On the distinguishability of geometrically uniform quantum states

• URL: http://arxiv.org/abs/2501.12376v1
• Date: Tue, 21 Jan 2025 18:53:53 GMT
• Title: On the distinguishability of geometrically uniform quantum states
• Authors: Juntai Zhou, Stefano Chessa, Eric Chitambar, Felix Leditzky,
• Abstract summary: A geometrically uniform (GU) ensemble is a uniformly weighted quantum state ensemble generated from a fixed state.<n>In this work we analyze the problem of discriminating GU ensembles from various angles.
• Score: 8.264068966517918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A geometrically uniform (GU) ensemble is a uniformly weighted quantum state ensemble generated from a fixed state by a unitary representation of a finite group $G$. In this work we analyze the problem of discriminating GU ensembles from various angles. Assuming that the representation of $G$ is irreducible, we first give explicit expressions for the optimal success probability of discriminating the GU states, and optimal measurements achieving this value, in terms of the largest eigenvalue of the generator state and its associated eigenspace. A particular optimal measurement can be understood as the limit of weighted "pretty good measurements" (PGM). This naturally provides examples of state discrimination for which the unweighted PGM is provably sub-optimal. We extend this analysis to certain reducible representations, and use Schur-Weyl duality to discuss two particular examples of GU ensembles in terms of Werner-type and permutation-invariant generator states. For the case of pure-state GU ensembles we give a new streamlined proof of optimality of the PGM first proved in [Eldar et al., 2004]. We use this result to give a simplified proof of the optimality of the PGM for the hidden subgroup problem over semidirect product groups, along with an expression for the corresponding success probability, proved in [Bacon et al., 2005]. Finally, we consider the discrimination of generic mixed-state GU-ensembles in the $n$-copy setting and adapt a result of [Montanaro, 2007] to derive a compact and easily evaluated lower bound on the success probability of the PGM for this task. This result can be applied to the hidden subgroup problem to obtain a new proof for an upper bound on the sample complexity by [Hayashi et al., 2006].

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