Unitary matrix models are mapped to vacuum correlators of quantized symmetric functions in the N-magnon sector of a spin chain via Schur orthogonality.
Classical and quantum algorithms for characters of the symmetric group
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abstract
Characters of irreducible representations are ubiquitous in group theory. However, computing characters of some groups such as the symmetric group $S_n$ is a challenging problem known to be $\#P$-hard in the worst case. Here we describe a Matrix Product State (MPS) algorithm for characters of $S_n$. The algorithm computes an MPS encoding all irreducible characters of a given permutation. It relies on a mapping from characters of $S_n$ to quantum spin chains proposed by Crichigno and Prakash. We also provide a simpler derivation of this mapping. We complement this result by presenting a $poly(n)$ size quantum circuit that prepares the corresponding MPS, obtaining an efficient quantum algorithm for certain sampling problems based on characters of $S_n$. To assess classical hardness of these problems we present a general reduction from strong simulation (computing a given probability) to weak simulation (sampling with a small error). This reduction applies to any sampling problem with a certain granularity structure and may be of independent interest.
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hep-th 1years
2026 1verdicts
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Unitary matrix models, quantized symmetric functions and spin chain
Unitary matrix models are mapped to vacuum correlators of quantized symmetric functions in the N-magnon sector of a spin chain via Schur orthogonality.