Journal Club for Quantum Physics and Machine Learning



Next Journal Club Meeting: Tuesday, 15th June 2021, 4:30pm CET.

Nishad Maskara (Harvard University) will present the following paper: A learning algorithm with emergent scaling behavior for classifying phase transitions

Abstract: Machine learning-inspired techniques have emerged as a new paradigm for analysis of phase transitions in quantum matter. In this work, we introduce a supervised learning algorithm for studying critical phenomena from measurement data, which is based on iteratively training convolutional networks of increasing complexity, and test it on the transverse field Ising chain and q=6 Potts model. At the continuous Ising transition, we identify scaling behavior in the classification accuracy, from which we infer a characteristic classification length scale. It displays a power-law divergence at the critical point, with a scaling exponent that matches with the diverging correlation length. Our algorithm correctly identifies the thermodynamic phase of the system and extracts scaling behavior from projective measurements, independently of the basis in which the measurements are performed. Furthermore, we show the classification length scale is absent for the q=6 Potts model, which has a first order transition and thus lacks a divergent correlation length. The main intuition underlying our finding is that, for measurement patches of sizes smaller than the correlation length, the system appears to be at the critical point, and therefore the algorithm cannot identify the phase from which the data was drawn.

Upcoming Journal Club meetings:

Date Time Speaker Affiliation Reference
15th June 2021 4:30pm CET  Nishad Maskara  Harvard University  A learning algorithm with emergent scaling behavior for classifying phase transitions
29th June 2021 4:30pm CET  TBA  TBA  TBA


Past Journal Club Meetings:

Date Time Speaker Affiliation Reference
1st June 2021 4:30pm CET  Rouven Koch  Aalto University  Neural network enhanced hybrid quantum many-body dynamical distributions
18th May 2021 4:30pm CET  Wei Chen  PUC Rio de Janeiro A supervised learning algorithm for interacting topological insulators based on local curvature
4th May 2021 4:30pm CET  Agnes Valenti ETH Zürich  Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
20th April 2021 4:30pm CET  Rui Lin  ETH Zürich 

Minimal model of permutation symmetry in unsupervised learning


6th April 2021 4:30pm CET  Miriam Büttner  University of Freiburg

Learning Potentials of Quantum Systems using Deep Neural Networks


 23rd March 2021 4:30pm CET Luuk Coopmans  Trinity College Dublin/Dublin Institute for Advanced Studies Protocol Discovery for the Quantum Control of Majoranas by Differential Programming and Natural Evolution Strategies
 9th March 2021 4:30pm CET  Axel Lode University of Freiburg

Interpretable Phase Detection and Classification with Persistent Homology


 23rd February 2021  4:30pm CET  Evert van Nieuwenburg  University of Copenhagen A NEAT Quantum Error Decoder
 9th February 2021  3pm CET  Shahnawaz Ahmed  Chalmers University of  Technology Classification and reconstruction of optical quantum states with deep neural networks 
 26th January 2021  3pm CET  Julian Arnold  University of Basel Interpretable and unsupervised phase classification
 12th January 2021  3pm CET  Paolo Molignini  University of Cambridge

Scientific intuition inspired by machine learning generated hypotheses


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