  We present a machine-learning scheme based on the relativistic dynamics of a
quantum system, namely a quantum detector inside a cavity resonator. An
equivalent analog model can be realized for example in a circuit QED platform
subject to properly modulated driving fields. We consider a reservoir-computing
scheme where the input data are embedded in the modulation of the system
(equivalent to the acceleration of the relativistic object) and the output data
are obtained by linear combinations of measured observables. As an illustrative
example, we have simulated such a relativistic quantum machine for a
challenging classification task, showing a very large enhancement of the
accuracy in the relativistic regime. Using kernel-machine theory, we show that
in the relativistic regime the task-independent expressivity is dramatically
magnified with respect to the Newtonian regime.
