New approach cuts samples needed for quantum error correction

Researchers have developed a method for learning error rates in fault-tolerant quantum computations that requires a number of samples scaling polynomially with code distance, an advantage their method offers over existing approaches. The work centers on extracting information from "syndrome" data generated when measuring Pauli operators within stabilizer codes, a technique used in recent experiments demonstrating error-corrected quantum memory and computations. This new framework provides a means for in situ characterization of both physical and logical noise, utilizing natively available data to benchmark and verify quantum circuits and algorithms. The team's approach addresses the limitations of idealized noise models, moving toward more accurate error modeling for optimization of fault-tolerant computations.…