MCA: Application of Quantum Computing in Bioinformatics and Computational Biology

该职业中期进步奖支持计算和理论研究，通过教育和实践实验，用量子计算方法推进生物计算。考虑到技术发展的总体速度和我们正在经历的所谓的第二次量子革命的速度，发展量子劳动力的需求正成为学术界和工业界的一个严肃问题。从学术角度来看，构建量子计算应用程序比构建现代经典计算机更具挑战性。在量子器件上进行计算时，必须将研究问题转化为量子形式，这是一项需要物理学家和非物理学领域专家密切合作的任务。量子计算机使用不同的概念和数学工具执行一种完全不同类型的计算。其结果是，将生物信息学移植到量子计算中并不像用不同的编程语言编写计算机代码那么简单。因此，弥合量子物理学和生物学之间的差距需要学科之间的密切合作才能取得进展。这个中期职业发展奖回应了生物学家与他们的物理学同事在中间相遇以改变生物信息学的需求。该奖项支持PI学习如何将生物学问题的经典统计分析与量子信息理论联系起来。这项任务虽然具有挑战性，但也是合理的，因为线性代数的语言在这两个领域都是通用的。PI将开发三个子项目，逐步建立实用的量子计算驱动的生物信息学。首先，对正交性和不相关如何暗示不同统计特性的含义的分析工作将在量子计算框架中应用。这项工作很有趣，因为一些量子机器学习算法依赖于内积来对数据进行分类，并且内积对于正交性有很好的定义，但对于不相关的数据并不总是如此。其次，基因组数据的嵌入方案将被解析地定义用于量子计算。第三，所提出的嵌入方案的统计特性将在量子计算模拟器以及学术界可用的量子计算硬件上进行实证测试。该奖项满足了NSF劳动力发展的使命，同时也通过扩展生物学中可用的计算工具，促进了对生物信息学领域的更广泛影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Mid-Career Advancement award supports both computational and theoretical research, through education and practical experiments, to advance biological computing with quantum computing methods. Given the rapid pace of technological development generally and the speed of the so-called second quantum revolution we are now experiencing, the need to develop a quantum workforce is becoming a serious matter for academics and industry. From the academic side, building applications for quantum computing is much more challenging than is true for modern classical computers. Research questions must be recast into the quantum formalism for calculation on quantum devices, which is a task that requires close collaboration between physicists and non-physics domain experts. A quantum computer performs a fundamentally different type of calculation using different concepts and mathematical tools. The result is that porting bioinformatics for quantum computing is not as simple as writing computer code in a different programming language. As such, bridging the gap between quantum physics and biology requires close collaboration between disciplines to make progress. This Mid-Career Advancement award responds to the need for biologists to meet in the middle with their physics colleagues to transform bioinformatics.This award supports the PI in learning how to bridge classical statistical analysis of biology problems to quantum information theory. This task, while challenging, is reasonable since the language of linear algebra is common to both fields. The PI will develop three sub-projects that progressively build to toward practical quantum computing driven bioinformatics. First, analytical work on the implications of how orthogonality and uncorrelated imply different statistical properties will be applied in the quantum computing framework. This work is of interest since some quantum machine learning algorithms relay on inner products to classify data, and inner products are well defined for orthogonality but not always so for uncorrelated data. Second, embedding schemes for genomic data will be analytically defined for quantum computing. Third, the statistical properties of the proposed embedding schemes will be empirically tested on quantum computing simulators as well as quantum computing hardwareavailable to academics. This award meets the NSF’s mission of workforce development while also facilitating a broader impact on the field of bioinformatics by extending the computational tools available to biology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.