Training neural belief-propagation decoders for quantum error-correcting codes

Two researchers at Université de Sherbrooke, in Canada, have recently developed and trained neural belief-propagation (BP) decoders for quantum low-density parity-check (LDPC) codes. Their study, outlined in a paper published in Physical Review Letters, suggests that training can enhance the performance of BP decoders significantly, helping to solve issues that are commonly associated with their application in quantum research. "Ten years ago, I wrote an article with Yeojin Chung explaining how standard decoding algorithms for LDPC codes, which are broadly used in classical communication, would fail in the quantum setting," David Poulin, one of the researchers who carried out the study, told Phys.org. "This problem has been obsessing me ever since. Recently, people have started to investigate the use of neural networks to decode quantum codes, but they all focused on a problem (decoding topological codes) that already had a number of good human-designed solutions. This was the perfect occasion to revisit my favorite open problem and use neural networks to decode quantum codes that had no previously known decoder."