5 papers
Characterizing the loss landscape of variational quantum circuits
Patrick Huembeli, Alexandre Dauphin
Machine learning techniques enhanced by noisy intermediate-scale quantum (NISQ) devices and especially variational quantum circuits (VQC) have recently attracted much interest and…
Phase Detection with Neural Networks: Interpreting the Black Box
Anna Dawid, Patrick Huembeli, Michał Tomza +2
Neural networks (NNs) usually hinder any insight into the reasoning behind their predictions. We demonstrate how influence functions can unravel the black box of NN when trained to…
Unsupervised phase discovery with deep anomaly detection
Korbinian Kottmann, Patrick Huembeli, Maciej Lewenstein +1
We demonstrate how to explore phase diagrams with automated and unsupervised machine learning to find regions of interest for possible new phases. In contrast to supervised learnin…
QuCumber: wavefunction reconstruction with neural networks
Matthew J. S. Beach, Isaac De Vlugt, Anna Golubeva +6
As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter…
Automated discovery of characteristic features of phase transitions in many-body localization
Patrick Huembeli, Alexandre Dauphin, Peter Wittek +1
We identify a new "order parameter" for the disorder driven many-body localization (MBL) transition by leveraging artificial intelligence. This allows us to pin down the transition…