30 citations · 47 across the 5 of their papers we have counts for
5 papers
Learning symmetry-protected topological order from trapped-ion experiments
Nicolas Sadoune, Ivan Pogorelov, Claire L. Edmunds +6
Classical machine learning has proven remarkably useful in post-processing quantum data, yet typical learning algorithms often require prior training to be effective. In this work,…
Human-machine collaboration: ordering mechanism of rank-2 spin liquid on breathing pyrochlore lattice
Nicolas Sadoune, Ke Liu, Han Yan +3
Machine learning algorithms thrive on large data sets of good quality. Here we show that they can also excel in a typical research setting with little data of limited quality, thro…
Unsupervised Interpretable Learning of Phases From Many-Qubit Systems
Nicolas Sadoune, Giuliano Giudici, Ke Liu +1
Experimental progress in qubit manufacturing calls for the development of new theoretical tools to analyze quantum data. We show how an unsupervised machine-learning technique can…
Efficient and scalable Path Integral Monte Carlo Simulations with worm-type updates for Bose-Hubbard and XXZ models
Nicolas Sadoune, Lode Pollet
We present a novel and open-source implementation of the worm algorithm, which is an algorithm to simulate Bose-Hubbard and sign-positive spin models using a path integral represen…
Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin Liquids
Ke Liu, Nicolas Sadoune, Nihal Rao +2
Kitaev materials are promising materials for hosting quantum spin liquids and investigating the interplay of topological and symmetry-breaking phases. We use an unsupervised and in…