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Paper Detail

Paper Title Channel Decoding with Quantum Approximate Optimization Algorithm
Paper IdentifierFR1.R6.4
Authors Toshiki Matsumine, Yokohama National University, Japan; Toshiaki Koike-Akino, Ye Wang, Mitsubishi Electric Research Laboratories (MERL), United States
Session Classical Meets Quantum
Location Sorbonne, Level 5
Session Time Friday, 12 July, 09:50 - 11:10
Presentation Time Friday, 12 July, 10:50 - 11:10
Manuscript  Click here to download the manuscript
Abstract Motivated by the recent advancement of quantum processors, we investigate quantum approximate optimization algorithm (QAOA) to employ quasi-maximum-likelihood (ML) decoding of classical channel codes. QAOA is a hybrid quantum-classical 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.