The project comprises an integrated team of quantum algorithm developers, mathematicians, and computer scientists who will design and deliver novel algorithms, compiling techniques, scheduling tools, and linear algebra approaches for chemical sciences. The aim is to break new ground in modeling dynamical processes in chemistry and to advance machine learning on near-term quantum computing platforms.
The dynamics and machine learning algorithms developed by the team will enable researchers in chemical sciences and other engineering domains to exploit quantum architectures. These new algorithms will also provide a solid foundation for algorithmic development and exploitation of quantum computers in other research and engineering domains. To ensure effective codesign of quantum devices and algorithms, the QAT project will closely collaborate with DOE quantum testbed developers and industrial hardware developers,
Argonne’s effort will focus on developing stochastic optimization algorithms and tools critical to advancing the performance of emerging quantum computing systems.
Our advances in compiler and quantum circuit optimization will be integrated in open-source software frameworks.