By coupling academic rigor with industrial scale, the lab aims to define the computational foundations that will power the next generation of AI, algorithms, and quantum computing. The lab is designed to accelerate progress toward powerful new computational approaches that take advantage of rapid advances in AI and quantum-centric supercomputing, including those that combine maturing quantum hardware with classical systems and AI methods.

To do this, the lab advances core capabilities in AI and classical algorithms, with an emphasis on integrating AI seamlessly into computational systems. This includes research into efficient, modular, language model architectures and new programming approaches that enable their deployment at scale. The lab also develops specialized retrieval-augmented and agentic systems tailored to enterprise environments and integration into complex workflows. Across this work, the lab is equally focused on strengthening governance, transparency, and open ecosystems to ensure that AI systems are robust and trustworthy.

In parallel, the lab explores computational frontiers through quantum computing and its convergence with AI. Lab researchers are developing novel quantum algorithms and investigating the mathematical and algorithmic foundations of machine learning, optimization, Hamiltonian simulations, and partial differential equations to address challenging mathematical problems, including those arising in complex, dynamic systems with applications across the physical and life sciences and global industries. The lab also investigates the co-design of AI and quantum architectures, and creates advanced protocols for fault-tolerant quantum computing. Together, this work establishes the underpinning for scalable, high-performance computing systems that extend beyond the limits of current approaches.