Learning t-doped stabilizer states

HIGHLIGHTS

• What: The authors finally show that it is possible to extend learning algorithms to general states obtained by Clifford+T unitaries. The authors propose an innovative algebraic framework for t-doped stabilizer states by employing concepts from stabilizer entropy . In appendix B the authors provide a slightly more efficient algorithm relying instead on the notion of diagonalize where the number of computational steps to verify the containment of one bad generator is O(n2 k), and so reducing the computational steps to learn all the bad generators to O(n2 k 2 ). The authors focus on comparing . . .
  

   

  

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