Learning to Learn Quantum Turbo Detection

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

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Learning to Learn Quantum Turbo Detection

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arxiv 2205.08611 v1 pith:LGZC44KM submitted 2022-05-17 eess.SP cs.ETcs.LG

Learning to Learn Quantum Turbo Detection

Bryan Liu , Toshiaki Koike-Akino , Ye Wang , Kieran Parsons This is my paper

classification eess.SP cs.ETcs.LG

keywords turboquantumalgorithmdecoderlearnlearningperformanceachieve

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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.

Learning to Learn Quantum Turbo Detection

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