Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session S37: Quantum Machine Learning I
8:00 AM–10:48 AM,
Thursday, March 17, 2022
Room: McCormick Place W-194B
Sponsoring
Units:
DQI GDS
Chair: Zoe Holmes, Los Alamos National Laboratory
Abstract: S37.00012 : Machine learning phase transitions in a scalable manner*
10:36 AM–10:48 AM
Presenter:
Marina Krstic Marinkovic
(ETH Zurich)
Authors:
Marina Krstic Marinkovic
(ETH Zurich)
Arturo DeGiorgi
(Autonomous University of Madrid)
As the applications of the quantum machine learning approach in quantum field theories are moving beyond the toy models, the parallelization of learning algorithms and alternative approaches to their efficient implementation gains in importance. In this talk, I will present two possible avenues to speed up the machine learning methods with applications to phase transitions classifications. After the discussion of the support vector machine learning model with a focus on its efficient parallelization, we will move the SVM to a quantum circuit and benchmark it on the spin systems in two dimensions.
*The authors acknowledge the funding through the Platform for Advanced Scientific Computing (PASC) and access to Piz Daint at the Swiss National Supercomputing Centre, Switzerland under the project ID c21. Computations were also performed on the Euler HPC cluster at ETH Zurich. We acknowledge the use of the IBM Q for this work. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM Q team.
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