Learning t-doped stabilizer states

Abstract

In this paper, we present a learning algorithm aimed at learning states obtained from computational basis states by Clifford circuits doped with a finite number t of non-Clifford gates. To tackle this problem, we introduce a novel algebraic framework for t-doped stabilizer states by utilizing tools from stabilizer entropy. Leveraging this new structure, we develop an algorithm that uses sampling from the distribution obtained by squaring expectation values of Pauli operators that can be obtained by Bell sampling on the state and its conjugate in the computational basis. The algorithm requires resources of complexity O(\exp(t)\poly(n)) and exhibits an exponentially small probability of failure.Comment: L.L. and S.O. contributed equally to this wor