Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks

Entong Zhao, Jeongwon Lee, Chengdong He, Zejian Ren, Elnur Hajiyev, Junwei Liu and Gyu-Boong Jo ()
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Entong Zhao: The Hong Kong University of Science and Technology
Jeongwon Lee: The Hong Kong University of Science and Technology
Chengdong He: The Hong Kong University of Science and Technology
Zejian Ren: The Hong Kong University of Science and Technology
Elnur Hajiyev: The Hong Kong University of Science and Technology
Junwei Liu: The Hong Kong University of Science and Technology
Gyu-Boong Jo: The Hong Kong University of Science and Technology

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract The power of machine learning (ML) provides the possibility of analyzing experimental measurements with a high sensitivity. However, it still remains challenging to probe the subtle effects directly related to physical observables and to understand physics behind from ordinary experimental data using ML. Here, we introduce a heuristic machinery by using machine learning analysis. We use our machinery to guide the thermodynamic studies in the density profile of ultracold fermions interacting within SU(N) spin symmetry prepared in a quantum simulator. Although such spin symmetry should manifest itself in a many-body wavefunction, it is elusive how the momentum distribution of fermions, the most ordinary measurement, reveals the effect of spin symmetry. Using a fully trained convolutional neural network (NN) with a remarkably high accuracy of ~94% for detection of the spin multiplicity, we investigate how the accuracy depends on various less-pronounced effects with filtered experimental images. Guided by our machinery, we directly measure a thermodynamic compressibility from density fluctuations within the single image. Our machine learning framework shows a potential to validate theoretical descriptions of SU(N) Fermi liquids, and to identify less-pronounced effects even for highly complex quantum matter with minimal prior understanding.

Date: 2021
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DOI: 10.1038/s41467-021-22270-5

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