CAREER: On the Foundations of End-to-End Quantum Applications

University Of Maryland, College Park, College Park MD

Investigators

Abstract

This project sets a comprehensive research agenda focusing on the foundation of the design and implementation of end-to-end quantum applications, as an effort to bridge the gap between the theoretical foundation of quantum computing and the limitation of realistic quantum machines. In particular, it aims to make fundamental contributions to research in quantum algorithms, quantum machine learning, and quantum software foundation. This project will help facilitate end-to-end quantum applications in all related fields, such as physics, chemistry, and material science, and hence contribute to the establishment of major milestones of quantum computing. The results obtained will be disseminated through a variety of venues, including conferences, new course materials, expository writings, and high school open days aimed at exposing young computer scientists to the frontiers of quantum information research. The PI will integrate methods from the fields of theoretical computer science, (theoretical) machine learning, formal methods and programming languages to address the unique research challenges in achieving end-to-end quantum applications. In particular, this project includes (1) designing quantum algorithms for sub-modular optimization and log-concave sampling, (2) developing new functionality for variational quantum methods (or the so-called quantum neural networks) and their training methods, (3) leveraging techniques from formal methods and programming languages to certify the correctness of the implementation of quantum applications as well as to obtain concrete (rather than asymptotic) resource/error analysis. This research agenda will enable a more complete assessment of end-to-end quantum applications and their near-term feasibility. 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.