Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise

Tüysüz, Cenk; Grossi, Michele; Chang, Su Yeon; Demidik, Maria; Jansen, Karl; Vallecorsa, Sofia

Preprint

PUBDB-2024-07090

Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise

Tüysüz, C. ; Chang, S. Y. ; Demidik, M. ; Jansen, K. (Corresponding author)DESY* ; Vallecorsa, S. ; Grossi, M.

2024

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Report No.: arXiv:2401.10293

Abstract: Geometric quantum machine learning based on equivariant quantum neural networks (EQNNs) recently appeared as a promising direction in quantum machine learning. Despite encouraging progress, studies are still limited to theory, and the role of hardware noise in EQNN training has never been explored. This work studies the behavior of EQNN models in the presence of noise. We show that certain EQNN models can preserve equivariance under Pauli channels, while this is not possible under the amplitude damping channel. We claim that the symmetry breaks linearly in the number of layers and noise strength. We support our claims with numerical data from simulations as well as hardware up to 64 qubits. Furthermore, we provide strategies to enhance the symmetry protection of EQNN models in the presence of noise.

Keyword(s): machine learning: quantum ; neural network: quantum ; noise ; hardware ; symmetry breaking ; qubit ; Pauli