Journal Article

PUBDB-2024-04833

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise

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

2024
American Physical Society College Park, MD

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Please use a persistent id in citations: doi:10.48550/ARXIV.2401.10293  doi:10.1103/PRXQuantum.5.030314  doi:10.3204/PUBDB-2024-04833

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): Quantum Physics (quant-ph) ; Machine Learning (cs.LG) ; FOS: Physical sciences ; FOS: Computer and information sciences

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Contributing Institute(s):

1. Zeuthen Particle PhysicsTheory (Z_ZPPT)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)
2. QUEST - QUantum computing for Excellence in Science and Technology (101087126) (101087126)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2024

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Preprint Tüysüz, C. ; Chang, S. Y. ; Demidik, M. ; Jansen, K. (Corresponding author)DESY* ; Vallecorsa, S. ; Grossi, M.
Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise
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