TR2019-071
Channel Decoding with Quantum Approximate Optimization Algorithm
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- "Channel Decoding with Quantum Approximate Optimization Algorithm", IEEE International Symposium on Information Theory (ISIT), DOI: 10.1109/ISIT.2019.8849710, July 2019.BibTeX TR2019-071 PDF Presentation
- @inproceedings{Matsumine2019jul,
- author = {Matsumine, Toshiki and Koike-Akino, Toshiaki and Wang, Ye},
- title = {Channel Decoding with Quantum Approximate Optimization Algorithm},
- booktitle = {IEEE International Symposium on Information Theory (ISIT)},
- year = 2019,
- month = jul,
- doi = {10.1109/ISIT.2019.8849710},
- issn = {2157-8117},
- isbn = {978-1-5386-9291-2},
- url = {https://www.merl.com/publications/TR2019-071}
- }
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- "Channel Decoding with Quantum Approximate Optimization Algorithm", IEEE International Symposium on Information Theory (ISIT), DOI: 10.1109/ISIT.2019.8849710, July 2019.
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Research Areas:
Abstract:
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.