Kavli Institute for Theoretical Physics organises wonderful few-weeks-long workshops throughout the year, and I recently attended one on machine learning in many-body physics. The general idea of these workshops is that one gets an office to work in, there is only one or two talks a day and the participants get to interact in the informal, but super stimulating atmosphere. Also, you get an incredible ocean view! If this sounds good, you should check out KITP webpage to see if there is a workshop interesting for you (you can also propose a new one).
In this post I would like to round up a few things that I found most interesting and useful.
You can find the full list of talks here. I recommend watching Aleksander Kubica, Marin Bukov and Evert van Nieuwenburg, they all do very cool stuff.
Overall it seems to me that while there was no one mindblowing result that changed how we think about physics, there has been tremendous amount of progress. The one place where ml methods seem to provide most advantage is the processing and analysis of experimental data, but the many-body state ansatzes, error correction improvements, and phase transitions analyses definitely offer very cool insights.
The interest in possible applications of machine learning in physics has been growing exponentially for a while now and there seems to be a sea of literature. Couple of months ago we decided to review the literature and have weekly seminar about the papers we found interesting. There is a dedicated blog post for each of these on our group website.
Recently I have contributed with the discussion of the paper by E. M. Stoudenmire and D. J. Schwab: Supervised Learning with Quantum-Inspired Tensor Networks (arXiv:1605.05775 (2017)). In this paper the authors propose to train a tensor network with DMRG-like sweeps. You can read my full post here.
In our latest work, now on arXiv, we show how to use a convolutional neural network to extract physical parameters (even the quantum ones!) from experimental currents.
In my PhD I was generally concerned with monitoring and parameter estimation of quantum systems. These elements are crucial for efficiently functioning quantum devices, and, in difference from on-chip quantum operations, there is still a long way to go in terms of getting efficient readout at reasonable times. The ability to extract the maximum amount of information from an experimental record is therefore essential.
In practice, the experimental noise is sometimes so stubborn and viciously correlated that it may be really hard if not impossible to construct a quantum model that describes it. In our work we show that even for the cases where traditional parameter estimation methods do not work the convolutional network is a great solution to find the parameters governing the dynamics of the system.
Lately I have been working a lot with Google’s TensorFlow library for machine learning. It has a really nice tool for data visualisation, TensorBoard, which can be very useful to understand how the training and evaluation of your model is working. One small bottleneck though is that it has a built-in tool for data export that only works for the scalar functions and unfortunately not for more complex visualisation means like histograms. I find especially the histograms to be particularly useful because they show how is your probability distribution narrowing as a function of learning steps, so it is a really useful figure of merit for understanding the training/evaluation. This is also the reason why I thought it would be useful to export the histograms and customise them for example in Matlab. I would like to share here the code for exporting the histograms from the TensorFlow model. Hopefully you will find it useful! You can download it here.
Recently I finished my latest work that has been done in collaboration with my wonderful supervisor and Oxford experimental team and I would like to use this post to advertise it a bit in general terms. You can read it in full at arXiv.
The past quantum state method relies on a simple assumption: since in practical experimental situations you would like to monitor your system continuously and collect as much data as possible it makes sense to condition your probability not only what happened to your system BEFORE the time t (that is any given time for which you would like to make you probability prediction), but also AFTER the time t. In other words you use both the PAST and the FUTURE (from the point of the time, t, you are interested in) to make a probability prediction. This might sound a little bit sci-fi but as in general in quantum reality it is nothing too fancy, you basically just need to modify the Born rule a bit. The method was first proposed here and we used this kind of reasoning to argue stuff about correlation functions and improve fidelity of the teleportation protocol.
Here we took on the challenge to improve the experimental readout of the single electron quantum dot as well as modify existing techniques for parameter estimation. As it turns out, for typical experimental parameters, we are able to remove most of the noise and we are able to find time of each tunnelling event with super high precision. In addition to that we modified the Baum-Welch parameter estimation method and combined it with good old Bayesian to estimate both coherent and incoherent parameters under the same footing. So if you like quantum dots or you are just interested in quantum measurement theory in general, please have a look!
