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 […]
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 […]
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 […]
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 export […]
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 […]