Machine learning blog at ETH

  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”

A Book That Everyone Should Read: Naomi Alderman

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”

New paradigm for parameter estimation

  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”

Exporting histograms from TensorFlow

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”

A Case for Past Quantum State

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 inContinue reading “A Case for Past Quantum State”