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Training Parameterized Quantum Circuits with Triplet Loss

Wendenius, Christof 1; Kuehn, Eileen ORCID iD icon 2; Streit, Achim ORCID iD icon 2
1 Karlsruher Institut für Technologie (KIT)
2 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Training parameterized quantum circuits (PQCs) is a growing research area that has received a boost from the emergence of new hybrid quantum classical algorithms and Quantum Machine Learning (QML) to leverage the power of today’s quantum computers. However, a universal pipeline that guarantees good learning behavior has not yet been found, due to several challenges. These include in particular the low number of qubits and their susceptibility to noise but also the vanishing of gradients during training. In this work, we apply and evaluate Triplet Loss in a QML training pipeline utilizing a PQC for the first time. We perform extensive experiments for the Triplet Loss based setup and training on two common datasets, the MNIST and moon dataset. Without significant fine-tuning of training parameters and circuit layout, our proposed approach achieves competitive results to a regular training. Additionally, the variance and the absolute values of gradients are significantly better compared to training a PQC without Triplet Loss. The usage of metric learning proves to be suitable for QML and its high dimensional space as it is not as restrictive as learning on hard labels. ... mehr


Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2023
Sprache Englisch
Identifikator ISBN: 978-3-031-26418-4
ISSN: 0302-9743
KITopen-ID: 1000157788
HGF-Programm 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups
Erschienen in Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings. Part V. Ed.: M.-R. Amini
Veranstaltung European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022), Online, 19.09.2022 – 23.09.2022
Auflage 1
Verlag Springer Nature Switzerland
Seiten 515–530
Serie Lecture Notes in Computer Science ; 13717
Vorab online veröffentlicht am 17.03.2023
Nachgewiesen in Scopus
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