Low-overhead tailoring and learning of noise in graph states
The work introduces a low-overhead method to tailor and learn noise in graph states by converting them into a diagonal form. This approach uses lower-level Clifford gates and Bell measurements, proving that polynomial time complexity is sufficient for bounded noise estimation error, overcoming the exponential complexity typical of existing methods.