  Establishing low-error and fast detection methods for qubit readout is
crucial for efficient quantum error correction. Here, we test neural networks
to classify a collection of single-shot spin detection events, which are the
readout signal of our qubit measurements. This readout signal contains a
stochastic peak, for which a Bayesian inference filter including Gaussian noise
is theoretically optimal. Hence, we benchmark our neural networks trained by
various strategies versus this latter algorithm. Training of the network with
10$^{6}$ experimentally recorded single-shot readout traces does not improve
the post-processing performance. A network trained by synthetically generated
measurement traces performs similar in terms of the detection error and the
post-processing speed compared to the Bayesian inference filter. This neural
network turns out to be more robust to fluctuations in the signal offset,
length and delay as well as in the signal-to-noise ratio. Notably, we find an
increase of 7 % in the visibility of the Rabi-oscillation when we employ a
network trained by synthetic readout traces combined with measured signal noise
of our setup. Our contribution thus represents an example of the beneficial
role which software and hardware implementation of neural networks may play in
scalable spin qubit processor architectures.
