstructions for progress on free entropy or Brown measures for the q-Gaussian distributions. (Okay, there has been some progress via free transport by Alice Guionnet and Dima Shlyakhtenko, but this is quite abstract without concrete analytic formulas.) It would be nice (and surely helpful) if we could get some more concrete description of the operator-valued Cauchy transform of the multivariate q-Gaussian distribution.