Generation and Decoding of Random Sparse Stabilizer Codes

Over the last few years there has been increasing interest in the design of sparse quantum error correcting codes, inspired by the great success of their classical counterpart, the low density parity check codes (LDPCs). However, two difficulties arise in this pursuit: it has proven difficult to extend the CSS formalism to the creation of quantum LDPC codes with non-vanishing rates; moreover, efficiently decoding quantum LDPC codes is unsolved problem as the iterative belief propagation decoders used for classical codes do not converge when applied to quantum codes. We explore machine learning techniques for decoding as well as generating efficient codes from this class.