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”

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”