MCA: Application of Quantum Computing in Bioinformatics and Computational Biology

这一职业生涯中期推进奖通过教育和实际实验支持计算和理论研究，以促进使用量子计算方法的生物计算。鉴于技术发展的总体速度和我们正在经历的所谓第二次量子革命的速度，发展一支量子劳动力的必要性正成为学术界和工业界的一件严肃的事情。从学术方面来看，为量子计算构建应用程序比为现代经典计算机构建应用程序更具挑战性。研究问题必须重塑为量子形式，以便在量子设备上进行计算，这是一项需要物理学家和非物理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，弥合量子物理和生物学之间的差距需要各学科之间的密切合作才能取得进展。这一职业生涯中期推进奖是为了回应生物学家与他们的物理同事在中间会面以转变生物信息学的需要。这一奖项支持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对这两个领域都是通用的。PI将开发三个子项目，逐步构建实用的量子计算驱动的生物信息学。首先，关于正交性和不相关性如何意味着不同的统计特性的分析工作将在量子计算框架中应用。这项工作很有意义，因为一些量子机器学习算法依靠内积对数据进行分类，内积对于正交性定义得很好，但对于不相关的数据并不总是这样。其次，将为量子计算解析定义基因组数据的嵌入方案。第三，将在量子计算模拟器以及学者可用的量子计算硬件上对所提出的嵌入方案的统计特性进行经验测试。这一奖项符合NSF的劳动力发展使命，同时通过扩展生物可用的计算工具，促进在生物信息学领域产生更广泛的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.