On Quantum Hyperparameters Selection in Hybrid Classifiers for Earth
Observation Data
Abstract
Quantum Machine Learning (QML) is an emerging technology that only
recently has begun to take root in the research fields of Earth
Observation (EO) and Remote Sensing (RS), and whose state of the art is
roughly divided into one group oriented to fully quantum solutions, and
in another oriented to hybrid solutions. Very few works applied QML to
EO tasks, and none of them explored a methodology able to give
guidelines on the hyperparameter tuning of the quantum part. As a first
step in the direction of quantum advantage for RS data classification,
this letter opens new research lines, allowing us to demonstrate that
there are more convenient solutions to simply increasing the number of
qubits in the quantum part. To pave the first steps for researchers
interested in the above, the structure of a new hybrid quantum neural
network is proposed with a strategy to choose the number of qubits to
find the most efficient combination in terms of both system complexity
and results accuracy. We sampled and tried a number of configurations,
and using the suggested method we came up with the most efficient
solution (in terms of the selected metrics). Better performance is
achieved with less model complexity when tested and compared with
state-of-the-art techniques for identifying volcanic eruptions chosen
as a case study. Additionally, the method makes the model more resilient
to dataset imbalance, a major problem when training classical models.
Lastly, the code is freely available so that interested researchers can
reproduce and extend the results.