Home > Publications database > Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise > print

001     617836
005     20250115142352.0
024 7 _ |2 INSPIRETeX
|a Tuysuz:2024gnk
024 7 _ |2 inspire
|a inspire:2749483
024 7 _ |2 arXiv
|a arXiv:2401.10293
024 7 _ |a altmetric:158592528
|2 altmetric
037 _ _ |a PUBDB-2024-07090
041 _ _ |a English
082 _ _ |a 530
088 _ _ |2 arXiv
|a arXiv:2401.10293
100 1 _ |a Tüysüz, Cenk
|b 0
245 _ _ |a Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise
260 _ _ |c 2024
336 7 _ |0 PUB:(DE-HGF)25
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336 7 _ |2 ORCID
|a WORKING_PAPER
336 7 _ |0 28
|2 EndNote
|a Electronic Article
336 7 _ |2 DRIVER
|a preprint
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 DataCite
|a Output Types/Working Paper
520 _ _ |a 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.
536 _ _ |0 G:(DE-HGF)POF4-611
|a 611 - Fundamental Particles and Forces (POF4-611)
|c POF4-611
|f POF IV
|x 0
536 _ _ |0 G:(EU-Grant)101087126
|a QUEST - QUantum computing for Excellence in Science and Technology (101087126)
|c 101087126
|f HORIZON-WIDERA-2022-TALENTS-01
|x 1
588 _ _ |a Dataset connected to CrossRef, INSPIRE, Journals: bib-pubdb1.desy.de
650 _ 7 |2 INSPIRE
|a machine learning: quantum
650 _ 7 |2 INSPIRE
|a neural network: quantum
650 _ 7 |2 INSPIRE
|a noise
650 _ 7 |2 INSPIRE
|a hardware
650 _ 7 |2 INSPIRE
|a symmetry breaking
650 _ 7 |2 INSPIRE
|a qubit
650 _ 7 |2 INSPIRE
|a Pauli
693 _ _ |0 EXP:(DE-MLZ)NOSPEC-20140101
|5 EXP:(DE-MLZ)NOSPEC-20140101
|e No specific instrument
|x 0
700 1 _ |0 0000-0001-5768-2434
|a Chang, Su Yeon
|b 1
700 1 _ |a Demidik, Maria
|b 2
700 1 _ |0 P:(DE-H253)PIP1003636
|a Jansen, Karl
|b 3
|e Corresponding author
700 1 _ |a Vallecorsa, Sofia
|b 4
700 1 _ |0 0000-0003-1718-1314
|a Grossi, Michele
|b 5
856 4 _ |u https://bib-pubdb1.desy.de/record/617836/files/2401.10293v1.pdf
|y Restricted
856 4 _ |u https://bib-pubdb1.desy.de/record/617836/files/2401.10293v1.pdf?subformat=pdfa
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909 C O |o oai:bib-pubdb1.desy.de:617836
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910 1 _ |0 I:(DE-588b)2008985-5
|6 P:(DE-H253)PIP1003636
|a Deutsches Elektronen-Synchrotron
|b 3
|k DESY
913 1 _ |0 G:(DE-HGF)POF4-611
|1 G:(DE-HGF)POF4-610
|2 G:(DE-HGF)POF4-600
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|a DE-HGF
|b Forschungsbereich Materie
|l Matter and the Universe
|v Fundamental Particles and Forces
|x 0
914 1 _ |y 2024
915 _ _ |a Published
|0 StatID:(DE-HGF)0580
|2 StatID
920 1 _ |0 I:(DE-H253)CQTA-20221102
|k CQTA
|l Centre f. Quantum Techno. a. Application
|x 0
980 _ _ |a preprint
980 _ _ |a VDB
980 _ _ |a I:(DE-H253)CQTA-20221102
980 _ _ |a UNRESTRICTED

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