Optimizing hypergraph product codes with random walks, simulated annealing and reinforcement learning

Understand

Hypergraph products are quantum low-density parity-check (LDPC) codes constructed from two classical LDPC codes.

• Although their dimension and distance depend only on the parameters of the underlying classical codes, optimizing their performance against various noise channels remains challenging.
• This difficulty partly stems from the complexity of decoding in the quantum setting.
• The standard, ad hoc approach typically involves selecting classical LDPC codes with large girth.