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Modern high-performance computing is being reshaped by novel hardware architectures, particularly those developed for AI, offering unprecedented performance. Effectively using these systems for scientific workloads, however, often requires redesigning the underlying physics, algorithms, and data structures, as well as adapting existing methods to better align with these architectures. In this talk, we present our efforts to apply emerging AI hardware to non-AI workloads, specifically quantum chemistry and electronic structure calculations, and showcase our significant performance gains.