QOMIC：用于模体识别的量子优化
QOMIC: Quantum optimization for motif identification
AI总结：
针对经典计算机求解非重叠网络模体识别计算难度高的问题，提出首个量子优化方案QOMIC，通过整数模型转化问题并构建量子电路，实验显示其模体计数优于经典方案，可高效识别五种神经退行性疾病相关人类调控网络中的模体。
摘要：
网络模体识别问题旨在生物网络中发现拓扑模式。使用经典计算机识别非重叠模体是一项具有计算挑战性的问题。量子计算机能够求解经典计算机无法扩展求解的高复杂度问题。本文提出了首个用于模体识别问题的量子解决方案，名为QOMIC（Quantum Optimization for Motif IdentifiCation，用于模体识别的量子优化）。QOMIC通过整数模型对模体识别问题进行转化，该模型是开发我们量子解决方案的基础。我们基于该模型开发并实现了量子电路，用于在给定网络中查找模体位置。我们的实验表明，在模体计数方面，QOMIC的性能优于现有的经典计算机解决方案。我们还观察到，QOMIC能够高效地在与五种神经退行性疾病相关的人类调控网络中找到模体，这五种疾病分别是阿尔茨海默病、帕金森病、亨廷顿病、肌萎缩侧索硬化症（ALS）以及运动神经元病（MND）。
英文摘要：
Network motif identification problem aims to find topological patterns in biological networks. Identifying non-overlapping motifs is a computationally challenging problem using classical computers. Quantum computers enable solving high complexity problems which do not scale using classical computers. In this paper, we develop the first quantum solution, called QOMIC (Quantum Optimization for Motif IdentifiCation), to the motif identification problem. QOMIC transforms the motif identification problem using a integer model, which serves as the foundation to develop our quantum solution. We develop and implement the quantum circuit to find motif locations in the given network using this model. Our experiments demonstrate that QOMIC outperforms the existing solutions developed for the classical computer, in term of motif counts. We also observe that QOMIC can efficiently find motifs in human regulatory networks associated with five neurodegenerative diseases: Alzheimers, Parkinsons, Huntingtons, Amyotrophic Lateral Sclerosis (ALS), and Motor Neurone Disease (MND).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
