IQIM Postdoctoral and Graduate Student Seminar
Abstract: The typical model for measurement noise in quantum error correction is to randomly flip the binary measurement outcome. In experiments, measurements yield much more information - e.g., continuous current values, discrete photon counts which is then mapped into binary outcomes. In this talk I will present a method to include this information into the decoding of surface codes. I will review hard decoding of surface codes and then describe a soft syndrome decoding version of Minimum Weight Perfect Matching decoder. I will then use our soft syndrome decoders to show the benefits of tuning experimental parameters with quantum error correction in mind.