  Quantum machine learning provides a fundamentally novel and promising
approach to analyzing data. However, many data sets are too complex for
currently available quantum computers. Consequently, quantum machine learning
applications conventionally resort to dimensionality reduction algorithms,
e.g., auto-encoders, before passing data through the quantum models. We show
that using a classical auto-encoder as an independent preprocessing step can
significantly decrease the classification performance of a quantum machine
learning algorithm. To ameliorate this issue, we design an architecture that
unifies the preprocessing and quantum classification algorithms into a single
trainable model: the guided quantum compression model. The utility of this
model is demonstrated by using it to identify the Higgs boson in proton-proton
collisions at the LHC, where the conventional approach proves ineffective.
Conversely, the guided quantum compression model excels at solving this
classification problem, achieving a good accuracy. Additionally, the model
developed herein shows better performance compared to the classical benchmark
when using only low-level kinematic features.
