Department of Computer Science, Federal University of Lavras, Lavras, MG, Brazil.
Center for Engineering, Modelling and Applied Sciences, CECS, Federal University of ABC, São Paulo, Brazil.
Sci Rep. 2024 Nov 1;14(1):26333. doi: 10.1038/s41598-024-71826-0.
In the realm of intelligent healthcare, there is a growing ambition to reshape medical services through the integration of artificial intelligence (AI). However, conventional machine learning faces inherent challenges such as privacy issues, delayed updates, and protracted training times, particularly due to the hesitance of medical institutions to directly share sensitive data, with possible noises. In response to these concerns, a Quantum-Assisted Federated Intelligent Diagnosis Algorithm ( -QuAFIDA) is proposed, applied into real medical data. Leveraging the capabilities of the 5G mobile network, this approach works the connection between Internet of Medical Things (IoMT) devices through the 5G, synchronizing training and updating the server model without disrupting their real-world applications. In our quest to safeguard patient data and enhance training efficiency, our study employs an innovative heuristic approach marked by a nested loop structure. Specifically, the inner loop is dedicated to training the beta-variational quantum eigensolver ( -VQE) to approximate the expectation values of the proposed algorithm; the outer loop trains the -QuAFIDA to reduce the relative entropy towards the target. This approach involves a balance between privacy considerations and the urgency of training. Results demonstrate that representations with low-rank attained through -QuAFIDA offer an effective approach for acquiring low-rank states. This research signifies a step forward in the synergy between AI and 5G technologies, presenting a novel avenue for the advancement of intelligent healthcare.
在智能医疗领域，通过人工智能（AI）的融合来重塑医疗服务的愿望日益强烈。然而，传统的机器学习面临着固有挑战，如隐私问题、更新延迟和训练时间长，特别是由于医疗机构不愿直接共享敏感数据，可能存在噪声。针对这些问题，提出了一种基于量子辅助的联邦智能诊断算法（ -QuAFIDA），并将其应用于真实的医疗数据中。该方法利用 5G 移动网络的功能，通过 5G 连接物联网（IoMT）设备，在不干扰其实际应用的情况下，同步训练和更新服务器模型。在保护患者数据和提高训练效率的过程中，我们的研究采用了一种创新的启发式方法，其特点是嵌套循环结构。具体来说，内循环用于训练β变分量子本征求解器（ -VQE）来逼近所提出算法的期望值；外循环则用于训练 -QuAFIDA 以降低相对熵以达到目标。这种方法涉及到隐私考虑和训练紧迫性之间的平衡。研究结果表明，通过 -QuAFIDA 获得的低秩表示为获取低秩状态提供了一种有效的方法。这项研究标志着人工智能和 5G 技术之间协同作用的一个进步，为智能医疗的发展提供了一个新的途径。