In general, I am a huge fan of literary classics and a contemporary fiction, and I spend most of my time reading it. Lately, I tried to extend my literary horizons by adding some new biographies and non-fiction books into this years reading list. I wasn’t disappointed and enjoyed throughly some contemporary perspectives from talented individuals with fresh and eye-opening points of view.
Let me start with Giant of the Senate by Al Franken, which is genuine and funny autobiography of the junior Minnesota senator. There is many funny stories making the book light and super entertaining read, but at the same time he doesn’t come out as a person who is living his life just to create a lot of funny stories about himself. There are underlying ideas and standpoints strongly present and Al seems like a person who genuinely wants to make the world a better place (I guess the fact that I happen to personally agree with much of his political ideology kind of helps there:)). Of course, he is little bit full of himself, but at the same time he is able to make fun of himself and also convey very strongly motivations for his political stands and principles. At the moment, I am reading another of his books, Lies and the Lying Liars Who Tell Them, which he wrote during the Bush administration before he became a senator and I must say I find it to be an extraordinary piece of political satire and if you ever been frustrated with Ann Coulter or Bill O’Reilly reading it definitely brings a lot of humorous relieve..
Another memoir I read recently was Born a Crime by Trevor Noah. In this book Trevor concentrates on his growing up in South Africa during apartheid and right after it was dismantled. Even though he is still his usual funny self, he shows much more serious side in his book and well, most of his experiences are extremely chilling. Reading about the ‘perfect racism’ of apartheid is extremely scary… and then you get sad and scared even more when you realise that there is still way too many people around the world who subscribe to those points of view (which for some reason become somehow acceptable if you deviate just slightly enough from appalling apartheid rules). I must say that I found mostly admirable how he dealt with the difficult situations he was put in and found the book overall great and worth reading.
New book by Neil deGrasse Tyson, Astrophysics for People in a Hurry, is a pure pleasure to read (or listen to, which is what I did, since Neil narrates the recording himself). I have been raving on this blog about how great job is American Museum of Natural History doing in making science fun and accessible and Neil of course contributes to that greatly. He has wonderful storytelling skills and cool ways to explain science without unnecessary trivialisations. As a physicist I am always wary reading popular books about physics, because there is only so many popular explanations of entanglement I can take, but this book was an overall pleasant surprise. Well, I’m not an astrophysicist, so maybe some of them would beg to differ. But I really liked the science explanations (even the LIGO detection of gravitational waves was included!), and while I don’t necessarily agree with all Neil’s philosophical points of view I thoroughly enjoyed the book and would recommend it to everyone, a scientist or not.
Finally, I read Modern Romance: An Investigation by Aziz Ansari and Eric Klinenberg. This was definitely the lightest of this portion of my non-fiction reads content-wise, but still interesting, serious and sufficiently scientific investigation of how the newest technology is impacting the human relationships. Aziz is a great writer and the studies presented seemed serious enough to me, even though I don’t have experience in social science. The conclusion presented is basically this: while we are in much better position to find a partner that is a right match for us compared to the generation of our (grand)parents, we get overwhelmed with the amount of choice and put much less effort into actually cultivating relationships with the people we meet. So while our parents had to make a choice about a spouse from a very small set of people, they were generally valuing the relationship more and tried to put more effort into making it work (because, well, that was the person they were supposed to be stuck with for the rest of their life), we tend to dismiss people based on tinder photo or weird text and never talk to them again.
In addition to this I am re-reading To Kill a Mockingbird mostly because I feel like everyone around me keeps quoting Atticus Finch and I barely remember any details from the story since high school, so I feel perhaps I didn’t appreciate it enough as a teenager and want to give it deeper look.