  We demonstrate - for the first time - the application of a quantum machine
learning (QML) algorithm on an on-site room-temperature quantum computer. A
two-qubit quantum computer installed at the Pawsey Supercomputing Centre in
Perth, Australia, is used to solve multi-class classification problems on
unseen, i.e. untrained, 2D data points. The underlying 1-qubit model is based
on the data re-uploading framework of the universal quantum classifier and was
trained on an ideal quantum simulator using the Adam optimiser. No noise models
or device-specific insights were used in the training process. The optimised
model was deployed to the quantum device by means of a single XYX decomposition
leading to three parameterised single qubit rotations. The results for
different classification problems are compared to the optimal results of an
ideal simulator. The room-temperature quantum computer achieves very high
classification accuracies, on par with ideal state vector simulations.
