Authors:
Michael Kölle
;
Alessandro Giovagnoli
;
Jonas Stein
;
Maximilian Mansky
;
Julian Hager
and
Claudia Linnhoff-Popien
Affiliation:
Institute of Informatics, LMU Munich, Oettingenstraße 67, Munich, Germany
Keyword(s):
Variational Quantum Circuits, Variational Classifier, Weight Re-Mapping.
Abstract:
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum
circuits (VQCs). VQCs are inspired by artificial neural networks, which achieve extraordinary performance
in a wide range of AI tasks as massively parameterized function approximators. VQCs have already demonstrated promising results, for example, in generalization and the requirement for fewer parameters to train, by
utilizing the more robust algorithmic toolbox available in quantum computing. A VQCs’ trainable parameters
or weights are usually used as angles in rotational gates and current gradient-based training methods do not
account for that. We introduce weight re-mapping for VQCs, to unambiguously map the weights to an interval
of length 2π, drawing inspiration from traditional ML, where data rescaling, or normalization techniques have
demonstrated tremendous benefits in many circumstances. We employ a set of five functions and evaluate
them on the Iris and Wine
datasets using variational classifiers as an example. Our experiments show that
weight re-mapping can improve convergence in all tested settings. Additionally, we were able to demonstrate
that weight re-mapping increased test accuracy for the Wine dataset by 10% over using unmodified weights.
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