癌症是全球主要死因之一。虽然常规癌症诊断主要通过活检取样进行，但准确表征肿瘤异质性仍然存在不足。正电子发射断层扫描（PET）驱动的放射组学研究在预测临床终点方面表现出有希望的结果。本研究旨在研究量子机器学习在模拟器和现有的量子计算机中的附加价值，利用误差缓解技术来预测不同PET癌症患者的临床终点。本研究利用先前发表的PET放射组学数据集，包括11C-MET PET脑胶质瘤、68GA-PSMA-11 PET前列腺和肺18F-FDG PET的3年生存、低风险与高风险格里森分数以及2年生存等临床终点。对所有队列进行0.7、0.8和0.9鲁金曼等级阈值（SRT）的冗余削减，然后从所有队列中选择8个和16个特征，进行了18个数据集变体。通过每个数据集变体的几何差异（GDQ）得分评估量子优势。在各个数据集变体中，使用了五个经典机器学习（CML）及其量子版本（QML），在模拟器环境中进行训练和测试。进行了量子电路优化和误差缓解，并在21量子比特的IonQ Aria量子计算机上训练和测试选定的QML方法。预测性能通过测试平衡准确率（BACC）值进行评估。平均而言，在16个特征的模拟器环境中，QML的表现优于CML（BACC分别为70%和69%），而在8个特征时，CML的表现优于QML（+1%）。最高平均QML优势为+4%。在所有8个特征情况下，GDQ得分均≤1.0，而在9个中有11个情况下，当QML优于CML时，GDQ得分>1.0。在无误差缓解（EM）的情况下，IonQ设备中选定的QML方法和数据集的测试BACC为69.94%，而EM将测试BACC提高到75.66%（在无噪声的模拟器中为76.77%）。我们证明，在这些与临床相关的PET癌症队列中，通过误差缓解，可以在真实存在的量子计算机上实现量子优势，用于预测临床终点。在这些队列中，在依赖QML时，模拟器环境中已经可以实现量子优势。© 2023年。作者。

Cancer is a leading cause of death worldwide. While routine diagnosis of cancer is performed mainly with biopsy sampling, it is suboptimal to accurately characterize tumor heterogeneity. Positron emission tomography (PET)-driven radiomic research has demonstrated promising results when predicting clinical endpoints. This study aimed to investigate the added value of quantum machine learning both in simulator and in real quantum computers utilizing error mitigation techniques to predict clinical endpoints in various PET cancer patients.Previously published PET radiomics datasets including 11C-MET PET glioma, 68GA-PSMA-11 PET prostate and lung 18F-FDG PET with 3-year survival, low-vs-high Gleason risk and 2-year survival as clinical endpoints respectively were utilized in this study. Redundancy reduction with 0.7, 0.8, and 0.9 Spearman rank thresholds (SRT), followed by selecting 8 and 16 features from all cohorts, was performed, resulting in 18 dataset variants. Quantum advantage was estimated by Geometric Difference (GDQ) score in each dataset variant. Five classic machine learning (CML) and their quantum versions (QML) were trained and tested in simulator environments across the dataset variants. Quantum circuit optimization and error mitigation were performed, followed by training and testing selected QML methods on the 21-qubit IonQ Aria quantum computer. Predictive performances were estimated by test balanced accuracy (BACC) values.On average, QML outperformed CML in simulator environments with 16-features (BACC 70% and 69%, respectively), while with 8-features, CML outperformed QML with + 1%. The highest average QML advantage was + 4%. The GDQ scores were ≤ 1.0 in all the 8-feature cases, while they were > 1.0 when QML outperformed CML in 9 out of 11 cases. The test BACC of selected QML methods and datasets in the IonQ device without error mitigation (EM) were 69.94% BACC, while EM increased test BACC to 75.66% (76.77% in noiseless simulators).We demonstrated that with error mitigation, quantum advantage can be achieved in real existing quantum computers when predicting clinical endpoints in clinically relevant PET cancer cohorts. Quantum advantage can already be achieved in simulator environments in these cohorts when relying on QML.© 2023. The Author(s).