Detecting quantum phase transitions in a frustrated spin chain via
transfer learning of a quantum classifier algorithm
- URL: http://arxiv.org/abs/2309.15339v1
- Date: Wed, 27 Sep 2023 01:11:11 GMT
- Title: Detecting quantum phase transitions in a frustrated spin chain via
transfer learning of a quantum classifier algorithm
- Authors: Andr\'e J. Ferreira-Martins, Leandro Silva, Alberto Palhares, Rodrigo
Pereira, Diogo O. Soares-Pinto, Rafael Chaves and Askery Canabarro
- Abstract summary: We propose an alternative framework to identify quantum phase transitions.
Using the axial next-nearest neighbor Ising (ANNNI) model as a benchmark, we show how machine learning can detect three phases.
We also compare the performance of common classical machine learning methods with a version of the quantum nearest neighbors (QNN) algorithm.
- Score: 1.2145532233226681
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The classification of phases and the detection of phase transitions are
central and challenging tasks in diverse fields. Within physics, it relies on
the identification of order parameters and the analysis of singularities in the
free energy and its derivatives. Here, we propose an alternative framework to
identify quantum phase transitions. Using the axial next-nearest neighbor Ising
(ANNNI) model as a benchmark, we show how machine learning can detect three
phases (ferromagnetic, paramagnetic, and a cluster of the antiphase with the
floating phase). Employing supervised learning, we demonstrate the feasibility
of transfer learning. Specifically, a machine trained only with
nearest-neighbor interactions can learn to identify a new type of phase
occurring when next-nearest-neighbor interactions are introduced. We also
compare the performance of common classical machine learning methods with a
version of the quantum nearest neighbors (QNN) algorithm.
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