  For the first time, we enable the execution of hybrid machine learning
methods on real quantum computers with 100 data samples and real-device-based
simulations with 5,000 data samples, thereby outperforming the current state of
research of Suryotrisongko and Musashi from 2022 who were dealing with 1,000
data samples and quantum simulators (pure software-based emulators) only.
Additionally, we beat their reported accuracy of $76.8\%$ by an average
accuracy of $91.2\%$, all within a total execution time of 1,687 seconds. We
achieve this significant progress through two-step strategy: Firstly, we
establish a stable quantum architecture that enables us to execute HQML
algorithms on real quantum devices. Secondly, we introduce new hybrid quantum
binary classification algorithms based on Hoeffding decision tree algorithms.
These algorithms speed up the process via batch-wise execution, reducing the
number of shots required on real quantum devices compared to conventional
loop-based optimizers. Their incremental nature serves the purpose of online
large-scale data streaming for DGA botnet detection, and allows us to apply
hybrid quantum machine learning to the field of cybersecurity analytics. We
conduct our experiments using the Qiskit library with the Aer quantum
simulator, and on three different real quantum devices from Azure Quantum:
IonQ, Rigetti, and Quantinuum. This is the first time these tools are combined
in this manner.
