Image classification with rotation-invariant variational quantum circuits
Paul San Sebastian, Mikel Cañizo, Román Orús · Physical Review Research · 2025
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 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.