  Variational quantum algorithms are gaining attention as an early application
of Noisy Intermediate-Scale Quantum (NISQ) devices. One of the main problems of
variational methods lies in the phenomenon of Barren Plateaus, present in the
optimization of variational parameters. Adding geometric inductive bias to the
quantum models has been proposed as a potential solution to mitigate this
problem, leading to a new field called Geometric Quantum Machine Learning. In
this work, an equivariant architecture for variational quantum classifiers is
introduced to create a label-invariant model for image classification with
$C_4$ rotational label symmetry. The equivariant circuit is benchmarked against
two different architectures, and it is experimentally observed that the
geometric approach boosts the model's performance. Finally, a classical
equivariant convolution operation is proposed to extend the quantum model for
the processing of larger images, employing the resources available in NISQ
devices.
