ExpandQISE: Track 1: Quantum Algorithms for Relativistic Quantum Chemistry

Quantum computers offer the opportunity to solve some of the long-standing challenges in science and engineering. A major question today is where and how quantum computers will show the first useful advantages over classical computers. While enormous progress has been made in improving quantum hardware, current quantum devices are not yet able to handle more than relatively small-sized problems. This research aims to develop new algorithms and schemes to extend the capabilities of quantum devices and apply them to some of the challenging problems in heavy-element computational chemistry. These developments could help advance our understanding of the chemistry of heavy-elements and help identify new materials and molecules for applications such as drug discovery, improvements in energy technology, and more. The project has a strong emphasis on quantum information science and engineering (QISE) workforce development in the Red River Valley region by introducing accessible and application-focused coursework to University of North Dakota (UND) students and other regional institutes, along with fostering partnerships with industry, national labs, and academia to facilitate the effective training of students.

With the promising developments in quantum computing hardware and algorithms, challenging scientific and engineering problems are being explored to take advantage of quantum computers. This project aims to develop quantum algorithmic strategies and implementations to pave the way towards achieving quantum utility/advantage in relativistic quantum chemistry. Accurate computational modeling of molecules containing lanthanides and actinides must incorporate challenging strong electron correlation effects along with relativistic effects to obtain accurate ground and excited state properties. Although such systems are useful in several biological processes and energy research, accurate computational predictions often lie beyond the reach of classical computers. Quantum computers can potentially surpass the limitations of classical computers in treating strong electron correlation, but current quantum computers are noisy and have a low number of high-quality qubits limiting their application to small test molecules. To extend the capabilities of current and near-term quantum devices and to tackle challenging problems in relativistic quantum chemistry, this project develops new error mitigation algorithms and a combined optimization-based and optimization-free adaptive strategy to achieve improved accuracy in ground and excited state calculations, along with implementations of these methods for challenging molecules containing heavy-elements. This research develops a path for one of the initial demonstrations of quantum advantage while potentially making an advance in heavy-element computational chemistry. Closely aligned with the research plan, the workforce development plan centers around the development of two strategic courses for QISE education along with a collaborative effort with academia, national labs, and industry for effective training of students. These courses are based on more accessible pictorial tools and an application-focused approach to teach QISE concepts and bring together a wide range of majors at UND and students in regional educational institutes with the aim to develop leading QISE workforce and support science and engineering education in the Red River Valley region.