  Quantum computing combined with machine learning (ML) is an extremely
promising research area, with numerous studies demonstrating that quantum
machine learning (QML) is expected to solve scientific problems more
effectively than classical ML. In this work, we successfully apply QML to drug
discovery, showing that QML can significantly improve model performance and
achieve faster convergence compared to classical ML. Moreover, we demonstrate
that the model accuracy of the QML improves as the number of qubits increases.
We also introduce noise to the QML model and find that it has little effect on
our experimental conclusions, illustrating the high robustness of the QML
model. This work highlights the potential application of quantum computing to
yield significant benefits for scientific advancement as the qubit quantity
increase and quality improvement in the future.
