On Quantum Hyperparameters Selection in Hybrid Classifiers for Earth Observation Data

• Published in: IEEE geoscience and remote sensing letters 2023-01, Vol.20, p.1-1
• Main Authors: Sebastianelli, Alessandro, Del Rosso, Maria Pia, Ullo, Silvia L., Gamba, Paolo
• Format: Article
• Language: English
• Subjects: Classification; Complexity; Computer architecture; Convolutional neural networks; Earth Observation; Image Analysis; Land cover; Logic gates; Machine learning; Neural networks; New technology; Quantum computing; Quantum Convolutional Neural Networks; Quantum Deep Learning; Quantum entanglement; Qubit; Qubits (quantum computing); Remote observing; Remote Sensing; Volcanic eruptions
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2023.3308105

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 for Land Cover Classification (LCC). 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 for EO data and LCC 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 (SOTA) and standard techniques for identifying volcanic eruptions chosen as a case study. Additionally, the method makes the model more resilient to dataset imbalance, a significant problem when training classical models. Lastly, the code is freely available so that interested researchers can reproduce and extend the results.