Image classification with rotation-invariant variational quantum circuits

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 , 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 <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:msub><a:mi>C</a:mi><a:mn>4</a:mn></a:msub></a:math> 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.