Machine Learning for Many-Body Physics @ KITP

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 theContinue reading “Machine Learning for Many-Body Physics @ KITP”

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”

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”