  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 $2\pi$, 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.
