[论文解读] The state of quantum computing applications in health and medicine

Frederik F. Flöther|arXiv (Cornell University)|Jan 22, 2023

Genetics, Bioinformatics, and Biomedical Research参考文献 74被引用 22

一句话总结

这是一个将临床与医学概念验证量子计算应用映射的评审，覆盖基因组学、临床研究、诊断和治疗，强调量子机器学习和短期实现。

ABSTRACT

Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although biology and quantum theory have arguably been entangled ever since Schrödinger's cat). The initial focus was on biochemical and computational biology problems; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The use cases and algorithms are summarized and an outlook on medicine in the quantum era, including technical and ethical challenges, is provided.

研究动机与目标

• 映射临床与医学量子计算应用的全景。
• 总结健康与医学领域的实验和理论研究（超过40项）。
• 识别医学中的QML和量子计算的用例领域及典型算法。
• 讨论技术与伦理挑战并展望医学中的量子时代。

提出的方法

• 对临床和医学量子计算研究及概念验证实验进行评审与综合。
• 对用例领域进行分类（基因组学、临床研究与发现、诊断、治疗）。
• 评估量子机器学习在相对于经典基准的性能。
• 讨论以临床和真实世界数据为训练基础的短期QML方法。

实验结果

研究问题

• RQ1 健康与医学中量子计算的当前用例领域是什么？
• RQ2 在近期研究中，量子机器学习在医疗数据上相对于经典方法的表现如何？
• RQ3 最常用于短期量子应用的数据类型和临床情境是什么？
• RQ4 医学领域中量子计算面临的关键技术与伦理挑战是什么？
• RQ5 量子解决方案在临床实践与研究中的采用前景如何？

主要发现

• 有超过40项实验和理论研究映射了健康与医学中的量子计算应用。
• 用例领域包括基因组学、临床研究与发现、诊断，以及治疗与干预。
• 量子机器学习发展迅速，在最近的医学研究中已与经典基准展现出竞争力。
• 短期QML算法已在多样化的临床与真实世界数据集上进行了训练。
• 应用包括药物候选物生成、医疗影像分类、患者依从性预测、治疗预测以及放射治疗定制。