  In addition to the need for stable and precisely controllable qubits, quantum
computers take advantage of good readout schemes. Superconducting qubit states
can be inferred from the readout signal transmitted through a dispersively
coupled resonator. This work proposes a novel readout classification method for
superconducting qubits based on a neural network pre-trained with an
autoencoder approach. A neural network is pre-trained with qubit readout
signals as autoencoders in order to extract relevant features from the data
set. Afterwards, the pre-trained network inner layer values are used to perform
a classification of the inputs in a supervised manner. We demonstrate that this
method can enhance classification performance, particularly for short and long
time measurements where more traditional methods present lower performance.
