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.

翻译：量子计算与机器学习（ML）的结合是一个极具前景的研究领域，大量研究表明，量子机器学习（QML）有望比经典机器学习更有效地解决科学问题。在本工作中，我们成功将QML应用于药物发现，结果表明，与经典ML相比，QML能显著提升模型性能并实现更快的收敛速度。此外，我们证明了QML的模型精度随着量子比特数量的增加而提高。我们还在QML模型中引入了噪声，发现其对我们的实验结论影响甚微，这说明了QML模型具有很高的鲁棒性。本工作凸显了随着未来量子比特数量增加与质量提升，量子计算在推动科学进步方面具有带来显著效益的潜在应用价值。