Learning to Learn Quantum Turbo Detection

Bryan Liu; Toshiaki Koike-Akino; Ye Wang; Kieran Parsons

Learning to Learn Quantum Turbo Detection

Signal Processing 2022-05-19 v1 Emerging Technologies Machine Learning

Authors: Bryan Liu , Toshiaki Koike-Akino , Ye Wang , Kieran Parsons

Abstract

This paper investigates a turbo receiver employing a variational quantum circuit (VQC). The VQC is configured with an ansatz of the quantum approximate optimization algorithm (QAOA). We propose a 'learning to learn' (L2L) framework to optimize the turbo VQC decoder such that high fidelity soft-decision output is generated. Besides demonstrating the proposed algorithm's computational complexity, we show that the L2L VQC turbo decoder can achieve an excellent performance close to the optimal maximum-likelihood performance in a multiple-input multiple-output system.

Keywords

automatic modulation classification machine learning wireless communication

Cite

@article{arxiv.2205.08611,
  title  = {Learning to Learn Quantum Turbo Detection},
  author = {Bryan Liu and Toshiaki Koike-Akino and Ye Wang and Kieran Parsons},
  journal= {arXiv preprint arXiv:2205.08611},
  year   = {2022}
}

Comments

6 pages, 3 figures, IEEE GLOBECOM 2022

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