NSF Award
2120949
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.<br/><br/>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 hardware<br/>available 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.<br/><br/>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.
