Channel Decoding with Quantum Approximate Optimization Algorithm


Motivated by the recent advancement of quantum processors, we investigate quantum approximate optimizationalgorithm (QAOA) to employ quasi-maximum-likelihood (ML) decoding of classical channel codes. QAOA is a hybrid quantumclassical variational algorithm, which is advantageous for the near-term noisy intermediate-scale quantum (NISQ) devices, where the fidelity of quantum gates is limited by noise and decoherence. We first describe how to construct Ising Hamiltonian model to realize quasi-ML decoding with QAOA. For level-1 QAOA, we derive the systematic way to generate theoretical expressions of cost expectation for arbitrary binary linear codes. Focusing on [7, 4] Hamming code as an example, we analyze the impact of the degree distribution in associated generator matrix on the quantum decoding performance. The excellent performance of higher-level QAOA decoding is verified when Pauli rotation angles are optimized through meta-heuristic variational quantum eigensolver (VQE). Furthermore, we demonstrate the QAOA decoding performance in a real quantum device.


  • Related Publication

  •  Matsumine, T., Koike-Akino, T., Wang, Y., "Channel Decoding with Quantum Approximate Optimization Algorithm", arXiv, March 2019.
    BibTeX arXiv
    • @article{Matsumine2019mar3,
    • author = {Matsumine, Toshiki and Koike-Akino, Toshiaki and Wang, Ye},
    • title = {Channel Decoding with Quantum Approximate Optimization Algorithm},
    • journal = {arXiv},
    • year = 2019,
    • month = mar,
    • url = {}
    • }