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 forContinue reading “Machine learning blog at ETH”
I can’t even begin to describe the impact that reading The Power by Naomi Alderman had on me (and I am sure I am not alone). I would even go as far as to predict that this book will become a classic that will keep on being discussed for many decades. The main premise isContinue reading “A Book That Everyone Should Read: Naomi Alderman”
For me 2017 was all about reading bit out of my comfort zone and expanding my genre horizons. Even though it was fun and I found some delightful books, I am looking forward to go back to my line-up of contemporary fiction in 2018. The most remarkable fiction I read last year was definitely PaulContinue reading “2017 in Books”
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 fromContinue reading “New paradigm for parameter estimation”
I am happy to announce, that our paper “Conditioned spin and charge dynamics of a single-electron quantum dot” (I discussed it here) have been chosen as Editors’ Suggestion in Physical Review A.
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 exportContinue reading “Exporting histograms from TensorFlow”