Quantum Approximate Optimization for Decoding of Low-Density Parity-Check Codes
The study proposes using a Quantum Approximate Optimization Algorithm (QAOA) to decode Low-Density Parity-Check (LDPC) codes. Instead of relying solely on classical Belief Propagation, QAOA formulates the decoding as an optimization problem that simultaneously satisfies parity constraints and incorporates channel reliability information. Experiments show that this QAOA approach consistently achieves a higher probability of correctly recovering the transmitted codeword compared to standard BP decoding across various noise levels.