Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Existing VQC or QNN-based methods require significant computational resources to perform the gradient-based optimization of a larger number of quantum circuit parameters. The major drawback is that such quantum gradient calculation requires a large amount of circuit evaluation, posing challenges in current near-term quantum hardware and simulation software. In this work, we approach sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) that are based on classical RNN, LSTM and GRU. The main idea to this RC approach is that the QRNN with randomly initialized weights is treated as a dynamical system and only the final classical linear layer is trained. Our numerical simulations show that the QRNN-RC can reach results comparable to fully trained QRNN models for several function approximation and time series prediction tasks. Since the QRNN training complexity is significantly reduced, the proposed model trains notably faster. In this work we also compare to corresponding classical RNN-based RC implementations and show that the quantum version learns faster by requiring fewer training epochs in most cases. Our results demonstrate a new possibility to utilize quantum neural network for sequential modeling with greater quantum hardware efficiency, an important design consideration for noisy intermediate-scale quantum (NISQ) computers.

通用数据模型解决了标准化电子健康记录（EHR）数据的许多挑战，但无法将其集成深度表型所需的资源。开放的生物学和生物医学本体论（OBO）铸造本体论提供了可用于生物学知识的语义计算表示，并能够整合多种生物医学数据。但是，将EHR数据映射到OBO Foundry本体论需要大量的手动策展和域专业知识。我们介绍了一个框架，用于将观察性医学成果合作伙伴关系（OMOP）标准词汇介绍给OBO铸造本体。使用此框架，我们制作了92,367条条件，8,615种药物成分和10,673个测量结果的映射。域专家验证了映射准确性，并且在24家医院进行检查时，映射覆盖了99％的条件和药物成分和68％的测量结果。最后，我们证明OMOP2OBO映射可以帮助系统地识别可能受益于基因检测的未诊断罕见病患者。

子痫前期是孕产妇和胎儿发病率和死亡率的主要原因。目前，先兆子痫的唯一明确治疗方法是胎盘的递送，这对于疾病的发病机理至关重要。已经广泛地进行了鉴定出差异表达的基因（DEGS），已经进行了广泛的先兆子痫对人胎盘的转录分析。使用无偏见的测定法确定了DEG，但是，在实验上研究DEG的决策受到许多因素的偏见，导致许多DEGS仍未被评估。一组与疾病在实验上相关的DEG，但与文献中的疾病尚无相关性，被称为无知组。先兆子痫具有广泛的科学文献，大量的DEG数据库，只有一种确定的治疗方法。促进基于知识的分析的工具能够将许多来源的不同数据结合起来，以提出基本的行动机制，可能是支持发现并提高我们对这种疾病的理解的宝贵资源。在这项工作中，我们证明了如何使用生物医学知识图（KG）来识别新型的先兆子痫分子机制。现有的开源生物医学资源和公开可用的高通量转录分析数据用于识别和注释当前未经资助的先兆子痫相关的DEG的功能。使用文本挖掘方法从PubMed摘要中鉴定出与先兆子痫相关的基因。文本媒介和荟萃分析衍生的列表的相对补体被确定为未经投票的前启示性脱位相关的DEG（n = 445），即先前的无知组。使用KG研究相关的DEG，揭示了53种新型临床相关和生物学作用的机械关联。