The work proves a worst-case sample complexity of O(sqrt(4.5^n)) for distributed inner product estimation with local Clifford sampling on n-qubit states, with a conjectured O(sqrt(3.6^n)) for Haar sampling.
(C94) For each nonzeroa, the setA 0 ∪A a is affine isotropic andh a /∈L, so Eq
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Worst-Case Sample Complexity Bounds for Distributed Inner Product Estimation with Local Randomized Measurements
The work proves a worst-case sample complexity of O(sqrt(4.5^n)) for distributed inner product estimation with local Clifford sampling on n-qubit states, with a conjectured O(sqrt(3.6^n)) for Haar sampling.