  Machine learning is among the most widely anticipated use cases for near-term
quantum computers, however there remain significant theoretical and
implementation challenges impeding its scale up. In particular, there is an
emerging body of work which suggests that generic, data agnostic quantum
machine learning (QML) architectures may suffer from severe trainability
issues, with the gradient of typical variational parameters vanishing
exponentially in the number of qubits. Additionally, the high expressibility of
QML models can lead to overfitting on training data and poor generalisation
performance. A promising strategy to combat both of these difficulties is to
construct models which explicitly respect the symmetries inherent in their
data, so-called geometric quantum machine learning (GQML). In this work, we
utilise the techniques of GQML for the task of image classification, building
new QML models which are equivariant with respect to reflections of the images.
We find that these networks are capable of consistently and significantly
outperforming generic ansatze on complicated real-world image datasets,
bringing high-resolution image classification via quantum computers closer to
reality. Our work highlights a potential pathway for the future development and
implementation of powerful QML models which directly exploit the symmetries of
data.
