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Introduces iterative tensor network transformations (ITNTs) for efficient element-wise evaluation of nonlinear functions on tensor trains, enabling data processing and optimization on large-scale datasets with applications in reactive flow fields and Max-SAT problems.
This paper presents a case study of human-AI co-discovery in mathematics, where AI assisted in expanding an intuition about sign-embedding quantum algorithms into a formal framework and proofs, with human judgment guiding route selection.